what is data analysis methodology

Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

• It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
• As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
• It can be broken down into mathematical and AI analysis.
• Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
• Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
• Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

• It accounts for less than 30% of all data analysis and is common in social sciences .
• It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
• Because of this, some argue that it’s ultimately a quantitative type.
• Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
• Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
• Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

• Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
• Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
• Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
• Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

• Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
• Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
• Nature of Data: numeric.
• Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

• Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
• Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
• Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
• Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

• Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
• Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
• Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
• Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

• Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
• Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
• Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
• Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

• Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
• Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
• Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
• Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

• Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
• Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
• Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
• Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
• Here’s an example set:

Classification Method

• Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
• Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
• Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
• Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

• Description: the forecasting method uses time past series data to forecast the future.
• Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
• Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
• Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

• Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
• Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
• Nature of Data: the nature of optimizable data is a data set of at least two points.
• Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

• Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
• Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
• Nature of Data: data useful for content analysis is textual data.
• Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

• Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
• Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
• Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
• Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

• Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
• Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
• Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
• Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

• Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
• Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
• Nature of Data: the nature of data useful for framework analysis is textual.
• Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

• Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
• Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
• Nature of Data: the nature of data useful in the grounded theory method is textual.
• Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

• Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
• Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
• Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
• Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

• Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
• Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
• Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
• Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

• Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
• Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
• Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
• Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

• Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
• Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
• Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
• Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

• Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
• Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
• Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
• Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

• Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
• Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
• Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
• Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

• Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
• Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
• Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
• Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

• Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
• Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
• Nature of Data: the nature of data useful for moving averages is time series data .
• Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

• Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
• Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
• Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
• Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

• Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
• Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
• Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
• Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

• Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
• Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
• Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
• Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
• Video example :

Fuzzy Logic Technique

• Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
• Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
• Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
• Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

• Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
• Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
• Nature of Data: the nature of data useful in text analysis is words.
• Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

• Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
• Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
• Nature of Data: the nature of data useful for coding is long text documents.
• Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

• Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
• Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
• Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
• Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

• Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
• Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
• Nature of Data: the nature of data useful for word frequency is long, informative documents.
• Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

• Quantitative
• Qualitative
• Mathematical
• Machine Learning and AI
• Descriptive
• Prescriptive
• Classification
• Forecasting
• Optimization
• Grounded theory
• Artificial Neural Networks
• Decision Trees
• Evolutionary Programming
• Fuzzy Logic
• Text analysis
• Idea Pattern Analysis
• Word Frequency Analysis
• Nïave Bayes
• Exponential smoothing
• Moving average
• Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

• Content (qualitative)
• Narrative (qualitative)
• Discourse (qualitative)
• Framework (qualitative)
• Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

About the author.

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Table of Contents

What is data analysis, why is data analysis important, what is the data analysis process, data analysis methods, applications of data analysis, top data analysis techniques to analyze data, what is the importance of data analysis in research, future trends in data analysis, choose the right program, what is data analysis: a comprehensive guide.

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4. Risk Mitigation

Data analysis is a valuable tool for risk assessment and management. Organizations can assess potential issues and take preventive measures by analyzing historical data. For instance, data analysis detects fraudulent activities in the finance industry by identifying unusual transaction patterns. This not only helps minimize financial losses but also safeguards the reputation and trust of customers.

5. Efficient Resource Allocation

Data analysis helps organizations optimize resource allocation. Whether it's allocating budgets, human resources, or manufacturing capacities, data-driven insights can ensure that resources are utilized efficiently. For example, data analysis can help hospitals allocate staff and resources to the areas with the highest patient demand, ensuring that patient care remains efficient and effective.

6. Continuous Improvement

Data analysis is a catalyst for continuous improvement. It allows organizations to monitor performance metrics, track progress, and identify areas for enhancement. This iterative process of analyzing data, implementing changes, and analyzing again leads to ongoing refinement and excellence in processes and products.

The data analysis process is a structured sequence of steps that lead from raw data to actionable insights. Here are the answers to what is data analysis:

• Data Collection: Gather relevant data from various sources, ensuring data quality and integrity.
• Data Cleaning: Identify and rectify errors, missing values, and inconsistencies in the dataset. Clean data is crucial for accurate analysis.
• Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand the data's characteristics, distributions, and relationships. Visualization techniques are often used here.
• Data Transformation: Prepare the data for analysis by encoding categorical variables, scaling features, and handling outliers, if necessary.
• Model Building: Depending on the objectives, apply appropriate data analysis methods, such as regression, clustering, or deep learning.
• Model Evaluation: Depending on the problem type, assess the models' performance using metrics like Mean Absolute Error, Root Mean Squared Error , or others.
• Interpretation and Visualization: Translate the model's results into actionable insights. Visualizations, tables, and summary statistics help in conveying findings effectively.
• Deployment: Implement the insights into real-world solutions or strategies, ensuring that the data-driven recommendations are implemented.

1. Regression Analysis

Regression analysis is a powerful method for understanding the relationship between a dependent and one or more independent variables. It is applied in economics, finance, and social sciences. By fitting a regression model, you can make predictions, analyze cause-and-effect relationships, and uncover trends within your data.

2. Statistical Analysis

Statistical analysis encompasses a broad range of techniques for summarizing and interpreting data. It involves descriptive statistics (mean, median, standard deviation), inferential statistics (hypothesis testing, confidence intervals), and multivariate analysis. Statistical methods help make inferences about populations from sample data, draw conclusions, and assess the significance of results.

3. Cohort Analysis

Cohort analysis focuses on understanding the behavior of specific groups or cohorts over time. It can reveal patterns, retention rates, and customer lifetime value, helping businesses tailor their strategies.

4. Content Analysis

It is a qualitative data analysis method used to study the content of textual, visual, or multimedia data. Social sciences, journalism, and marketing often employ it to analyze themes, sentiments, or patterns within documents or media. Content analysis can help researchers gain insights from large volumes of unstructured data.

5. Factor Analysis

Factor analysis is a technique for uncovering underlying latent factors that explain the variance in observed variables. It is commonly used in psychology and the social sciences to reduce the dimensionality of data and identify underlying constructs. Factor analysis can simplify complex datasets, making them easier to interpret and analyze.

6. Monte Carlo Method

This method is a simulation technique that uses random sampling to solve complex problems and make probabilistic predictions. Monte Carlo simulations allow analysts to model uncertainty and risk, making it a valuable tool for decision-making.

7. Text Analysis

Also known as text mining , this method involves extracting insights from textual data. It analyzes large volumes of text, such as social media posts, customer reviews, or documents. Text analysis can uncover sentiment, topics, and trends, enabling organizations to understand public opinion, customer feedback, and emerging issues.

8. Time Series Analysis

Time series analysis deals with data collected at regular intervals over time. It is essential for forecasting, trend analysis, and understanding temporal patterns. Time series methods include moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models. They are widely used in finance for stock price prediction, meteorology for weather forecasting, and economics for economic modeling.

9. Descriptive Analysis

Descriptive analysis   involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.

10. Inferential Analysis

Inferential analysis   aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.

11. Exploratory Data Analysis (EDA)

EDA   focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.

12. Diagnostic Analysis

Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.

13. Predictive Analysis

Predictive analysis   involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.

14. Prescriptive Analysis

Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.

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Data analysis is a versatile and indispensable tool that finds applications across various industries and domains. Its ability to extract actionable insights from data has made it a fundamental component of decision-making and problem-solving. Let's explore some of the key applications of data analysis:

1. Business and Marketing

• Market Research: Data analysis helps businesses understand market trends, consumer preferences, and competitive landscapes. It aids in identifying opportunities for product development, pricing strategies, and market expansion.
• Sales Forecasting: Data analysis models can predict future sales based on historical data, seasonality, and external factors. This helps businesses optimize inventory management and resource allocation.

2. Healthcare and Life Sciences

• Disease Diagnosis: Data analysis is vital in medical diagnostics, from interpreting medical images (e.g., MRI, X-rays) to analyzing patient records. Machine learning models can assist in early disease detection.
• Drug Discovery: Pharmaceutical companies use data analysis to identify potential drug candidates, predict their efficacy, and optimize clinical trials.
• Genomics and Personalized Medicine: Genomic data analysis enables personalized treatment plans by identifying genetic markers that influence disease susceptibility and response to therapies.
• Risk Management: Financial institutions use data analysis to assess credit risk, detect fraudulent activities, and model market risks.
• Algorithmic Trading: Data analysis is integral to developing trading algorithms that analyze market data and execute trades automatically based on predefined strategies.
• Fraud Detection: Credit card companies and banks employ data analysis to identify unusual transaction patterns and detect fraudulent activities in real time.

4. Manufacturing and Supply Chain

• Quality Control: Data analysis monitors and controls product quality on manufacturing lines. It helps detect defects and ensure consistency in production processes.
• Inventory Optimization: By analyzing demand patterns and supply chain data, businesses can optimize inventory levels, reduce carrying costs, and ensure timely deliveries.

5. Social Sciences and Academia

• Social Research: Researchers in social sciences analyze survey data, interviews, and textual data to study human behavior, attitudes, and trends. It helps in policy development and understanding societal issues.
• Academic Research: Data analysis is crucial to scientific physics, biology, and environmental science research. It assists in interpreting experimental results and drawing conclusions.

6. Internet and Technology

• Search Engines: Google uses complex data analysis algorithms to retrieve and rank search results based on user behavior and relevance.
• Recommendation Systems: Services like Netflix and Amazon leverage data analysis to recommend content and products to users based on their past preferences and behaviors.

7. Environmental Science

• Climate Modeling: Data analysis is essential in climate science. It analyzes temperature, precipitation, and other environmental data. It helps in understanding climate patterns and predicting future trends.
• Environmental Monitoring: Remote sensing data analysis monitors ecological changes, including deforestation, water quality, and air pollution.

1. Descriptive Statistics

Descriptive statistics provide a snapshot of a dataset's central tendencies and variability. These techniques help summarize and understand the data's basic characteristics.

2. Inferential Statistics

Inferential statistics involve making predictions or inferences based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods are crucial for drawing conclusions from data and assessing the significance of findings.

3. Regression Analysis

It explores the relationship between one or more independent variables and a dependent variable. It is widely used for prediction and understanding causal links. Linear, logistic, and multiple regression are common in various fields.

4. Clustering Analysis

It is an unsupervised learning method that groups similar data points. K-means clustering and hierarchical clustering are examples. This technique is used for customer segmentation, anomaly detection, and pattern recognition.

5. Classification Analysis

Classification analysis assigns data points to predefined categories or classes. It's often used in applications like spam email detection, image recognition, and sentiment analysis. Popular algorithms include decision trees, support vector machines, and neural networks.

6. Time Series Analysis

Time series analysis deals with data collected over time, making it suitable for forecasting and trend analysis. Techniques like moving averages, autoregressive integrated moving averages (ARIMA), and exponential smoothing are applied in fields like finance, economics, and weather forecasting.

7. Text Analysis (Natural Language Processing - NLP)

Text analysis techniques, part of NLP , enable extracting insights from textual data. These methods include sentiment analysis, topic modeling, and named entity recognition. Text analysis is widely used for analyzing customer reviews, social media content, and news articles.

8. Principal Component Analysis

It is a dimensionality reduction technique that simplifies complex datasets while retaining important information. It transforms correlated variables into a set of linearly uncorrelated variables, making it easier to analyze and visualize high-dimensional data.

9. Anomaly Detection

Anomaly detection identifies unusual patterns or outliers in data. It's critical in fraud detection, network security, and quality control. Techniques like statistical methods, clustering-based approaches, and machine learning algorithms are employed for anomaly detection.

10. Data Mining

Data mining involves the automated discovery of patterns, associations, and relationships within large datasets. Techniques like association rule mining, frequent pattern analysis, and decision tree mining extract valuable knowledge from data.

11. Machine Learning and Deep Learning

ML and deep learning algorithms are applied for predictive modeling, classification, and regression tasks. Techniques like random forests, support vector machines, and convolutional neural networks (CNNs) have revolutionized various industries, including healthcare, finance, and image recognition.

12. Geographic Information Systems (GIS) Analysis

GIS analysis combines geographical data with spatial analysis techniques to solve location-based problems. It's widely used in urban planning, environmental management, and disaster response.

• Uncovering Patterns and Trends: Data analysis allows researchers to identify patterns, trends, and relationships within the data. By examining these patterns, researchers can better understand the phenomena under investigation. For example, in epidemiological research, data analysis can reveal the trends and patterns of disease outbreaks, helping public health officials take proactive measures.
• Testing Hypotheses: Research often involves formulating hypotheses and testing them. Data analysis provides the means to evaluate hypotheses rigorously. Through statistical tests and inferential analysis, researchers can determine whether the observed patterns in the data are statistically significant or simply due to chance.
• Making Informed Conclusions: Data analysis helps researchers draw meaningful and evidence-based conclusions from their research findings. It provides a quantitative basis for making claims and recommendations. In academic research, these conclusions form the basis for scholarly publications and contribute to the body of knowledge in a particular field.
• Enhancing Data Quality: Data analysis includes data cleaning and validation processes that improve the quality and reliability of the dataset. Identifying and addressing errors, missing values, and outliers ensures that the research results accurately reflect the phenomena being studied.
• Supporting Decision-Making: In applied research, data analysis assists decision-makers in various sectors, such as business, government, and healthcare. Policy decisions, marketing strategies, and resource allocations are often based on research findings.
• Identifying Outliers and Anomalies: Outliers and anomalies in data can hold valuable information or indicate errors. Data analysis techniques can help identify these exceptional cases, whether medical diagnoses, financial fraud detection, or product quality control.
• Revealing Insights: Research data often contain hidden insights that are not immediately apparent. Data analysis techniques, such as clustering or text analysis, can uncover these insights. For example, social media data sentiment analysis can reveal public sentiment and trends on various topics in social sciences.
• Forecasting and Prediction: Data analysis allows for the development of predictive models. Researchers can use historical data to build models forecasting future trends or outcomes. This is valuable in fields like finance for stock price predictions, meteorology for weather forecasting, and epidemiology for disease spread projections.
• Optimizing Resources: Research often involves resource allocation. Data analysis helps researchers and organizations optimize resource use by identifying areas where improvements can be made, or costs can be reduced.
• Continuous Improvement: Data analysis supports the iterative nature of research. Researchers can analyze data, draw conclusions, and refine their hypotheses or research designs based on their findings. This cycle of analysis and refinement leads to continuous improvement in research methods and understanding.

Data analysis is an ever-evolving field driven by technological advancements. The future of data analysis promises exciting developments that will reshape how data is collected, processed, and utilized. Here are some of the key trends of data analysis:

1. Artificial Intelligence and Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are expected to play a central role in data analysis. These technologies can automate complex data processing tasks, identify patterns at scale, and make highly accurate predictions. AI-driven analytics tools will become more accessible, enabling organizations to harness the power of ML without requiring extensive expertise.

2. Augmented Analytics

Augmented analytics combines AI and natural language processing (NLP) to assist data analysts in finding insights. These tools can automatically generate narratives, suggest visualizations, and highlight important trends within data. They enhance the speed and efficiency of data analysis, making it more accessible to a broader audience.

3. Data Privacy and Ethical Considerations

As data collection becomes more pervasive, privacy concerns and ethical considerations will gain prominence. Future data analysis trends will prioritize responsible data handling, transparency, and compliance with regulations like GDPR . Differential privacy techniques and data anonymization will be crucial in balancing data utility with privacy protection.

4. Real-time and Streaming Data Analysis

The demand for real-time insights will drive the adoption of real-time and streaming data analysis. Organizations will leverage technologies like Apache Kafka and Apache Flink to process and analyze data as it is generated. This trend is essential for fraud detection, IoT analytics, and monitoring systems.

5. Quantum Computing

It can potentially revolutionize data analysis by solving complex problems exponentially faster than classical computers. Although quantum computing is in its infancy, its impact on optimization, cryptography , and simulations will be significant once practical quantum computers become available.

6. Edge Analytics

With the proliferation of edge devices in the Internet of Things (IoT), data analysis is moving closer to the data source. Edge analytics allows for real-time processing and decision-making at the network's edge, reducing latency and bandwidth requirements.

7. Explainable AI (XAI)

Interpretable and explainable AI models will become crucial, especially in applications where trust and transparency are paramount. XAI techniques aim to make AI decisions more understandable and accountable, which is critical in healthcare and finance.

8. Data Democratization

The future of data analysis will see more democratization of data access and analysis tools. Non-technical users will have easier access to data and analytics through intuitive interfaces and self-service BI tools , reducing the reliance on data specialists.

9. Advanced Data Visualization

Data visualization tools will continue to evolve, offering more interactivity, 3D visualization, and augmented reality (AR) capabilities. Advanced visualizations will help users explore data in new and immersive ways.

10. Ethnographic Data Analysis

Ethnographic data analysis will gain importance as organizations seek to understand human behavior, cultural dynamics, and social trends. This qualitative data analysis approach and quantitative methods will provide a holistic understanding of complex issues.

11. Data Analytics Ethics and Bias Mitigation

Ethical considerations in data analysis will remain a key trend. Efforts to identify and mitigate bias in algorithms and models will become standard practice, ensuring fair and equitable outcomes.

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1. What is the difference between data analysis and data science? 

Data analysis primarily involves extracting meaningful insights from existing data using statistical techniques and visualization tools. Whereas, data science encompasses a broader spectrum, incorporating data analysis as a subset while involving machine learning, deep learning, and predictive modeling to build data-driven solutions and algorithms.

2. What are the common mistakes to avoid in data analysis?

Common mistakes to avoid in data analysis include neglecting data quality issues, failing to define clear objectives, overcomplicating visualizations, not considering algorithmic biases, and disregarding the importance of proper data preprocessing and cleaning. Additionally, avoiding making unwarranted assumptions and misinterpreting correlation as causation in your analysis is crucial.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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What is Data Analysis? (Types, Methods, and Tools)

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
• JSON Analytics + Real-Time Insights
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Product Blog

• Posted in: Analytics , Application Design , Best Practices and Tutorials
• Tagged in: data analytics , data visualization , time series

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The 7 Most Useful Data Analysis Methods and Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights. These insights are then used to determine the best course of action.

When is the best time to roll out that marketing campaign? Is the current team structure as effective as it could be? Which customer segments are most likely to purchase your new product?

Ultimately, data analytics is a crucial driver of any successful business strategy. But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

You can get a hands-on introduction to data analytics in this free short course .

In this post, we’ll explore some of the most useful data analysis techniques. By the end, you’ll have a much clearer idea of how you can transform meaningless data into business intelligence. We’ll cover:

• What is data analysis and why is it important?
• What is the difference between qualitative and quantitative data?
• Regression analysis
• Monte Carlo simulation
• Factor analysis
• Cohort analysis
• Cluster analysis
• Time series analysis
• Sentiment analysis
• The data analysis process
• The best tools for data analysis
•  Key takeaways

The first six methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the difference between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, just use the clickable menu.

1. What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These data will appear as different structures, including—but not limited to—the following:

The concept of big data —data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety. 

• Volume: As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
• Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
• Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.

This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.

Real-time data

This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.

Machine data

This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

2. What is the difference between quantitative and qualitative data?

How you analyze your data depends on the type of data you’re dealing with— quantitative or qualitative . So what’s the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explain certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more subjective interpretation. Some examples of qualitative data include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in product reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such as written text, or transcripts of spoken conversations). Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated.

Data analysts work with both quantitative and qualitative data , so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now.

3. Data analysis techniques

Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data. 

a. Regression analysis

Regression analysis is used to estimate the relationship between a set of variables. When conducting any type of regression analysis , you’re looking to see if there’s a correlation between a dependent variable (that’s the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order to identify trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let’s imagine you work for an ecommerce company and you want to examine the relationship between: (a) how much money is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—it’s the factor you’re most interested in predicting and boosting. Social media spend is your independent variable; you want to determine whether or not it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward. However: It’s important to note that, on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they don’t tell you anything about cause and effect. So, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it’s impossible to draw definitive conclusions based on this analysis alone.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For example, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you’d use a different type of regression analysis than if your dependent variable was categorical in nature (i.e. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such as customer location by continent). You can learn more about different types of dependent variables and how to choose the right regression analysis in this guide .

Regression analysis in action: Investigating the relationship between clothing brand Benetton’s advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain actions, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you might get caught in the rain or bump into your chatty neighbor, potentially delaying your journey. In everyday life, we tend to briefly weigh up the pros and cons before deciding which action to take; however, when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will be realized. The Monte Carlo method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can it tell us? To run a Monte Carlo simulation, you’ll start with a mathematical model of your data—such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in; profit, for example, or number of sales. You’ll also have a number of inputs; these are variables that may impact your output variable. If you’re looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If you knew the exact, definitive values of all your input variables, you’d quite easily be able to calculate what profit you’d be left with at the end. However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 100,000 sales and hire five new employees on a salary of $50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 12,000 sales and hire five new employees? And so on. It does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the effect of unpredictable variables on a specific output variable, making it ideal for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for risk analysis

 c. Factor analysis

Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the basis that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful not only because it condenses large datasets into smaller, more manageable samples, but also because it helps to uncover hidden patterns. This allows you to explore concepts that cannot be easily measured or observed—such as wealth, happiness, fitness, or, for a more business-relevant example, customer loyalty and satisfaction.

Let’s imagine you want to get to know your customers better, so you send out a rather long survey comprising one hundred questions. Some of the questions relate to how they feel about your company and product; for example, “Would you recommend us to a friend?” and “How would you rate the overall customer experience?” Other questions ask things like “What is your yearly household income?” and “How much are you willing to spend on skincare each month?”

Once your survey has been sent out and completed by lots of customers, you end up with a large dataset that essentially tells you one hundred different things about each customer (assuming each customer gives one hundred responses). Instead of looking at each of these responses (or variables) individually, you can use factor analysis to group them into factors that belong together—in other words, to relate them to a single underlying construct. In this example, factor analysis works by finding survey items that are strongly correlated. This is known as covariance . So, if there’s a strong positive correlation between household income and how much they’re willing to spend on skincare each month (i.e. as one increases, so does the other), these items may be grouped together. Together with other variables (survey responses), you may find that they can be reduced to a single factor such as “consumer purchasing power”. Likewise, if a customer experience rating of 10/10 correlates strongly with “yes” responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single factor such as “customer satisfaction”.

In the end, you have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further analysis, allowing you to learn more about your customers (or any other area you’re interested in exploring).

Factor analysis in action: Using factor analysis to explore customer behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is a data analytics technique that groups users based on a shared characteristic , such as the date they signed up for a service or the product they purchased. Once users are grouped into cohorts, analysts can track their behavior over time to identify trends and patterns.

So what does this mean and why is it useful? Let’s break down the above definition further. A cohort is a group of people who share a common characteristic (or action) during a given time period. Students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in the month of December may also be considered a cohort.

With cohort analysis, you’re dividing your customers or users into groups and looking at how these groups behave over time. So, rather than looking at a single, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you’re examining your customers’ behavior in the context of the customer lifecycle. As a result, you can start to identify patterns of behavior at various points in the customer journey—say, from their first ever visit to your website, through to email newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, allowing you to uncover valuable insights about the customer lifecycle.

This is useful because it allows companies to tailor their service to specific customer segments (or cohorts). Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website. Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything and, if they do, whether or not (and how frequently) they make a repeat purchase. With these insights, you’ll start to gain a much better understanding of when this particular cohort might benefit from another discount offer or retargeting ads on social media, for example. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized experience. You can learn more about how to run cohort analysis using Google Analytics .

Cohort analysis in action: How Ticketmaster used cohort analysis to boost revenue

e. Cluster analysis

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other, and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for a more targeted approach to advertising and communication. Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It’s important to note that, while cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in mind, cluster analysis is a useful starting point for understanding your data and informing further analysis. Clustering algorithms are also used in machine learning—you can learn more about clustering in machine learning in our guide .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study example

f. Time series analysis

Time series analysis is a statistical technique used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

• Trends: Stable, linear increases or decreases over an extended time period.
• Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example, you might see a peak in swimwear sales in summer around the same time every year.
• Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economic or industry-related conditions.

As you can imagine, the ability to make informed predictions about the future has immense value for business. Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis, economic forecasting, and sales forecasting. There are different types of time series models depending on the data you’re using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth look at time series analysis, refer to our guide .

Time series analysis in action: Developing a time series model to predict jute yarn demand in Bangladesh

g. Sentiment analysis

When you think of data, your mind probably automatically goes to numbers and spreadsheets.

Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially customers) write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis , a technique which belongs to the broader category of text analysis —the (usually automated) process of sorting and understanding textual data.

With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.

There are several different types of sentiment analysis models, each with a slightly different focus. The three main types include:

Fine-grained sentiment analysis

If you want to focus on opinion polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment analysis will allow you to do so.

For example, if you wanted to interpret star ratings given by customers, you might use fine-grained sentiment analysis to categorize the various ratings along a scale ranging from very positive to very negative.

Emotion detection

This model often uses complex machine learning algorithms to pick out various emotions from your textual data.

You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or your product on, say, a product review site.

Aspect-based sentiment analysis

This type of analysis allows you to identify what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign.

If a customer writes that they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but also the object towards which it’s directed.

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) algorithms and systems which are trained to associate certain inputs (for example, certain words) with certain outputs.

For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time!

Sentiment analysis in action: 5 Real-world sentiment analysis case studies

4. The data analysis process

In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data

Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Learn more: Quantitative vs. Qualitative Data: What’s the Difference? 

Cleaning the data

Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:

• Removing major errors, duplicates, and outliers
• Removing unwanted data points
• Structuring the data—that is, fixing typos, layout issues, etc.
• Filling in major gaps in data

Analyzing the data

Now that we’ve finished cleaning the data, it’s time to analyze it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:

• Descriptive analysis , which identifies what has already happened
• Diagnostic analysis , which focuses on understanding why something has happened
• Predictive analysis , which identifies future trends based on historical data
• Prescriptive analysis , which allows you to make recommendations for the future

Visualizing and sharing your findings

We’re almost at the end of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such as Google Charts, or Tableau.

Learn more: 13 of the Most Common Types of Data Visualization

To sum up the process, Will’s explained it all excellently in the following video:

5. The best tools for data analysis

As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article , but, in summary, here’s our best-of-the-best list, with links to each product:

The top 9 tools for data analysts

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Microsoft Power BI

6. Key takeaways and further reading

As you can see, there are many different data analysis techniques at your disposal. In order to turn your raw data into actionable insights, it’s important to consider what kind of data you have (is it qualitative or quantitative?) as well as the kinds of insights that will be useful within the given context. In this post, we’ve introduced seven of the most useful data analysis techniques—but there are many more out there to be discovered!

So what now? If you haven’t already, we recommend reading the case studies for each analysis technique discussed in this post (you’ll find a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that data analysts use, try out this free introductory data analytics short course. In the meantime, you might also want to read the following:

• The Best Online Data Analytics Courses for 2024
• What Is Time Series Data and How Is It Analyzed?
• What is Spatial Analysis?

8 Types of Data Analysis

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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• Data, Analysis, & AI

What is Data Analysis? Methods, Techniques & Tools

• What Is Data Analysis?

The systematic application of statistical and logical techniques to describe the data scope, modularize the data structure, condense the data representation, illustrate via images, tables, and graphs, and evaluate statistical inclinations, probability data, and derive meaningful conclusions known as Data Analysis. These analytical procedures enable us to induce the underlying inference from data by eliminating the unnecessary chaos created by its rest. Data generation is a continual process; this makes data analysis a continuous, iterative process where the collection and performing data analysis simultaneously. Ensuring data integrity is one of the essential components of data analysis. 

There are various examples where data analysis is used, ranging from transportation, risk and fraud detection, customer interaction, city planning healthcare, web search, digital advertisement, and more. 

Considering the example of healthcare, as we have noticed recently that with the outbreak of the pandemic, Coronavirus hospitals are facing the challenge of coping up with the pressure in treating as many patients as possible, considering data analysis allows to monitor machine and data usage in such scenarios to achieve efficiency gain. 

Before diving any more in-depth, make the following pre-requisites for proper Data Analysis: 

• Ensure availability of the necessary analytical skills
• Ensure appropriate implementation of data collection methods and analysis.
• Determine the statistical significance
• Check for inappropriate analysis
• Ensure the presence of legitimate and unbiased inference
• Ensure the reliability and validity of data, data sources, data analysis methods, and inferences derived.
• Account for the extent of analysis 
• Data Analysis Methods

There are two main methods of Data Analysis: 

1. Qualitative Analysis

This approach mainly answers questions such as ‘why,’ ‘what’ or ‘how.’ Each of these questions is addressed via quantitative techniques such as questionnaires, attitude scaling, standard outcomes, and more. Such analysis is usually in the form of texts and narratives, which might also include audio and video representations.

2. Quantitative Analysis

Generally, this analysis is measured in terms of numbers. The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.  

The other techniques include: 

3. Text analysis

Text analysis is a technique to analyze texts to extract machine-readable facts. It aims to create structured data out of free and unstructured content. The process consists of slicing and dicing heaps of unstructured, heterogeneous files into easy-to-read, manage and interpret data pieces. It is also known as text mining, text analytics, and information extraction.

The ambiguity of human languages is the biggest challenge of text analysis. For example, humans know that “Red Sox Tames Bull” refers to a baseball match. Still, if this text is fed to a computer without background knowledge, it would generate several linguistically valid interpretations. Sometimes people who are not interested in baseball might have trouble understanding it too.

4. Statistical analysis

Statistics involves data collection, interpretation, and validation. Statistical analysis is the technique of performing several statistical operations to quantify the data and apply statistical analysis. Quantitative data involves descriptive data like surveys and observational data. It is also called a descriptive analysis. It includes various tools to perform statistical data analysis such as SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Stat soft, and more.

5. Diagnostic analysis

The diagnostic analysis is a step further to statistical analysis to provide a more in-depth analysis to answer the questions. It is also referred to as root cause analysis as it includes processes like data discovery, mining, and drill down and drill through.

The functions of diagnostic analytics fall into three categories:

• Identify anomalies: After performing statistical analysis, analysts are required to identify areas requiring further study as such data raise questions that cannot be answered by looking at the data.
• Drill into the Analytics (discovery): Identification of the data sources helps analysts explain the anomalies. This step often requires analysts to look for patterns outside the existing data sets. It requires pulling in data from external sources, thus identifying correlations and determining if they are causal in nature.
• Determine Causal Relationships: Hidden relationships are uncovered by looking at events that might have resulted in the identified anomalies. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.

6. Predictive analysis

Predictive analysis uses historical data and feds it into the machine learning model to find critical patterns and trends. The model is applied to the current data to predict what would happen next. Many organizations prefer it because of its various advantages like volume and type of data, faster and cheaper computers, easy-to-use software, tighter economic conditions, and a need for competitive differentiation.

The following are the common uses of predictive analysis:

• Fraud Detection: Multiple analytics methods improves pattern detection and prevents criminal behavior.
• Optimizing Marketing Campaigns: Predictive models help businesses attract, retain, and grow their most profitable customers. It also helps in determining customer responses or purchases, promoting cross-sell opportunities.
• Improving Operations: The use of predictive models also involves forecasting inventory and managing resources. For example, airlines use predictive models to set ticket prices.
• Reducing Risk:  The credit score used to assess a buyer’s likelihood of default for purchases is generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

7. Prescriptive Analysis

Prescriptive analytics suggests various courses of action and outlines the potential implications that could be reached after predictive analysis. Prescriptive analysis generating automated decisions or recommendations requires specific and unique algorithmic and clear direction from those utilizing the analytical techniques.

Data Analysis Masterclass (4 courses in 1)

• Data Analysis Process

Once you set out to collect data for analysis, you are overwhelmed by the amount of information you find to make a clear, concise decision. With so much data to handle, you need to identify relevant data for your analysis to derive an accurate conclusion and make informed decisions. The following simple steps help you identify and sort out your data for analysis.

1. Data Requirement Specification - define your scope:

• Define short and straightforward questions, the answers to which you finally need to make a decision.
• Define measurement parameters
• Define which parameter you take into account and which one you are willing to negotiate.
• Define your unit of measurement. Ex – Time, Currency, Salary, and more.

2. Data Collection

• Gather your data based on your measurement parameters. 
• Collect data from databases, websites, and many other sources. This data may not be structured or uniform, which takes us to the next step.

3. Data Processing

• Organize your data and make sure to add side notes, if any.
• Cross-check data with reliable sources.
• Convert the data as per the scale of measurement you have defined earlier.
• Exclude irrelevant data.

4. Data Analysis

• Once you have collected your data, perform sorting, plotting, and identifying correlations.  
• As you manipulate and organize your data, you may need to traverse your steps again from the beginning. You may need to modify your question, redefine parameters, and reorganize your data. 
• Make use of the different tools available for data analysis.

5. Infer and Interpret Results

• Review if the result answers your initial questions
• Review if you have considered all parameters for making the decision
• Review if there is any hindering factor for implementing the decision.
• Choose data visualization techniques to communicate the message better. These visualization techniques may be charts, graphs, color coding, and more.

Once you have an inference, always remember it is only a hypothesis. Real-life scenarios may always interfere with your results. In Data Analysis, there are a few related terminologies that identity with different phases of the process. 

1. Data Mining

This process involves methods in finding patterns in the data sample. 

2. Data Modelling

This refers to how an organization organizes and manages its data. 

Data Analysis Techniques 

There are different techniques for Data Analysis depending upon the question at hand, the type of data, and the amount of data gathered. Each focuses on taking onto the new data, mining insights, and drilling down into the information to transform facts and figures into decision-making parameters. Accordingly, the different techniques of data analysis can be categorized as follows:

1. Techniques based on Mathematics and Statistics

• Descriptive Analysis : Descriptive Analysis considers the historical data, Key Performance Indicators and describes the performance based on a chosen benchmark. It takes into account past trends and how they might influence future performance.
• Dispersion Analysis : Dispersion in the area onto which a data set is spread. This technique allows data analysts to determine the variability of the factors under study.
• Regression Analysis : This technique works by modeling the relationship between a dependent variable and one or more independent variables. A regression model can be linear, multiple, logistic, ridge, non-linear, life data, and more.
• Factor Analysis : This technique helps to determine if there exists any relationship between a set of variables. This process reveals other factors or variables that describe the patterns in the relationship among the original variables. Factor Analysis leaps forward into useful clustering and classification procedures.
• Discriminant Analysis : It is a classification technique in data mining. It identifies the different points on different groups based on variable measurements. In simple terms, it identifies what makes two groups different from one another; this helps to identify new items.
• Time Series Analysis : In this kind of analysis, measurements are spanned across time, which gives us a collection of organized data known as time series.

2. Techniques based on Artificial Intelligence and Machine Learning

• Artificial Neural Networks: a Neural network is a biologically-inspired programming paradigm that presents a brain metaphor for processing information. An Artificial Neural Network is a system that changes its structure based on information that flows through the network. ANN can accept noisy data and are highly accurate. They can be considered highly dependable in business classification and forecasting applications.
• Decision Trees : As the name stands, it is a tree-shaped model representing a classification or regression model. It divides a data set into smaller subsets, simultaneously developing into a related decision tree.
• Evolutionary Programming : This technique combines the different types of data analysis using evolutionary algorithms. It is a domain-independent technique, which can explore ample search space and manages attribute interaction very efficiently.
• Fuzzy Logic : It is a data analysis technique based on the probability that helps handle the uncertainties in data mining techniques. 

3. Techniques based on Visualization and Graphs

• Column Chart, Bar Chart : Both these charts are used to present numerical differences between categories. The column chart takes to the height of the columns to reflect the differences. Axes interchange in the case of the bar chart.
• Line Chart : This chart represents the change of data over a continuous interval of time. 
• Area Chart : This concept is based on the line chart. It also fills the area between the polyline and the axis with color, representing better trend information.
• Pie Chart : It is used to represent the proportion of different classifications. It is only suitable for only one series of data. However, it can be made multi-layered to represent the proportion of data in different categories.
• Funnel Chart : This chart represents the proportion of each stage and reflects the size of each module. It helps in comparing rankings.
• Word Cloud Chart: It is a visual representation of text data. It requires a large amount of data, and the degree of discrimination needs to be high for users to perceive the most prominent one. It is not a very accurate analytical technique.
• Gantt Chart : It shows the actual timing and the progress of the activity compared to the requirements.
• Radar Chart : It is used to compare multiple quantized charts. It represents which variables in the data have higher values and which have lower values. A radar chart is used for comparing classification and series along with proportional representation.
• Scatter Plot : It shows the distribution of variables in points over a rectangular coordinate system. The distribution in the data points can reveal the correlation between the variables.
• Bubble Chart : It is a variation of the scatter plot. Here, in addition to the x and y coordinates, the bubble area represents the 3rd value.
• Gauge: It is a kind of materialized chart. Here the scale represents the metric, and the pointer represents the dimension. It is a suitable technique to represent interval comparisons.
• Frame Diagram : It is a visual representation of a hierarchy in an inverted tree structure.
• Rectangular Tree Diagram : This technique is used to represent hierarchical relationships but at the same level. It makes efficient use of space and represents the proportion represented by each rectangular area.
• Regional Map: It uses color to represent value distribution over a map partition.
• Point Map: It represents the geographical distribution of data in points on a geographical background. When the points are the same in size, it becomes meaningless for single data, but if the points are as a bubble, it also represents the size of the data in each region.
• Flow Map: It represents the relationship between an inflow area and an outflow area. It represents a line connecting the geometric centers of gravity of the spatial elements. The use of dynamic flow lines helps reduce visual clutter.
• Heat Map : This represents the weight of each point in a geographic area. The color here represents the density.

Let us now read about a few tools used in data analysis in research. 

• Data Analysis Tools

There are several data analysis tools  available in the market, each with its own set of functions. The selection of tools should always be based on the type of analysis performed and the type of data worked. Here is a list of a few compelling tools for Data Analysis. 

It has various compelling features, and with additional plugins installed, it can handle a massive amount of data. So, if you have data that does not come near the significant data margin, Excel can be a versatile tool for data analysis.

Looking to learn Excel? Data Analysis with Excel Pivot Tables course is the highest-rated Excel course on udemy.

It falls under the BI Tool category, made for the sole purpose of data analysis. The essence of Tableau is the Pivot Table and Pivot Chart and works towards representing data in the most user-friendly way. It additionally has a data cleaning feature along with brilliant analytical functions.

If you want to learn Tableau, udemy's online course Hands-On Tableau Training For Data Science can be a great asset for you.

3. Power BI

It initially started as a plugin for Excel, but later on, detached from it to develop in one of the most data analytics tools. It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can implement sophisticated advanced analytics similar to writing Excel formulas.

4. Fine Report

Fine Report comes with a straightforward drag and drops operation, which helps design various reports and build a data decision analysis system. It can directly connect to all kinds of databases, and its format is similar to that of Excel. Additionally, it also provides a variety of dashboard templates and several self-developed visual plug-in libraries.

5. R & Python

These are programming languages that are very powerful and flexible. R is best at statistical analysis, such as normal distribution, cluster classification algorithms, and regression analysis. It also performs individual predictive analyses like customer behavior, spending, items preferred by him based on his browsing history, and more. It also involves concepts of machine learning and artificial intelligence.

It is a programming language for data analytics and data manipulation, which can easily access data from any source. SAS has introduced a broad set of customer profiling products for web, social media, and marketing analytics. It can predict their behaviors, manage, and optimize communications.

This is our complete beginner's guide on "What is Data Analysis". If you want to learn more about data analysis, Complete Introduction to Business Data Analysis is a great introductory course.

Data Analysis is the key to any business, whether starting up a new venture, making marketing decisions, continuing with a particular course of action, or going for a complete shut-down. The inferences and the statistical probabilities calculated from data analysis help base the most critical decisions by ruling out all human bias. Different analytical tools have overlapping functions and different limitations, but they are also complementary tools. Before choosing a data analytical tool, it is essential to consider the scope of work, infrastructure limitations, economic feasibility, and the final report to be prepared.

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In this article

• Data Analysis Techniques 
• 30+ Top Data Analyst Interview Questions and Answers in 2024 Data Analytics Career Development Interview Questions
• Best Data Analysis Software in 2024 Data Analytics
• 7 Top Data Analytics Tools to Use in 2024 Data Analytics

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng Published: May 18, 2022

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A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

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What Is Data Analysis? Methods, Process & Tools

Up to 55% of data collected by companies goes unused for analysis .

That’s a large chunk of insights companies are missing out on.

So, what can you do to make sure your data doesn't get lost among the noise, and how can you properly analyze your data? What even is data analysis?

In this guide, you’ll learn all this and more.

Let’s dive in.

What Is Data Analysis?

• Why Is Data Analysis Important?
• Data Analysis Techniques

Data Analysis Process

Data analysis tools, data analysis tips.

Data analysis is the process of cleaning, analyzing, and visualizing data, with the goal of discovering valuable insights and driving smarter business decisions.

The methods you use to analyze data will depend on whether you’re analyzing quantitative or qualitative data .

Either way, you’ll need data analysis tools to help you extract useful information from business data, and help make the data analysis process easier.

You’ll often hear the term data analytics in business, which is the science or discipline that encompasses the whole process of data management, from data collection and storage to data analysis andvisualization.

Data analysis, while part of the data management process, focuses on the process of turning raw data into useful statistics, information, and explanations.

Why Is Data Analysis important in 2022?

Data is everywhere: in spreadsheets, your sales pipeline, social media platforms, customer satisfaction surveys , customer support tickets, and more. In our modern information age it’s created at blinding speeds and, when data is analyzed correctly, can be a company’s most valuable asset. 

Businesses need to know what their customers need, so that they can increase customer retention and attract new customers. But to know exactly what customers need and what their pain points are, businesses need to deep-dive into their customer data.

In short, through data analysis businesses can reveal insights that tell you where you need to focus your efforts to help your company grow.  

It can help businesses improve specific aspects about their products and services, as well as their overall brand image and customer experience .

Product teams, for example, often analyze customer feedback to understand how customers interact with their product, what they’re frustrated with, and which new features they’d like to see. Then, they translate this insight into UX improvements, new features, and enhanced functionalities.

Through data analysis, you can also detect the weaknesses and strengths of your competition, uncovering opportunities for improvement.

6 Types of Data Analysis: Techniques and Methods

There are a number of useful data analysis techniques you can use to discover insights in all types of data, and emerging data analysis trends that can help you stay ahead of your competitors.

Types of data analysis:

• Text Analysis
• Descriptive Analysis
• Inferential Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Text Analysis: What is happening?

Text analysis , also text analytics or data mining, uses machine learning with natural language processing (NLP) to organize unstructured text data so that it can be properly analyzed for valuable insights. Text analysis is a form of qualitative analysis that is concerned with more than just statistics and numerical values.

By transforming human language into machine-readable data, text analysis tools can sort text by topic, extract keywords, and read for emotion and intent. It tells us “What is happening” as specific, often subjective data. It offers more in-depth and targeted views into why something may be happening, or why something happened.

You can use text analysis to detect topics in customer feedback, for example, and understand which aspects of your brand are important to your customers. 

Try out this survey analyzer that sorts open-ended survey responses into different topics:

Test with your own text

Sentiment analysis is another approach to text analysis, used to analyze data and sort it as Positive, Negative, or Neutral to gain in-depth knowledge about how customers feel towards each aspect . 

Try out this sentiment analyzer , below, to get an idea of how text analysis works.

Descriptive Analysis: What happened?

Descriptive data analysis provides the “What happened?” when analyzing quantitative data. It is the most basic and most common form of data analysis concerned with describing, summarizing, and identifying patterns through calculations of existing data, like mean, median, mode, percentage, frequency, and range. 

Descriptive analysis is usually the baseline from which other data analysis begins. It is, no doubt, very useful for producing things like revenue reports and KPI dashboards. However, as it is only concerned with statistical analysis and absolute numbers, it can’t provide the reason or motivation for why and how those numbers developed.

Inferential Analysis: What happened?

Inferential analysis generalizes or hypothesizes about “What happened?” by comparing statistics from groups within an entire population: the population of a country, existing customer base, patients in a medical study, etc. The most common methods for conducting inferential statistics are hypothesis tests and estimation theories.

Inferential analysis is used widely in market research, to compare two variables in an attempt to reach a conclusion: money spent by female customers vs. male or among different age groups, for example. Or it can be used to survey a sample set of the population in an attempt to extrapolate information about the entire population. In this case it is necessary to properly calculate for a representative sample of the population.

Diagnostic Analysis: Why did it happen?

Diagnostic analysis, also known as root cause analysis, aims to answer “Why did 'X' happen?” . It uses insights from statistical analysis to attempt to understand the cause or reason behind statistics, by identifying patterns or deviations within the data to answer for why .

Diagnostic analysis can be helpful to understand customer behavior, to find out which marketing campaigns actually increase sales, for example. Or let’s say you notice a sudden decrease in customer complaints: Why did this happen?  

Perhaps you fired a certain employee or hired new ones. Maybe you have a new online interface or added a particular product feature. Diagnostic analysis can help calculate the correlation between these possible causes and existing data points. 

Predictive Analysis: What is likely to happen?

Predictive analysis uses known data to postulate about future events. It is concerned with “What is likely to happen.” Used in sales analysis , it often combines demographic data and purchase data with other data points to predict the actions of customers. 

For example, as the demographics of a certain area change, this will affect the ability of certain businesses to exist there. Or as the salary of a certain customer increases, theoretically, they will be able to buy more of your products.

There is often a lot of extrapolative guesswork involved in predictive analysis, but the more data points you have on a given demographic or individual customer, the more accurate the prediction is likely to be. 

Prescriptive Analysis: What action to take

Prescriptive analysis is the most advanced form of analysis, as it combines all of your data and analytics, then outputs a model prescription: What action to take. Prescriptive analysis works to analyze multiple scenarios, predict the outcome of each, and decide which is the best course of action based on the findings.

Artificial intelligence is an example of prescriptive analysis that’s at the cutting edge of data analysis. AI allows for prescriptive analysis that can ingest and break down massive amounts of data and effectively teach itself how to use the information and make its own informed decisions.

AI used to require huge computing power, making it difficult for businesses to implement. However, with the rise of more advanced data analysis tools , there are many exciting options available.

To speed up your data analysis process, you should consider integrating data analysis tools .

There are many data analysis tools you can get started with, depending on your technical skills, budget, and type of data you want to analyze. Most tools can easily be integrated via APIs and one-click integrations. 

If using an API, you might need a developer’s help to set it up. Once connected, your data can run freely through your data analysis tools.

Here’s a quick rundown of the top data analysis tools that can help you perform everything from text analysis to data visualization.

• MonkeyLearn – No-code machine learning platform that provides a full suite of text analysis tools and a robust API . Easily build custom machine learning models in a point and click interface.
• KNIME: – Open-source platform for building advanced machine learning solutions, and visualizing data.
• RapidMiner – For data analytics teams that want to tackle challenging tasks and handle large amounts of data.
• Microsoft Excel – Filter, organize, and visualize quantitative data. The perfect tool for performing simple data analysis. Explore common functions and formulas for data analysis in Excel .
• Tableau – A powerful analytics and data visualization platform. Connect all your data and create interactive dashboards that update in real-time. 
• R – A free software environment for statistical computing and graphics. Learning R is relatively easy, even if you don’t have a programming background.
• Python – The preferred programming language for machine learning. Use it to build data analysis solutions for various use cases.

You’ll need to implement a data analysis process to get the most out of your data. While it can be complex to perform data analysis, depending on the type of data you’re analyzing, there are some hard and fast rules that you can follow.

Below, we’ve outlined the steps you’ll need to follow to analyze your data :

• Data Decision
• Data Collection
• Data Cleaning
• Data Analysis
• Data Interpretation
• Data Visualization

1. Data Decision

First, you’ll need to set  clear objectives. What do you want to gain from your data analysis.

This will help you determine the type of data that you’ll need to collect and analyze, and which data analysis technique you need to apply.

2. Data Collection

Data is everywhere, and you’ll want to bring it together in one place ready for analysis.

Whether you’re collecting quantitative or qualitative data, Excel is a great platform for storing your data, or you could connect data sources directly to your analysis tools via APIs and integrations.

3. Data Cleaning

It’s likely that unstructured data will need to be cleaned before analyzing it to gain more accurate results.

Get rid of the noise, like special characters, punctuation marks, stopwords (and, too, she, they), HTML tags, duplicates, etc. Discover some more in-depth tips on how to clean your data .

4. Data Analysis

Once your data has been cleaned it will be ready for analysis. As you choose topics to focus on and parameters for measuring your data, you might notice that you don’t have enough relevant data. That might mean you have to go back to the data collection phase.

It’s important to remember that data analysis is not a linear process. You’ll have to go back and forth and reiterate. During the actual analysis, you’ll benefit from using data analysis tools that will make it easier to understand, interpret, and draw clear conclusions from your data.

5. Data Interpretation

Remember the goals you set at the beginning?

Now you can interpret the results of your data to help you reach your goals. Structure the results in a way that’s clear and makes sense to all teams. And make decisions based on what you’ve learned.

6. Data Visualization

Dashboards are a great way to aggregate your data, and make it easy to spot trends and patterns. Some data analysis tools, like MonkeyLearn , have in-built dashboards or you can connect to your existing BI tools.

Check out MonkeyLearn’s data dashboard, below, and try out the public data visualization dashboard , where you can slice and dice your data by topic, keyword, sentiment, and more.

Remember data analysis is a reiterative process. 

It can be painstaking and tedious at times, especially if you are manually analyzing huge amounts of data. 

However, once you’ve defined your goals and collected enough relevant data, you should be well on your way to discovering those valuable insights.

So, without further ado, here are some final tips before you set off on your data analysis journey:

• Collect as much data as possible – the more relevant data you have, the more accurate your insights will be data. 
• Systematically reach out to your customers – up-to-date insights will help your business grow and, besides, your customers' needs are constantly changing – which means your data is too. To stay relevant, keep on top of what your customers are requesting or complaining about.  
• Keep data analysis in-house – your ‘data analyst’ should know your business and understand your strategic goals. Remember that the insights you might uncover from performing data analysis could lead to valuable business decisions. The more familiar someone is with your data and goals, the more likely they are to find value in your data. 
• Remember, data is everywhere – Don’t forget to analyze data from external sources too. From third-party payment processing services to public online reviews.

Get Started with Data Analysis

There is almost no end to the possibilities of data analysis when you know how to do it right. Whether quantitative or qualitative, there are a number of analytical solutions and pathways to get real insights from your data.

Performing text analysis on your unstructured text data can offer huge advantages and potential advancements for your company, whether it comes from surveys, social media, customer service tickets – the list goes on and on. There is a wealth of information to be gathered from text data you may not have even considered.

MonkeyLearn offers dozens of easy-to-use text analysis tools that can be up and running in just a few minutes to help you get the most from your data. Schedule a demo to see how it works.

Inés Roldós

January 9th, 2021

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What is Data Analysis? Definition, Tools, Examples

Appinio Research · 11.04.2024 · 35min read

Have you ever wondered how businesses make decisions, scientists uncover new discoveries, or governments tackle complex challenges? The answer often lies in data analysis. In today's data-driven world, organizations and individuals alike rely on data analysis to extract valuable insights from vast amounts of information. Whether it's understanding customer preferences, predicting future trends, or optimizing processes, data analysis plays a crucial role in driving informed decision-making and problem-solving. This guide will take you through the fundamentals of analyzing data, exploring various techniques and tools used in the process, and understanding the importance of data analysis in different domains. From understanding what data analysis is to delving into advanced techniques and best practices, this guide will equip you with the knowledge and skills to harness the power of data and unlock its potential to drive success and innovation.

What is Data Analysis?

Data analysis is the process of examining, cleaning, transforming, and interpreting data to uncover insights, identify patterns, and make informed decisions. It involves applying statistical, mathematical, and computational techniques to understand the underlying structure and relationships within the data and extract actionable information from it. Data analysis is used in various domains, including business, science, healthcare, finance, and government, to support decision-making, solve complex problems, and drive innovation.

Importance of Data Analysis

Data analysis is crucial in modern organizations and society, providing valuable insights and enabling informed decision-making across various domains. Here are some key reasons why data analysis is important:

• Informed Decision-Making:  Data analysis enables organizations to make evidence-based decisions by providing insights into past trends, current performance, and future predictions.
• Improved Efficiency:  By analyzing data, organizations can identify inefficiencies, streamline processes, and optimize resource allocation, leading to increased productivity and cost savings.
• Identification of Opportunities:  Data analysis helps organizations identify market trends, customer preferences, and emerging opportunities, allowing them to capitalize on new business prospects and stay ahead of competitors.
• Risk Management:  Data analysis enables organizations to assess and mitigate risks by identifying potential threats, vulnerabilities, and opportunities for improvement.
• Performance Evaluation:  Data analysis allows organizations to measure and evaluate their performance against key metrics and objectives, facilitating continuous improvement and accountability.
• Innovation and Growth:  By analyzing data, organizations can uncover new insights, discover innovative solutions, and drive growth through product development, process optimization, and strategic initiatives.
• Personalization and Customer Satisfaction:  Data analysis enables organizations to understand customer behavior, preferences, and needs, allowing them to deliver personalized products, services, and experiences that enhance customer satisfaction and loyalty .
• Regulatory Compliance:  Data analysis helps organizations ensure compliance with regulations and standards by monitoring and analyzing data for compliance-related issues, such as fraud, security breaches, and data privacy violations.

Overall, data analysis empowers organizations to harness the power of data to drive strategic decision-making, improve performance, and achieve their goals and objectives.

Understanding Data

Understanding the nature of data is fundamental to effective data analysis. It involves recognizing the types of data, their sources, methods of collection, and the crucial process of cleaning and preprocessing data before analysis.

Types of Data

Data can be broadly categorized into two main types: quantitative and qualitative data .

• Quantitative data:  This type of data represents quantities and is measurable. It deals with numbers and numerical values, allowing for mathematical calculations and statistical analysis. Examples include age, height, temperature, and income.
• Qualitative data:  Qualitative data describes qualities or characteristics and cannot be expressed numerically. It focuses on qualities, opinions, and descriptions that cannot be measured. Examples include colors, emotions, opinions, and preferences.

Understanding the distinction between these two types of data is essential as it influences the choice of analysis techniques and methods.

Data Sources

Data can be obtained from various sources, depending on the nature of the analysis and the project's specific requirements.

• Internal databases:  Many organizations maintain internal databases that store valuable information about their operations, customers, products, and more. These databases often contain structured data that is readily accessible for analysis.
• External sources:  External data sources provide access to a wealth of information beyond an organization's internal databases. This includes data from government agencies, research institutions, public repositories, and third-party vendors. Examples include census data, market research reports, and social media data.
• Sensor data:  With the proliferation of IoT (Internet of Things) devices, sensor data has become increasingly valuable for various applications. These devices collect data from the physical environment, such as temperature, humidity, motion, and location, providing real-time insights for analysis.

Understanding the available data sources is crucial for determining the scope and scale of the analysis and ensuring that the data collected is relevant and reliable.

Data Collection Methods

The process of collecting data can vary depending on the research objectives, the nature of the data, and the target population. Various data collection methods are employed to gather information effectively.

• Surveys :  Surveys involve collecting information from individuals or groups through questionnaires, interviews, or online forms. Surveys are versatile and can be conducted in various formats, including face-to-face interviews, telephone interviews, paper surveys, and online surveys.
• Observational studies:  Observational studies involve observing and recording behavior, events, or phenomena in their natural settings without intervention. This method is often used in fields such as anthropology, sociology, psychology, and ecology to gather qualitative data.
• Experiments:  Experiments are controlled investigations designed to test hypotheses and determine cause-and-effect relationships between variables. They involve manipulating one or more variables while keeping others constant to observe the effect on the dependent variable.

Understanding the strengths and limitations of different data collection methods is essential for designing robust research studies and ensuring the quality and validity of the data collected. For businesses seeking efficient and insightful data collection, Appinio offers a seamless solution.

With its user-friendly interface and comprehensive features, Appinio simplifies the process of gathering valuable insights from diverse audiences. Whether conducting surveys, observational studies, or experiments, Appinio provides the tools and support needed to collect, analyze, and interpret data effectively.

Ready to elevate your data collection efforts? Book a demo today and experience the power of real-time market research with Appinio!

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Data Cleaning and Preprocessing

Data cleaning and preprocessing are essential steps in the data analysis process aimed at improving data quality, consistency, and reliability.

• Handling missing values:  Missing values are common in datasets and can arise due to various reasons, such as data entry errors, equipment malfunction, or non-response. Techniques for handling missing values include deletion, imputation, and predictive modeling.
• Dealing with outliers:  Outliers are data points that deviate significantly from the rest of the data and may distort the analysis results. It's essential to identify and handle outliers appropriately using statistical methods, visualization techniques, or domain knowledge.
• Standardizing data:  Standardization involves scaling variables to a common scale to facilitate comparison and analysis. It ensures that variables with different units or scales contribute equally to the analysis results. Standardization techniques include z-score normalization, min-max scaling, and robust scaling.

By cleaning and preprocessing the data effectively, you can ensure that it is accurate, consistent, and suitable for analysis, leading to more reliable and actionable insights.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial phase in the data analysis process, where you explore and summarize the main characteristics of your dataset. This phase helps you gain insights into the data, identify patterns, and detect anomalies or outliers. Let's delve into the key components of EDA.

Descriptive Statistics

Descriptive statistics provide a summary of the main characteristics of your dataset, allowing you to understand its central tendency, variability, and distribution. Standard descriptive statistics include measures such as mean, median, mode, standard deviation, variance, and range.

• Mean: The average value of a dataset, calculated by summing all values and dividing by the number of observations. Mean = (Sum of all values) / (Number of observations)
• Median:  The middle value of a dataset when it is ordered from least to greatest.
• Mode:  The value that appears most frequently in a dataset.
• Standard deviation:  A measure of the dispersion or spread of values around the mean. Standard deviation = Square root of [(Sum of squared differences from the mean) / (Number of observations)]
• Variance: The average of the squared differences from the mean. Variance = Sum of squared differences from the mean / Number of observations
• Range:  The difference between the maximum and minimum values in a dataset.

Descriptive statistics provide initial insights into the central tendencies and variability of the data, helping you identify potential issues or areas for further exploration.

Data Visualization Techniques

Data visualization is a powerful tool for exploring and communicating insights from your data. By representing data visually, you can identify patterns, trends, and relationships that may not be apparent from raw numbers alone. Common data visualization techniques include:

• Histograms:  A graphical representation of the distribution of numerical data divided into bins or intervals.
• Scatter plots:  A plot of individual data points on a two-dimensional plane, useful for visualizing relationships between two variables.
• Box plots:  A graphical summary of the distribution of a dataset, showing the median, quartiles, and outliers.
• Bar charts:  A visual representation of categorical data using rectangular bars of varying heights or lengths.
• Heatmaps :  A visual representation of data in a matrix format, where values are represented using colors to indicate their magnitude.

Data visualization allows you to explore your data from different angles, uncover patterns, and communicate insights effectively to stakeholders.

Identifying Patterns and Trends

During EDA, you'll analyze your data to identify patterns, trends, and relationships that can provide valuable insights into the underlying processes or phenomena.

• Time series analysis:  Analyzing data collected over time to identify temporal patterns, seasonality, and trends.
• Correlation analysis:  Examining the relationships between variables to determine if they are positively, negatively, or not correlated.
• Cluster analysis:  Grouping similar data points together based on their characteristics to identify natural groupings or clusters within the data.
• Principal Component Analysis (PCA):  A dimensionality reduction technique used to identify the underlying structure in high-dimensional data and visualize it in lower-dimensional space.

By identifying patterns and trends in your data, you can uncover valuable insights that can inform decision-making and drive business outcomes.

Handling Missing Values and Outliers

Missing values and outliers can distort the results of your analysis, leading to biased conclusions or inaccurate predictions. It's essential to handle them appropriately during the EDA phase. Techniques for handling missing values include:

• Deletion:  Removing observations with missing values from the dataset.
• Imputation:  Filling in missing values using methods such as mean imputation, median imputation, or predictive modeling.
• Detection and treatment of outliers:  Identifying outliers using statistical methods or visualization techniques and either removing them or transforming them to mitigate their impact on the analysis.

By addressing missing values and outliers, you can ensure the reliability and validity of your analysis results, leading to more robust insights and conclusions.

Data Analysis Examples

Data analysis spans various industries and applications. Here are a few examples showcasing the versatility and power of data-driven insights.

Business and Marketing

Data analysis is used to understand customer behavior, optimize marketing strategies, and drive business growth. For instance, a retail company may analyze sales data to identify trends in customer purchasing behavior, allowing them to tailor their product offerings and promotional campaigns accordingly.

Similarly, marketing teams use data analysis techniques to measure the effectiveness of advertising campaigns, segment customers based on demographics or preferences, and personalize marketing messages to improve engagement and conversion rates.

Healthcare and Medicine

In healthcare, data analysis is vital in improving patient outcomes, optimizing treatment protocols, and advancing medical research. For example, healthcare providers may analyze electronic health records (EHRs) to identify patterns in patient symptoms, diagnoses, and treatment outcomes, helping to improve diagnostic accuracy and treatment effectiveness.

Pharmaceutical companies use data analysis techniques to analyze clinical trial data, identify potential drug candidates, and optimize drug development processes, ultimately leading to the discovery of new treatments and therapies for various diseases and conditions.

Finance and Economics

Data analysis is used to inform investment decisions, manage risk, and detect fraudulent activities. For instance, investment firms analyze financial market data to identify trends, assess market risk, and make informed investment decisions.

Banks and financial institutions use data analysis techniques to detect fraudulent transactions, identify suspicious activity patterns, and prevent financial crimes such as money laundering and fraud. Additionally, economists use data analysis to analyze economic indicators, forecast economic trends, and inform policy decisions at the national and global levels.

Science and Research

Data analysis is essential for generating insights, testing hypotheses, and advancing knowledge in various fields of scientific research. For example, astronomers analyze observational data from telescopes to study the properties and behavior of celestial objects such as stars, galaxies, and black holes.

Biologists use data analysis techniques to analyze genomic data, study gene expression patterns, and understand the molecular mechanisms underlying diseases. Environmental scientists use data analysis to monitor environmental changes, track pollution levels, and assess the impact of human activities on ecosystems and biodiversity.

These examples highlight the diverse applications of data analysis across different industries and domains, demonstrating its importance in driving innovation, solving complex problems, and improving decision-making processes.

Statistical Analysis

Statistical analysis is a fundamental aspect of data analysis, enabling you to draw conclusions, make predictions, and infer relationships from your data. Let's explore various statistical techniques commonly used in data analysis.

Hypothesis Testing

Hypothesis testing is a method used to make inferences about a population based on sample data. It involves formulating a hypothesis about the population parameter and using sample data to determine whether there is enough evidence to reject or fail to reject the null hypothesis.

Common types of hypothesis tests include:

• t-test:  Used to compare the means of two groups and determine if they are significantly different from each other.
• Chi-square test:  Used to determine whether there is a significant association between two categorical variables.
• ANOVA (Analysis of Variance):  Used to compare means across multiple groups to determine if there are significant differences.

Correlation Analysis

Correlation analysis is used to measure the strength and direction of the relationship between two variables. The correlation coefficient, typically denoted by "r," ranges from -1 to 1, where:

• r = 1:  Perfect positive correlation
• r = -1:  Perfect negative correlation
• r = 0:  No correlation

Common correlation coefficients include:

• Pearson correlation coefficient:  Measures the linear relationship between two continuous variables.
• Spearman rank correlation coefficient:  Measures the strength and direction of the monotonic relationship between two variables, particularly useful for ordinal data.

Correlation analysis helps you understand the degree to which changes in one variable are associated with changes in another variable.

Regression Analysis

Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It aims to predict the value of the dependent variable based on the values of the independent variables. Common types of regression analysis include:

• Linear regression:  Models the relationship between the dependent variable and one or more independent variables using a linear equation. It is suitable for predicting continuous outcomes.
• Logistic regression:  Models the relationship between a binary dependent variable and one or more independent variables. It is commonly used for classification tasks.

Regression analysis helps you understand how changes in one or more independent variables are associated with changes in the dependent variable.

ANOVA (Analysis of Variance)

ANOVA is a statistical technique used to analyze the differences among group means in a sample. It is often used to compare means across multiple groups and determine whether there are significant differences between them. ANOVA tests the null hypothesis that the means of all groups are equal against the alternative hypothesis that at least one group mean is different.

ANOVA can be performed in various forms, including:

• One-way ANOVA:  Used when there is one categorical independent variable with two or more levels and one continuous dependent variable.
• Two-way ANOVA:  Used when there are two categorical independent variables and one continuous dependent variable.
• Repeated measures ANOVA:  Used when measurements are taken on the same subjects at different time points or under different conditions.

ANOVA is a powerful tool for comparing means across multiple groups and identifying significant differences that may exist between them.

Machine Learning for Data Analysis

Machine learning is a powerful subset of artificial intelligence that focuses on developing algorithms capable of learning from data to make predictions or decisions.

Introduction to Machine Learning

Machine learning algorithms learn from historical data to identify patterns and make predictions or decisions without being explicitly programmed. The process involves training a model on labeled data (supervised learning) or unlabeled data (unsupervised learning) to learn the underlying patterns and relationships.

Key components of machine learning include:

• Features:  The input variables or attributes used to train the model.
• Labels:  The output variable that the model aims to predict in supervised learning.
• Training data:  The dataset used to train the model.
• Testing data:  The dataset used to evaluate the performance of the trained model.

Supervised Learning Techniques

Supervised learning involves training a model on labeled data, where the input features are paired with corresponding output labels. The goal is to learn a mapping from input features to output labels, enabling the model to make predictions on new, unseen data.

Supervised learning techniques include:

• Regression:  Used to predict a continuous target variable. Examples include linear regression for predicting house prices and logistic regression for binary classification tasks.
• Classification:  Used to predict a categorical target variable. Examples include decision trees, support vector machines, and neural networks.

Supervised learning is widely used in various domains, including finance, healthcare, and marketing, for tasks such as predicting customer churn, detecting fraudulent transactions, and diagnosing diseases.

Unsupervised Learning Techniques

Unsupervised learning involves training a model on unlabeled data, where the algorithm tries to learn the underlying structure or patterns in the data without explicit guidance.

Unsupervised learning techniques include:

• Clustering:  Grouping similar data points together based on their features. Examples include k-means clustering and hierarchical clustering.
• Dimensionality reduction:  Reducing the number of features in the dataset while preserving its essential information. Examples include principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE).

Unsupervised learning is used for tasks such as customer segmentation, anomaly detection, and data visualization.

Model Evaluation and Selection

Once a machine learning model has been trained, it's essential to evaluate its performance and select the best-performing model for deployment.

• Cross-validation:  Dividing the dataset into multiple subsets and training the model on different combinations of training and validation sets to assess its generalization performance.
• Performance metrics:  Using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve to evaluate the model's performance on the validation set.
• Hyperparameter tuning:  Adjusting the hyperparameters of the model, such as learning rate, regularization strength, and number of hidden layers, to optimize its performance.

Model evaluation and selection are critical steps in the machine learning pipeline to ensure that the deployed model performs well on new, unseen data.

Advanced Data Analysis Techniques

Advanced data analysis techniques go beyond traditional statistical methods and machine learning algorithms to uncover deeper insights from complex datasets.

Time Series Analysis

Time series analysis is a method for analyzing data collected at regular time intervals. It involves identifying patterns, trends, and seasonal variations in the data to make forecasts or predictions about future values. Time series analysis is commonly used in fields such as finance, economics, and meteorology for tasks such as forecasting stock prices, predicting sales, and analyzing weather patterns.

Key components of time series analysis include:

• Trend analysis:  Identifying long-term trends or patterns in the data, such as upward or downward movements over time.
• Seasonality analysis:  Identifying recurring patterns or cycles that occur at fixed intervals, such as daily, weekly, or monthly seasonality.
• Forecasting:  Using historical data to make predictions about future values of the time series.

Time series analysis techniques include:

• Autoregressive integrated moving average (ARIMA) models.
• Exponential smoothing methods.
• Seasonal decomposition of time series (STL).

Predictive Modeling

Predictive modeling involves using historical data to build a model that can make predictions about future events or outcomes. It is widely used in various industries for customer churn prediction, demand forecasting, and risk assessment. This involves involves:

• Data preparation:  Cleaning and preprocessing the data to ensure its quality and reliability.
• Feature selection:  Identifying the most relevant features or variables contributing to the predictive task.
• Model selection:  Choosing an appropriate machine learning algorithm or statistical technique to build the predictive model.
• Model training:  Training the model on historical data to learn the underlying patterns and relationships.
• Model evaluation:  Assessing the performance of the model on a separate validation dataset using appropriate metrics such as accuracy, precision, recall, and F1-score.

Common predictive modeling techniques include linear regression, decision trees, random forests, gradient boosting, and neural networks.

Text Mining and Sentiment Analysis

Text mining, also known as text analytics, involves extracting insights from unstructured text data. It encompasses techniques for processing, analyzing, and interpreting textual data to uncover patterns, trends, and sentiments. Text mining is used in various applications, including social media analysis, customer feedback analysis, and document classification.

Key components of text mining and sentiment analysis include:

• Text preprocessing:  Cleaning and transforming raw text data into a structured format suitable for analysis, including tasks such as tokenization, stemming, and lemmatization.
• Sentiment analysis:  Determining the sentiment or opinion expressed in text data, such as positive, negative, or neutral sentiment.
• Topic modeling:  Identifying the underlying themes or topics present in a collection of documents using techniques such as latent Dirichlet allocation (LDA).
• Named entity recognition:  Identifying and categorizing entities mentioned in text data, such as names of people, organizations, or locations.

Text mining and sentiment analysis techniques enable organizations to gain valuable insights from textual data sources and make data-driven decisions.

Network Analysis

Network analysis, also known as graph analysis, involves studying the structure and interactions of complex networks or graphs. It is used to analyze relationships and dependencies between entities in various domains, including social networks, biological networks, and transportation networks.

Key concepts in network analysis include:

• Nodes:  Represent entities or objects in the network, such as people, websites, or genes.
• Edges:  Represent relationships or connections between nodes, such as friendships, hyperlinks, or interactions.
• Centrality measures:  Quantify the importance or influence of nodes within the network, such as degree centrality, betweenness centrality, and eigenvector centrality.
• Community detection:  Identify groups or communities of nodes that are densely connected within themselves but sparsely connected to nodes in other communities.

Network analysis techniques enable researchers and analysts to uncover hidden patterns, identify key influencers, and understand the underlying structure of complex systems.

Data Analysis Software and Tools

Effective data analysis relies on the use of appropriate tools and software to process, analyze, and visualize data.

What Are Data Analysis Tools?

Data analysis tools encompass a wide range of software applications and platforms designed to assist in the process of exploring, transforming, and interpreting data. These tools provide features for data manipulation, statistical analysis, visualization, and more. Depending on the analysis requirements and user preferences, different tools may be chosen for specific tasks.

Popular Data Analysis Tools

Several software packages are widely used in data analysis due to their versatility, functionality, and community support. Some of the most popular data analysis software include:

• Python:  A versatile programming language with a rich ecosystem of libraries and frameworks for data analysis, including NumPy, pandas, Matplotlib, and scikit-learn.
• R:  A programming language and environment specifically designed for statistical computing and graphics, featuring a vast collection of packages for data analysis, such as ggplot2, dplyr, and caret.
• Excel:  A spreadsheet application that offers basic data analysis capabilities, including formulas, pivot tables, and charts. Excel is widely used for simple data analysis tasks and visualization.

These software packages cater to different user needs and skill levels, providing options for beginners and advanced users alike.

Data Collection Tools

Data collection tools are software applications or platforms that gather data from various sources, including surveys, forms, databases, and APIs. These tools provide features for designing data collection instruments, distributing surveys, and collecting responses.

Examples of data collection tools include:

• Google Forms:  A free online tool for creating surveys and forms, collecting responses, and analyzing the results.
• Appinio :  A real-time market research platform that simplifies data collection and analysis. With Appinio, businesses can easily create surveys, gather responses, and gain valuable insights to drive decision-making.

Data collection tools streamline the process of gathering and analyzing data, ensuring accuracy, consistency, and efficiency. Appinio stands out as a powerful tool for businesses seeking rapid and comprehensive data collection, empowering them to make informed decisions with ease.

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Data Visualization Tools

Data visualization tools enable users to create visual representations of data, such as charts, graphs, and maps, to communicate insights effectively. These tools provide features for creating interactive and dynamic visualizations that enhance understanding and facilitate decision-making.

Examples of data visualization tools include Power BI, a business analytics tool from Microsoft that enables users to visualize and analyze data from various sources, create interactive reports and dashboards, and share insights with stakeholders.

Data visualization tools play a crucial role in exploring and presenting data in a meaningful and visually appealing manner.

Data Management Platforms

Data management platforms (DMPs) are software solutions designed to centralize and manage data from various sources, including customer data, transaction data, and marketing data. These platforms provide features for data integration, cleansing, transformation, and storage, allowing organizations to maintain a single source of truth for their data.

Data management platforms help organizations streamline their data operations, improve data quality, and derive actionable insights from their data assets.

Data Analysis Best Practices

Effective data analysis requires adherence to best practices to ensure the accuracy, reliability, and validity of the results.

• Define Clear Objectives:  Clearly define the objectives and goals of your data analysis project to guide your efforts and ensure alignment with the desired outcomes.
• Understand the Data:  Thoroughly understand the characteristics and limitations of your data, including its sources, quality, structure, and any potential biases or anomalies.
• Preprocess Data:  Clean and preprocess the data to handle missing values, outliers, and inconsistencies, ensuring that the data is suitable for analysis.
• Use Appropriate Tools:  Select and use appropriate tools and software for data analysis, considering factors such as the complexity of the data, the analysis objectives, and the skills of the analysts.
• Document the Process:  Document the data analysis process, including data preprocessing steps, analysis techniques, assumptions, and decisions made, to ensure reproducibility and transparency.
• Validate Results:  Validate the results of your analysis using appropriate techniques such as cross-validation, sensitivity analysis, and hypothesis testing to ensure their accuracy and reliability.
• Visualize Data:  Use data visualization techniques to represent your findings visually, making complex patterns and relationships easier to understand and communicate to stakeholders.
• Iterate and Refine:  Iterate on your analysis process, incorporating feedback and refining your approach as needed to improve the quality and effectiveness of your analysis.
• Consider Ethical Implications:  Consider the ethical implications of your data analysis, including issues such as privacy, fairness, and bias, and take appropriate measures to mitigate any potential risks.
• Collaborate and Communicate:  Foster collaboration and communication among team members and stakeholders throughout the data analysis process to ensure alignment, shared understanding, and effective decision-making.

By following these best practices, you can enhance the rigor, reliability, and impact of your data analysis efforts, leading to more informed decision-making and actionable insights.

Data analysis is a powerful tool that empowers individuals and organizations to make sense of the vast amounts of data available to them. By applying various techniques and tools, data analysis allows us to uncover valuable insights, identify patterns, and make informed decisions across diverse fields such as business, science, healthcare, and government. From understanding customer behavior to predicting future trends, data analysis applications are virtually limitless. However, successful data analysis requires more than just technical skills—it also requires critical thinking, creativity, and a commitment to ethical practices. As we navigate the complexities of our data-rich world, it's essential to approach data analysis with curiosity, integrity, and a willingness to learn and adapt. By embracing best practices, collaborating with others, and continuously refining our approaches, we can harness the full potential of data analysis to drive innovation, solve complex problems, and create positive change in the world around us. So, whether you're just starting your journey in data analysis or looking to deepen your expertise, remember that the power of data lies not only in its quantity but also in our ability to analyze, interpret, and use it wisely.

How to Conduct Data Analysis in Minutes?

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• From questions to insights in minutes:  With Appinio, get answers to your burning questions in record time, enabling you to act swiftly on emerging trends and consumer preferences.
• No research PhD required:  Our platform is designed to be user-friendly and intuitive, ensuring that anyone, regardless of their research background, can navigate it effortlessly and extract valuable insights.
• Rapid data collection:  With an average field time of less than 23 minutes for 1,000 respondents, Appinio enables you to gather comprehensive data from a diverse range of target groups spanning over 90 countries. Plus, it offers over 1,200 characteristics to define your target audience, ensuring precise and actionable insights tailored to your needs.

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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199 Comments

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I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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More From Forbes

Experts explain how to select and manage data for effective analysis.

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Data drives smart decision-making in modern industries, but the old saying still holds true: “Garbage in, garbage out.” The quality and completeness of the data pulled for analysis play a huge role in the accuracy and effectiveness of the results.

Whether you’re accessing data from internal sources such as sensors, customer transactions or marketing campaigns or purchasing data from one or more vendors, due diligence is essential to ensure your findings aren’t skewed or incomplete. Below, 20 members of Forbes Technology Council share important steps to take when analyzing and selecting data sources. Follow their tips to empower analysis that’s enlightening, not misleading.

1. Seek Out Comprehensive Data

First, selecting relevant and critical data sources is important; if you include only one geographical area for global customers, the results will be biased. Second, ensure the quality of the data: Are there any duplicates, is the data standardized or are there missing attributes (and so on)? Third, enrich your data with third-party data (such as point of interest data and demographics) for better insights. - Tendu Yogurtcu , Precisely

2. Measure Against A Known Good Dataset

Starting with quality data sources may seem obvious, but the challenge is identifying the highest-quality solution. A company often assumes that its current data source is the standard to measure against, but it can be troublesome if you are not 100% sure of its quality. Start with a truth set of known good data and measure against that first. - David Finkelstein , BDEX

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Ufc 300 results bonus winners after historic event, meet the country about to have three solar eclipses in three years, 3. ensure the dataset is fully representative.

When selecting data for analysis, it’s important that the dataset be fully representative of the system being measured and evaluated. The dataset should not be skewed by the manner in which it is collected—for example, we can’t track device failures only for those devices that log a problem. Otherwise, the analysis may not provide an accurate picture of how the system is behaving. - William Bain , ScaleOut Software, Inc.

4. Take A ‘Decision Back’ Approach

Focusing on data and analytics with a value-first drive is critical. To do this, a company must start with its business problem(s), not the data, and take a “decision back” approach to achieve a greater impact on the business. Value comes first, and data comes second. - Deepak Jose , Mars

5. Augment Primary Data As Needed

In a manufacturing environment, big data is only of value if one can extract insights that guide decision-making. To do this, aspects including data quality, accessibility, potential bias and coverage of the behavior or operational states of a plant or asset should be considered. Augmenting sensor data with other sources, such as fault reports or physics simulations, can strengthen and balance observations for model-building and improve insights. - Heiko Claussen , Aspen Technology, Inc .

6. Request Data Tests From Multiple Vendors

Being the leader of a big data company, I know the uncertainties of purchasing data—you never truly know what you’re getting. The most effective method I have found to validate data is to request a specific, quick-turnaround data test from three or four vendors. That’s the best trick in the book to prevent data hacks or manipulation and accurately assess the true quality of the data. - Ariel Katz , H1

7. Ensure The Data Relates To The Problem Or Question You’re Addressing

One key consideration when selecting data sources for analysis is the relevance and reliability of the data. It’s essential to ensure that the data being utilized is directly related to the problem or question being addressed and that it’s accurate and trustworthy. The choice of data source can impact the quality of insights derived in several ways. - Tina Chakrabarty , Sanofi Pharmaceutical

8. Trace The Data’s Origins

If your source is traceable, it typically means you can get reliable information on where it comes from, whether it respects relevant intellectual property and privacy considerations, what quality assessments have been performed on it, and whether it is suitably representative of the population to which your use case applies. - Shameek Kundu , TruEra

9. Balance Data Criticality With Ease Of Integration

The truth is in the data, and if the data is not trustworthy, the truth derived from it is impaired. The key consideration is balancing the criticality of the data source with the ease of integration with standard, out-of-the-box adapters. This balance can identify the low-hanging fruit that can enable you to prioritize and make fast progress. - Manoj Gujarathi , Dematic

10. Give AI Enough Data To Find Meaningful Patterns

It’s important to make sure that your data is wide-ranging enough for artificial intelligence to find meaningful patterns in it. If you limit yourself to a narrow slice of data that fits in with your preconceived hypothesis of what is happening, you run the risk of missing out on key insights that you have not considered—insights that AI can find if you consider enough data. - Michael Amori , Virtualitics

11. Include Sources That Rank Highly In Terms Of Suitability, Quality And Compliance

In data analysis, the output is only as good as the input. To get meaningful and actionable insights, include sources that rank highly for suitability, quality and compliance. Ensure sources fit the intended purpose of analysis, and qualify them based on their accuracy, completeness and reliability. Top it off with a compliance lens to safeguard the confidentiality of data protected by privacy laws. - Anupriya Ramraj , PriceWaterhouse Coopers

12. Make Sure The Data Is Truly ‘Available’

I’ve participated in countless projects where we planned to extract value by combining data from different sources, but after looking deeper, it turned out the system vendor that held the data did not provide an API or exports to make the data easily accessible. Pro tip: Check true availability early. It will save you a lot of time. - Erik Aasberg , eSmart Systems

13. Keep Information Content, Accuracy And Timeliness In Mind

The most important thing about data source selection is to think about the total information content of your dataset, as well as the accuracy and timeliness of the data source. You will get inaccurate insights if your AI system does not correct for variations automatically or if you don’t do some data preparation outside the system. - Gaurav Banga , Balbix

14. Avoid Overcollecting Or Hoarding Data

A common misconception about data analysis is that the more data you have access to, the better your analysis. It is quite the opposite. You need to be specific in terms of what you are looking to accomplish and review. This is why data-cleansing initiatives are often needed today—too much data is being collected. - Anna Frazzetto , Airswift

15. Seek To Fully Hear Your Customers

It is imperative that organizations understand the voice of the customer. Companies must get real-time insights from user feedback, from reviews to requests for tech support. Data sources should include app reviews, social media, support tickets, surveys and more. The best sources are where your users are active. This will provide a roadmap to improvements in product quality. - Christian Wiklund , unitQ

16. Comply With Ethical Standards And Regulations

Ethical considerations regarding privacy, anonymization and consent must be considered when selecting data sources. Comply with ethical standards and regulations, such as the GDPR, the CCPA, HIPAA and similar regulations. The quality of the insights derived will depend on several other factors as well, but adhering to the law will ensure you do not encounter trouble later on. - Nitesh Sinha , Sacumen

17. Clean And Normalize The Data

Data analysis is most effective when the data is clean and normalized to a standard taxonomy. In addition, you should consider the outcomes you are trying to produce or the questions you are trying to answer and input the data sources necessary to achieve the results you’re looking for. If the quality of the data is low, the quality of the results will be low (garbage in, garbage out). - James Carder , Eptura

18. Pay Attention To The Data’s Life Cycle

Within most organizations, data has a life cycle, and its importance and relevance to an analysis process is typically determined by that life cycle. Also, most organizations have multiple copies of their data and must use data intelligence tools to ensure they are leveraging the proper version of their data in the analysis process. - Russ Kennedy , Nasuni

19. Answer These Three Questions

The key consideration when selecting data sources is to find a fit between the desired business outcome and the data source so that it generates ROI. This can be accomplished by answering three questions. 1. What is the expectation and use case on the business side? 2. How consistent and predictable is the structure and frequency of the data? 3. How much human context needs to be added to the raw data? - Akash Mukherjee , Chartmetric

20. Hunt For The ‘Unsexy’ Data That Drives Real Value

Very often, the most impactful data isn’t flashy or readily available. It might be buried within your systems, require cleaning and transformation, or come from sources your competitors are ignoring. Remember, the quality of the data is directly connected to the value of the insights you can generate. - Adrian Dunkley , StarApple AI

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Seminar: Huiyan Sang Explores GS-BART Method for Data Analysis

During the seminar, Sang showcased GS-BART's performance compared to traditional ensemble tree models and Gaussian process models. The method demonstrated efficacy across various regression and classification tasks tailored for spatial and network data analysis. Sang, a distinguished professor at Texas A&M University, has extensive expertise in statistics, with interdisciplinary research spanning environmental sciences, geosciences, economics, and biomedical research.

Attendees gained valuable insights into cutting-edge statistical methodologies, witnessing the potential of GS-BART to advance data analysis in spatial and network contexts. Sang's presentation underscored the importance of innovative approaches to tackle the complexities of modern datasets, offering a glimpse into the future of statistical research and application.

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Title: a unified combination framework for dependent tests with applications to microbiome association studies.

Abstract: We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating $p$-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing $p$-value combination methods, including the vanilla Cauchy combination method, the proposed combination framework can handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to Microbiome Association Studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, %resulting in a significant enhancement of testing power across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations.

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• Open access
• Published: 15 April 2024

Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury

• C. M. Whitfield-Cargile 1 ,
• H. C. Chung 2 , 5 ,
• M. C. Coleman 1 ,
• N. D. Cohen 1 ,
• A. M. Chamoun-Emanuelli 1 ,
• I. Ivanov 3 ,
• J. S. Goldsby 4 ,
• L. A. Davidson 4 ,
• I. Gaynanova 2 ,
• Y. Ni 2 &
• R. S. Chapkin 4  

Microbiome volume  12 , Article number:  74 ( 2024 ) Cite this article

Metrics details

The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions.

Twenty horses were randomly assigned to 1 of 2 groups ( n  = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches.

Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa.

Conclusions

Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology.

Video Abstract

The mammalian gastrointestinal (GI) tract is a complex system both anatomically and physiologically that is further complicated by the vast collection of microorganisms inhabiting it. It is well appreciated that combinatory biology of the host (GI) tract and microbiota play an essential role in the digestion of nutrients and production of energy [ 1 , 2 ]. Importantly, changes in the microbiota have been linked to a diverse array of both GI and non-GI health conditions in people and animals and there are several reviews that describe these associations [ 3 , 4 ]. In human and veterinary medicine, the decreasing cost and increasing availability of culture-independent approaches (i.e., next generation sequencing) to study the microbiome have resulted in a wealth of descriptive studies examining the microbiota in the context of health and disease. These studies augment our understanding of how the composition of the microbiota can be altered in various disease states. Studies linking these microbial changes to host function are uncommon, however, because they are challenging to conduct. Without the combination of both microbial and host data, it remains unclear whether microbiomic changes are the cause or effect of a disease, limiting the utility of the information. Thus, the ability to sequentially interrogate changes in both the host and microbiome is needed to unravel the complex interplay between the host and the microbiome.

While non-invasive coprological approaches have been widely used to capture information regarding the microbial niche, there is a paucity of non-invasive approaches to capture similar information regarding host function. One such approach is the use of exfoliomics. This platform has been utilized in rodents [ 5 ], pigs [ 6 ], adult humans [ 7 ], and human neonates [ 8 ]. We recently also validated this approach in horses [ 9 ]. Non-steroidal anti-inflammatory drug (NSAID)-induced intestinal injury (i.e., enteropathy) is a clinical syndrome widely recognized in human medicine with a similar disease in animals albeit different anatomic sites affected depending on the animal species [ 5 , 10 , 11 ]. NSAID-induced intestinal injury of mice, rats, and pigs has been used as a model system for studying inflammatory bowel disease (IBD) in people [ 12 , 13 , 14 ]. Both the clinical syndrome and the model are characterized by microbiota changes, neutrophilic intestinal inflammation, and gross intestinal lesions ranging from subclinical evidence of mucosal injury to potentially fatal intestinal bleeding and perforations [ 15 , 16 ]. We have developed an equine model of NSAID-induced intestinal inflammation [ 17 , 18 , 19 ], which mirrors the microbiota changes and damage to both the upper and lower GI tract observed with NSAID-induced intestinal injury in both clinical cases and other animal models. Moreover, the inducible, mild, predictable, and reversible nature of this model make it an attractive platform to examine the intersection of host and microbial function in the context of GI intestinal injury. Importantly, the severity of injury is mild. Therefore, changes in the microbiome and host gene expression are not masked by the overwhelming inflammatory cascade that accompanies more severe injury. In addition, use of this model has potential clinical benefits for horses as any information gained about host and microbiota interactions could be leveraged to develop preventative or treatment strategies for GI diseases of horses. This is important because GI diseases, including colic and colitis, are of considerable importance to horses and the horse industry, second only to old age as a cause of death [ 20 ]. Further, gaining information about the equine GI tract is challenging from a clinical perspective due to the immense size of the horse, which precludes the use of advanced imaging modalities and the acquisition of diagnostic endoscopic biopsies in many cases. Thus, use of the equine model of NSAID-induced intestinal inflammation not only enables a novel platform for understanding host-microbiota interactions in the context of GI disease across species but also can aid in the identification of mechanisms for preventing GI disease and NSAID-induced injury in horses.

Another major limitation that has hampered understanding of host-microbiota interactions is the challenging computational analysis of large omic datasets. High-dimensional data are inherently noisy, and this becomes even more problematic when the sample size is small [ 21 ]. Here, we attempted to overcome this challenge by application of multiple computational approaches to select features that were commonly identified by all approaches. Taken together, our model and computational analysis provides a potential platform for elucidating host-microbiota interactions by identifying initiating events of injury in a robust and accurate manner. Our objectives were to first characterize changes in the host gene expression profiles, fecal microbiota, and fecal metabolome changes in an equine model of intestinal inflammation and then to apply multiple methods of computational data integration to examine host-microbiota interactions in the context of GI inflammation.

Study design

The protocol for this study was approved by the university Institutional Animal Care and Use Committee (IACUC 2018–003). The equine model of NSAID-induced intestinal injury was performed, as previously described [ 17 , 18 , 19 ]. Briefly, twenty healthy adult horses from the university herd were utilized for this study. Pairs of horses were matched based on breed, age (± 2 years), weight (± 45 kg), and sex. One horse from each pair was randomly assigned to either the control group or the NSAID group. The two groups were housed in separate but neighboring pastures. There was a 75-day acclimation period prior to the 9-day model (Supp. Figure  1 ). During both the acclimation period and the treatment period, all horses were managed identically. Horses were confined to sand dry lots only and thus no grazing occurred. The diet consisted of free-choice coastal Bermuda hay all from the same cutting and free choice water. On day 0, baseline feces and blood was collected and gastroscopy was performed (see below). Beginning on day 1, the NSAID phenylbutazone a was administered [4.4 mg/kg orally q24 hours] to the NSAID group and horses assigned to the control group were given an equivalent volume of placebo (base of phenylbutazone paste). These treatments were administered for 9 days. On day 10, fecal and blood samples were collected and gastroscopy repeated. A physical examination was performed on all horses each day during the 9 days of phenylbutazone administration. Rectal temperature, heart rate, and respiratory rate were recorded. The dosage of phenylbutazone was chosen based on label directions as this dosage is frequently used to manage common inflammatory conditions in horses (e.g., osteoarthritis) [ 22 , 23 , 24 ].

Gastroscopy, fecal collection, and blood collection

Fecal samples were collected by rectal palpation using one rectal sleeve per animal on days 0 and 10. Feces were collected in a sterile container, immediately placed on dry ice, and transferred to a − 80 °C freezer for long-term storage. For exfoliomic analysis, an additional 1 g of feces was homogenized in 20 mL of RNA Shield® (Zymo Research, Irvine, CA, USA) and stored at − 80 °C until processed (see below). Whole blood (10 mL) was collected on days 0 and 10 from an aseptically prepared jugular vein. Blood was collected in a serum separator tube (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) and processed within 60 min. Serum was collected after centrifugation (1000 RCF, 10 min, 20 °C) and stored at − 80 °C until utilized for an ELISA (see below).

Gastroscopy was performed on days 0 and 10 as previously described [ 18 ]. Briefly, each horse was held off feed for 18 h and water for 3 h before gastroscopy. Horses were sedated using xylazine hydrochloride (0.4 mg/kg IV) and a 3-m endoscope was passed into the stomach. The entire stomach was examined, including the pylorus, and assigned a score by a single observer board certified in large animal internal medicine and blinded to treatment group. Squamous scoring was based on a previously published scoring system: 0 = intact normal mucosa; 1 = intact mucosa with reddening, hyperkeratosis, or both; 2 = small single or small multifocal ulcers; 3 = large single or large multifocal ulcers; and 4 = extensive (often coalescing) ulcers with areas of deep ulceration [ 25 ]. Glandular ulcers were scored using the same criteria as described for squamous ulcers (without consideration of lesion depth).

Tumor necrosis factor ELISA

Tumor necrosis factor (TNF) was quantified from serum on days 0 and 10 using a commercially available kit (R&D Systems, Minneapolis, MN, USA), according to manufacturer’s protocol.

Global non-targeted mass spectrometry metabolomics analysis was performed at Metabolon, Inc (Metabolon, Inc, Durham, NC), a commercial supplier of metabolic analysis, which has developed a platform that integrates chemical analysis (including identification and relative quantification) and quality assurance. To maximize compound detection and accuracy, 3 separate analytical methods were utilized including ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-LC–MS) in both positive and negative ion modes and gas chromatography/mass spectrometry (GC–MS) [ 26 , 27 ]. Targeted analysis and quantification of eight short chain fatty acids (SCFA) was determined with LC–MS/MS.

Sample preparation was performed by the automated Mircolab STAR system (Hamilton Company, Salt Lake City, UT, USA). To remove, dissociate small molecules, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (GenoGrinder 2000, Glen Mills, Clifton, NJ, USA) followed by centrifugation. The resulting extract was placed briefly on a TurboVap® (Zymark Corporation, Hopkinton, MA, USA) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis. Bioinformatics for metabolite data consisted of 4 components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization. These analyses were all performed on the LAN backbone, and a database server running Oracle 10.2.0.1 Enterprise Edition. Prior to analysis, values were normalized in terms of raw area counts and the rescaled to set the median equal to 1.

DNA extraction, 16S rRNA gene PCR, and sequencing were performed, as previously described in a separate publication [ 18 ]. Briefly, 200 mg of feces was chipped from the frozen fecal sample and genomic DNA was isolated using a commercially available fecal DNA isolation kit (QIAamp® Fast DNA Stool Mini Kit, Qiagen, Germantown, MD, USA) according to manufacturer’s protocol with slight modification. The modifications included a bead beating step with 50 mg each of sterile DNAase-free 0.1- and 0.5-mm silica zirconium beads for 90 s at 6 m/s using a Bead Mill Homogenizer (VWR, Radnor, PA, USA). The sample then was heated at 70 °C for 10 min. The remainder of the protocol was performed according to manufacturer’s protocol.

Amplification and sequencing of the V3-V4 variable region of the16S rRNA gene was performed commercially (Zymo Research, Irvine, CA, USA). Briefly, a library was prepared using a commercially available 16S rRNA prep kit (Quick-16S NGS Prep Kit, Zymo Research, Irvine, CA, USA), samples were barcoded, and PCR primers for the V3-V4 hypervariable region of the 16S rRNA gene were used. Sequencing was performed on a MiSeq (Illumina, San Diego, CA, USA) following the manufacturer’s guidelines. The software Quantitative Insights Into Microbial Ecology (QIIME2—ver 2019.1) ( https://qiime2.org ), dada2 (ver 1.6), and phyloseq (ver 1.28.0) were used for data processing and analysis [ 28 , 29 , 30 ]. Sequences were quality filtered and assigned to amplicon sequence variant (ASV) using dada2. Qiime2 was used to assign taxonomy to these ASVs against the Greengenes database (ver. gg_13_8) filtered at 97% identity for 16S rRNA gene sequences. Count tables with assigned taxonomy and phylogenetic trees constructed in QIIME2 were exported to R (ver. 3.6.1). Phyloseq was used to collapse ASV tables to the genera level. Any genera that were present in 5 or fewer samples were removed. ASV genus level count tables were then exported for further analysis.

The global gastrointestinal transcriptome was assessed using exfoliomics from day 10 samples only. PolyA + RNA was isolated from fecal samples, as previously described [ 9 ]. Briefly, RNA was extracted using a commercially available kit (Active Motif, Carlsbad, CA, USA), quantified (Nanodrop spectrophotometer; Thermo Fisher Scientific, Waltham, MA, USA), and quality assessed (Bioanalyzer 2100; Agilent Technologies, Santa Clara, CA, USA). Each sample was processed with the NuGen Ovation 3′-DGE kit (San Carlos, CA, USA) to convert RNA into cDNA. Following cDNA fragment repair and purification, Illumina adaptors were ligated onto fragment ends and amplified to create the final library. Libraries were quantified using the NEBNext Library Quant kit for Illumina (NEB, Ipswich, MA, USA) and run on an Agilent DNA High Sensitivity Chip to confirm sizing and the exclusion of adapter dimers. Sequencing data were demultiplexed and assessed for quality using FastQC. Reads were aligned using Spliced Transcripts Alignment to a reference software with default parameters and referenced against the genome of the horse (EquCab 3.0) [ 31 ]. The resulting count table was used for subsequent statistical analysis.

Data analysis

Several statistical models were used to identify variables to discriminate the control and NSAID groups in each dataset. Initially, the discriminatory power of each variable was evaluated with Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis (MV-SIS) [ 32 ]. MV-SIS measures individual variables’ ability to discriminate between the control and NSAID groups and produces a measurement that represents the discriminatory power of each variable. Subsequently, Multi-Group Sparse Discriminant Analysis (MGSDA) was performed [ 33 ]. This procedure jointly identifies discriminatory variables and estimates a subspace that separates the two groups based on identified variables. Unlike the marginal selection of MV-SIS, MGSDA accounts for the correlation structure of variables and selects only a subset of variables when informative variables are highly correlated. Lastly, we utilized Joint Association and Classification Analysis of multi-view data (JACA) to integrate data and classify [ 34 ]. JACA simultaneously identifies discriminative variables from the three data sets (microbiome, exfoliome, and metabolome). The selected variables from each data set provide coherent information to the model in that the signals corresponding to selected variables have high correlation across data sets.

To avoid data overfitting resulting in biased variable selection, we identified a set of informative variables based on out-of-sample prediction accuracy using Leave-One-Out Cross-Validation (LOO-CV). Specifically, for MGSDA and JACA, we fitted a model leaving one observation out and predicted the class (control or NSAID) of the left-out observation using the fitted model. We repeated this process for each observation and selected the set of variables that produced the smallest total number of misclassifications. The procedure was not applied to MV-SIS as it does not perform the variable selection, but rather provides a ranking for all variables in terms of their individual discriminatory power.

Graphical data presentation included principal component analysis (PCA) plots and Venn diagrams made in R (ver 4.1.3) with the R packages FactoMineR and FactoExtra and Venn. Random forest analysis was performed with the R package RandomForest. Gene pathway enrichment was determined using QIAGEN IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA ) [ 35 ] by uploading appropriate gene lists with fold changes.

Cell culture

For all in vitro assays, chemicals were obtained from Thermo Fischer Scientific (Waltham, MA, USA) unless otherwise noted. YAMC cells were kindly provided by Dr. Robert Whitehead [ 36 ]. Unless otherwise stated, YAMC cells were cultured in RPMI 1640 media containing GlutaMAX, Hepes and supplemented with 5% fetal bovine serum, ITS (Corning, Tewksbury, MA, USA), and mouse interferon gamma (Sigma-Aldrich, St. Louis, MO, USA).

p62 nuclear translocation studies

To determine the effect of NSAIDs on nuclear translocation of p62, YAMC cells were seeded in 6-well plates (7.5 × 105 cells/well) and incubated under non-permissive conditions at 37 °C/5% CO 2 . The next day, media was replaced with media containing the appropriate treatments suspended in 0.04% dimethylformamide (VWR, Radnor, PA, USA), 0.4 mM ibuprofen (Cayman Chemical Co., Ann Arbor, MI, USA), 0.4 mM phenylbutazone (Cayman Chemical Co.), 0.25 mM indomethacin (Cayman Chemical Co.), 0.5 or 0.1 mM H 2 O 2 and returned to the incubator. Twenty-four hours later, cells were washed once with Dulbecco’s phosphate-buffered saline (DPBS), detached, and transferred to a centrifuge tube. Cytosolic and nuclear fractions were collected, as previously described [ 37 ]. Briefly, samples were centrifuged at 500 × g for 10 min, washed once with 1 mL of DPBS and cell pellets were resuspended in 1440 µL of hypotonic solution (20 mM Tris–HCl (pH 7.4), 10 mM KCl, 2 mM MgCl 2 , 1 mM EGTA, 0.5 mM DTT, 0.5 mM PMSF). Samples were incubated on ice for 3 min, supplemented with NP-40 to a final concentration of 0.1%, and vortexed for 10 s vigorously prior to centrifugation at 3000 × g and 4 °C for 5 min. Supernatants containing cytosolic fractions were transferred into a clean tube and pellets were kept on ice for nuclear fraction isolation. Cytosolic containing supernatants were centrifuged at 15,000 × g and 4 °C for 3 min to remove any residual debris, transferred into a clean tube and stored at − 80 °C until western blot analysis. Pellets containing the nuclear fraction were washed once with isotonic solution supplemented with 0.3% NP-40 to remove any residual cytosolic proteins, centrifuged (3000 × g and 4 °C for 3 min) and lysed with 100 µL of radioimmunoprecipitation assay buffer (RIPA; 150 mM sodium chloride, 1.0% NP-40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, 50 mM Tris, pH 8.0) supplemented with protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA). Samples were stored at − 80 °C until western blot analysis. The day of analysis, nuclear fractions stored in RIPA buffer were centrifuged at 3000 × g and 4° for 3 min before measuring protein concentration. Protein concentration was measured from the cytosolic and nuclear fractions using the bicinchoninic acid assay.

Western blot analysis

Protein lysates (12.5–50 µg for cytosolic fractions and 10–15 µg for nuclear fractions) in 1X SDS/β-mercaptoethanol buffer were resolved on a 4–20% TGS stain free gel (BioRad, Hercules, CA, USA) and electrotransferred onto a polyvinylidene difluoride transfer membrane. Western blot analysis was performed using mouse anti-lamin A/C (1:2000; Cell Signaling Technology #4777, Danvers, MA, USA), rabbit anti-SQSTM1/p62 (1:1000; Cell Signaling Technology #5114) or rabbit anti-GAPDH (1:1000; Cell Signaling Technology #5174) and horseradish peroxidase-conjugated goat anti-rabbit (1:2000; Cell Signaling Technology #7074) or goat anti-mouse (1:5000, Abcam #ab6789, Waltham, MA, USA) antibodies. Protein bands were visualized by chemiluminescence using a ChemiDocTouch Imaging System (BioRad, Hercules, CA, USA). Bands were quantified using the ImageLab software version 5.2.1.

YAMC cells were seeded in 2-well chamber cover glass (1.5 × 10 5 cells/well) and incubated under non-permissive conditions (37 °C, 5% CO 2 ). Thirty-six hours post seeding, media was replaced with the appropriate drug and cells were returned to the incubator. One-hour post drug addition, the reactive oxygen species (ROS) indicator (CM-H 2 DCFDA) was added to the wells at a final concentration of 2 ng/µL. Cells were returned to the incubator. One-hour post addition of ROS indicator, cells were washed once with media before imaging with a confocal laser scanning microscope (Olympus FV 3000, Shinjuku, Tokyo, JP). The ROS positive area was measured using Image J [ 38 ].

All horses completed the study but we were unable to collect one or both fecal samples from 2 horses (one from each group). Therefore, all fecal-based analyses are based on a sample size of 9 horses per group. All horses included in the study were geldings. The mean age in years ± SD for the control group and NSAID group was 14.7 ± 3.5 and 14.8 ± 3.2, respectively. Throughout the study, there was no clinical evidence of negative effects related to phenylbutazone administration, with vital parameters in all horses remaining within normal reference ranges. This is typical of the equine model of NSAID-induced intestinal injury [ 17 ]. All NSAID-treated horses in this study had prototypical evidence of subclinical intestinal injury including gastric ulcers and GI inflammation (Supp Figure 2 ).

Individual data analysis

Untargeted metabolomics was performed on fecal samples collected before and after 10 days of phenylbutazone administration for both the control and NSAID group. A total of 553 known compounds were identified (Supplemental Table 1 ). Phenylbutazone, only present in treated horses, was removed from the list of metabolites for all analyses. Principal component analysis (PCA) was performed to examine the ability of the entire fecal metabolome to separate the groups. There was overlap of all samples at day 0 but clear visual shifts of the fecal metabolic profile from day 0 to day 10, most notable in the NSAID group (Fig.  1 ). To further highlight differences in the fecal metabolome, random forest (RF) analysis was performed comparing control and NSAID groups at day 0 and day 10. RF analysis at day 0 resulted in an overall predictive accuracy of 60%, where a predictive accuracy of 50% would occur by chance alone. In contrast, following treatment, RF was 80% accurate at binning the samples.

Fecal metabolome is altered by phenylbutazone administration. PCA of fecal metabolites grouped by treatment (NSAID or control) and day (day 0 = before NSAID administration and day 10 = after NSAID administration). Ellipses represent 95% CI around the group mean points. Point size indicates quality of representation (cos 2 ) of individuals on the PCA; the larger point size reflects higher quality representation

Next, feature selection was performed with MV-SIS, which screens for important predictors for the ultrahigh dimensional discriminant analysis with a categorical response. MV-SIS examines each variable (i.e., metabolite) individually and provides a number that represents the ability of that metabolite to discriminate between groups. These analyses were performed on data that represented the difference between day 10 and day 0 for each group. The 50 most informative metabolites selected by MV-SIS and their distribution among samples are shown in Fig.  2 A. The top 300 features selected by MV-SIS were used for subsequent analyses (i.e., MGSDA and JACA). MGSDA jointly identifies discriminatory variables and, based on the variables, estimates a subspace that separates the two groups the most. Unlike the marginal selection of MV-SIS, MGSDA accounts for the correlation structure of variables and selects only a subset of variables when informative variables are highly correlated. Thus, if non-selected variables have high correlations ( R  > 0.9) with a MGSDA selected variable, then it is desired to investigate the non-selected variables because they have similar discriminatory power as the selected variable [ 33 ]. MGSDA identified the very long chain fatty acid (VLCFA) 2-hydroxynervonate as being the most informative metabolite. No additional metabolites were highly correlated with 2-hydroxynervonate. The average misclassification rate for this metabolite alone was 0.278. When MGSDA was allowed to select more variables, 4 additional metabolites were selected with an increased overall error rate (0.44) (Fig.  2 B).

Specific fecal metabolites discriminate between control and NSAID-treated horses. A Heat map showing the distribution of the difference in fecal metabolites between day 10 and day 0 among the samples for the 50 most discriminative metabolites selected by MV-SIS. Values are scaled around zero as indicated by the key. Negative numbers (purple/blue) indicate lower concentration at day 10 compared to day 0 whereas positive numbers (orange/red) indicate higher concentration at day 10 compared to day 0. MGSDA-selected metabolites are shown in red text. B Bar chart indicating the magnitude of the loadings of MGSDA-selected metabolites. The misclassification rate (MCR) is indicated by the number at the top of each bar(s)

There were 21,622 unique ASVs identified in the 16S rRNA gene sequence data. These were aggregated to the genera level for further analysis. We initially performed PCA based on relative abundance at the genus level. In order to improve visualization of the PCA, we removed genera that we present in fewer than 6 samples among the 36 samples that were available for analysis. Similar to the fecal metabolome of these horses, there was overlap of both groups at day 0 and clear visual shifts from day 0 to day 10 for both groups, although the direction of the population changes were different between the groups (Fig.  3 A). Feature screening with MV-SIS was initially performed (Fig.  4 A). MGSDA was applied to the features and the genus Sarcina was selected as the best discriminator of groups with an error rate of 0.11. Selection of the next most informative genera, Fibrobacter , Pseudobutyrivibrio , Sutterella , and Syntrophomonas increased the error rate to 0.22 (Fig.  4 B). The contribution of each bacterial genus to group separation on the PCA is demonstrated in the biplot along with the percent relative abundance of these genera (Fig.  2 A and B). Taken together, these findings highlight the importance of the bacterial genera selected by MGSDA.

Fecal metabolome is altered by phenylbutazone administration. A PCA with biplot of fecal metabolites grouped by treatment (NSAID or control) and day (day 0 = before treatment and day 10 = after treatment). Ellipses represent 95% CI around the group mean points. Point size indicates quality of representation (cos 2 ) of individuals on the PCA; the larger point size reflects higher quality representation. The 4 genera highlighted by the red box are the genera selected by subsequent analyses. B Boxplots showing the percent relative abundance of the 4 genera selected by subsequent analyses. Horizontal line represents the median, box extends from 25 to 75th percentiles, and whiskers extend to minimum and maximum values

Specific fecal bacterial genera discriminate between control and NSAID-treated horses. A Heat map showing the distribution among the samples of the 50 most discriminative bacterial genera selected by MV-SIS. Values are scaled around zero as indicated by the key. MGSDA-selected metabolites are shown in red text. B Bar chart indicating the magnitude of the loadings of MGSDA-selected metabolites. The misclassification rate (MCR) is indicated by the number at the top of each bar(s)

Gastrointestinal transcriptome

The RNA isolated from 2 samples, one from each group, was of insufficient quality to proceed with sequencing and therefore, exfoliome data from 8 horses in each group was analyzed. These 16 horses mapped to 14,092 genes out of the 30,000 genes in the EquCab 3.0 genome. PCA plots of the global transcriptome revealed nearly complete overlap of both groups; however, the NSAID group was tightly clustered whereas the control group was widely dispersed (Fig.  5 A). In addition, many of the most informative genes were downregulated in the NSAID group relative to the control group (Fig.  5 B), although there were no differences in library size between the groups nor expression of house-keeping genes (Supp. Figure  3 ). We next employed MV-SIS to screen the exfoliome data (Fig.  5 C). MGSDA selected 6 genes with a misclassification rate of 0.125, although LCORL was the most informative (Fig.  5 D). In addition, LCORL was highly correlated ( R  > 0.95) to 10 other genes that were also selected by MV-SIS (Table  1 ). Therefore, these additional genes were not selected by MGSDA because they were considered redundant due to their high correlation but possess similar discrimination ability as LCORL.

The equine exfoliome is altered by phenylbutazone administration. A PCA plot based on gene expression in the equine exfoliome after 9 days of phenylbutazone administration (NSAID) or placebo (control). Ellipses represent 95% CI around the group mean points. Point size indicates quality of representation (cos2) of individuals on the PCA; larger point size reflects higher quality representation. B Smear plot of the fold differences in exfoliome gene expression between NSAID and control horses. Red dots represent genes with greater than 2-fold difference between the groups. Yellow smear on the left of the graph represents genes with zero or very low counts in one group but not the other. C Heat map showing the distribution among the samples of the 50 most discriminative genes selected by MV-SIS. Values are scaled around zero as indicated by the key. MGSDA-selected metabolites are shown in red text. B Bar chart indicating the magnitude of the loadings of MGSDA-selected metabolites. The misclassification rate (MCR) is indicated by the number at the top of each bar(s)

Data integration and biological interpretation

Ultimately intestinal biology involves a complex interaction between the microbiota and the host, combined with their respective contributions to the intestinal metabolome. To elucidate the interaction between these sets of data (i.e., microbiota, exfoliome, and metabolome), we performed JACA. JACA jointly identifies discriminative variables from the combined three data sets [ 34 ]. This analytical platform provides coherent information to the model in that signals associated with selected variables have high correlation across data sets. Ultimately, 3 bacterial genera, 16 metabolites, and 25 host genes were selected by JACA (Fig.  6 A–C). Pairwise projections of the samples in the direction of the selected features revealed clear separation of the groups (Fig.  6 D–F). The correlation of the exfoliome and metabolome was strong (0.85) as was the correlation between exfoliome and microbiota (0.76). The correlation between the metabolome and microbiota was moderate (0.64).

JACA identifies features that correlate with features in all three datasets and discriminate between groups. JACA-selected features and the magnitude of the loadings of each feature, which indicates both ability of that feature to discriminate between the groups and its correlation to features in the other 2 datasets, for A metabolome, B exfoliome, and C microbiota. Pairwise correlation of JACA-selected features and projection of samples in the direction of JACA-selected features for D metabolome vs. microbiota, E metabolome vs. exfoliome, and F microbiota vs. exfoliome

Traditional statistical approaches that attempt to identify features that are differentially expressed or abundant provide useful information but results of these traditional analyses with ultra-high dimensional data and small sample size can generate suspicious findings due to the size and inherent noise in these types of data. Therefore, we attempted to overcome this by utilizing analytical techniques which were designed to address these specific limitations. Each analysis provided different information although there was substantial overlap of the results (Fig.  7 A–C). Since discriminating features commonly selected by multiple techniques are likely to be the most robust, we built our mechanistic hypothesis around features that were commonly selected by all analytical techniques with additional features added to this model based on 2 criteria: (1) highly correlative ( R  > 0.95) to the top MGSDA/JACA and MV-SIS features and (2) the top 1/3 of JACA-selected features based on magnitude of loadings. This feature selection approach resulted in identification of 8 metabolites, 4 bacterial genera, and 17 host genes (Table  1 ). We then explored these features to identify patterns that might be informative regarding host-microbiota interactions in our model.

Venn diagram depicting the congruency of features selected by our analytical approaches for the A metabolome, B microbiota, and C exfoliome

Interestingly, of the 8 metabolites selected, 2 were exclusively metabolized by peroxisomes, the VLCFA 2-hydroxynervonate and the phyanytic acid derivative 3-methyladipate. Based on these findings, we extracted the metabolites from our data that are routinely assayed for the clinical diagnosis of peroxisomal disorders in people including VLCFAs, branch chain fatty acids, plasmalogens, pristanic acid, and phytanic acid in order to examine this entire family of metabolites. PCA based on these 19 metabolites showed clear separation of the groups suggesting that this family of peroxisomal metabolites was altered by NSAID administration (Fig.  8 A). An additional 2 of the 8 metabolites were tryptophan metabolites. Similar to peroxisomal metabolites, the family of tryptophan metabolites also showed clear separation of the groups (Supp Figure 4 ). The bacterial genera selected by our strategy were Sarcina , Pseudobutyrivibrio , Syntrophomonas , and Fibrobacter . Of these, the known function of Pseudobutyrivibrio production of the short chain fatty acid (SCFA) butyrate suggests that loss may have important implications for intestinal health. In order to determine if loss of this genus also resulted in decreased butyrate as expected, targeted metabolomic analysis of the primary SCFAs (i.e., propionate, butyrate, and acetate) at day 10 of both groups was performed. In concordance with loss of Pseudobutyrivibrio , butyrate was decreased in the feces of NSAID-treated horses ( P  = 0.009) relative to control horses as was propionate (Fig.  8 B–D). Ultimately, 17 host genes met criteria for inclusion for biological interpretation. The top canonical pathways enriched by these 17 genes were EIF2 signaling and the protein ubiquitination pathway (Fig.  8 E).

In-depth exploration of the most informative features selected by our analytical approach. A PCA biplot based on metabolites known to be impacted by peroxisomal dysfunction grouped by treatment (control or NSAID). Ellipses represent 95% CI around the group mean points. Point size indicates quality of representation (cos2) of individuals on the PCA; larger point size reflects higher quality representation. B Fecal concentration (µg/g of feces) of the SCFA butyrate was significantly lower ( P  = 0.009, independent t -test) in NSAID-treated horses compared with control horses. C Fecal concentration (µg/g of feces) of the SCFA propionate was significantly lower ( P  = 0.002, independent t -test) in NSAID-treated horses compared with control horses. D Fecal concentration (µg/g of feces) of the SCFA acetate was not significantly different between the groups

Pseudobutyrivibrio is an obligate anaerobic bacteria [ 39 ] and therefore, is sensitive to changes in intestinal oxygenation. Oxidative injury within the intestinal mucosa is one mechanism that has been shown to alter oxygenation status and loss of commensal anaerobic bacteria. NSAIDs are known to induce oxidative injury; however, the ability of phenylbutazone to induce oxidative injury relative to other NSAIDs has not been examined. Thus, we compared NSAID-induced ROS accumulation in young adult mice colonocytes (YAMC) cells treated with phenylbutazone and 3 other commonly used NSAIDs (Fig.  9 A and B). The oxidative capability of phenylbutazone was similar to the other classes of NSAIDs examined suggesting that phenylbutazone induces similar oxidative stress as other NSAIDs. Cellular oxidative stress induces many cellular responses including endoplasmic reticulum (ER) stress [ 40 ]. Typically, the cellular response to ER stress is to increase protein degradation through induction of the ubiquitin proteome system and to decrease protein translation through the eukaryotic translation initiation factor 2 (eIF2) pathway, the top two canonical pathways enriched in this model. One cellular indication of ER stress is nuclear accumulation of p62 that occurs to increase the efficiency of the ubiquitin proteome system [ 41 ]. Interestingly, we observed nuclear accumulation of p62 in mouse colonocytes treated with various NSAIDs including phenylbutazone (Fig.  9 C). Taken together, these data suggest that, in vitro, phenylbutazone induces oxidative injury to colonocytes and that the resulting cellular response may indicate ER stress and activation of the proteasome ubiquitin system. Based on these data, we have generated a putative mechanism describing the effects on NSAIDs with respect to GI injury (Fig.  10 ).

In vitro data from mouse colonocytes suggests that NSAIDs, including phenylbutazone, induce oxidative stress and a subsequent cellular response known to occur when ER stress is induced by imbalanced redox homeostasis. A Fluorescent microscopic images of YAMC cells exposed to NSAIDs and H 2 O 2 , at the noted concentration, for 24 h prior to exposure to the ROS indicator CM-H2DCFDA. Ten images were taken from each treatment condition. B Average intensity of fluorescence for each treatment condition was significantly different that control cells (ANOVA) except the lowest concentration of H 2 O 2 . Graph represents data from 3 independent experiments. C Western blot of P62 protein from both the cytosolic (left blot) and nuclear (right blot) protein fractions of YAMC cells exposed to the NSAIDs at the indicated concentration for 24 h. Loading controls were the nuclear protein lamin A/C and the cytosolic protein GAPDH. D Graph of nuclear p62 represented as fold of DMF control from 3 independent experiments. DMF: dimethylformamide (0.04%), IB: ibuprofen (0.4 mM), PB: phenylbutazone (0.4 mM), IM: indomethacin (0.25 mM), H 2 O 2 (0.5 or 0.1 mM as indicated)

Putative mechanism describing how phenylbutazone induces injury in the GI tract. We propose that phenylbutazone induces oxidative injury to colonocytes which subsequently alters several cell signaling responses including ER stress and activation of the proteasome ubiquitin system. Further, this combination of host changes results in concommitant alteration of the microbiome, potentially due to lumenal redox imbalances. Obligate anaerobic bacteria, and their metabolites ( e.g., butyrate), are then depleted due to their sensitivity to lumenal oxygen content. Loss of the critical SCFA butyrate then exacerbates cellular injury

Non-invasively acquired data regarding the cellular and molecular function of the GI tract has broad implications in the field of gastroenterology for all animal species. Fecal microbiota data have been examined for decades in the context of GI diseases in both people and animals. While descriptive microbiota data can provide useful information, linking microbiota changes to intestinal function increases the value of such data. The major limitations of linking microbiota data and GI functional information are the difficulties of acquisition of cellular and molecular data regarding the GI tract and the challenges of computational analysis of these large data sets. We combined novel techniques (i.e., exfoliomics) and robust computational approaches to integrate host microbiota data in an equine model of NSAID-induced GI injury. Our findings recapitulating known mechanisms of NSAID-induced GI injury provide proof-of-principle for the validity of our non-invasive approach to investigate GI diseases in both humans and animals.

Of the many animal models of GI inflammation, chemically induced models are among the most common. Each of these models has advantages and disadvantages and these models have been extensively reviewed elsewhere [ 42 , 43 ]. NSAIDs have been used as a chemically induced model of GI injury [ 44 , 45 ]. NSAID-induced GI injury is an attractive model because it is a clinically relevant condition [ 46 , 47 ] and shares many pathological features with other IBDs [ 48 ]. As with IBD, microbiomic changes are a key feature of NSAID enteropathy, thus enabling use of the NSAID model for examination of host-microbiota interactions [ 49 ]. Although much of this work has been conducted in mice and rats, both clinical cases and the equine model of NSAID-induced GI injury have GI lesions similar to those in people and mice [ 17 , 18 ]. While the equine model has limitations, large animal models offer important benefits [ 50 ]. We used the model of NSAID-induced GI injury [ 17 , 18 ] in horses and our experience with equine exfoliomics [ 9 ] and microbiomics [ 19 ] to integrate host and microbiota data to gain insights into the pathogenesis of GI injury.

The most commonly accepted paradigm for lower GI injury begins with NSAID accumulation within intestinal epithelial cells (IEC). NSAIDs are weak organic acids [ 51 ] that can easily traverse the plasma membrane of IECs. Intracellularly, NSAIDs induce mitochondrial injury and subsequently oxidative stress [ 52 ]. This has been well-documented for the cyclooxygenase (COX) non-selective NSAID indomethacin, the most common NSAID utilized in animal models of GI injury. The ability of phenylbutazone to induce ROS in IECs has not been well-studied; however, our results demonstrate that phenylbutazone induces ROS accumulation. This is consistent with other studies where phenylbutazone induced ROS in other tissues [ 53 , 54 ].

Oxidative stress induces a myriad of cellular responses that are highly cell- and context-dependent. The metabolite identified by all of the analytical approaches was the VLCFA 2-hydroxynervonate which was increased in NSAID-treated horses relative to controls. Accumulation of VLCFAs is a hallmark of peroxisomal dysfunction. While inherited peroxisomal disorders have been described in humans, acquired disorders are more common. One of the few defined causes of acquired peroxisome disorders is oxidative stress which has been demonstrated in diabetes [ 55 ] and aging [ 56 ]. In our model, oxidative stress appears to be the most likely cause given our findings and the known effects of other NSAIDs on redox homeostasis [ 57 ]. Peroxisomes are critically involved in redox homeostasis, generate large amounts of cellular hydrogen peroxide [ 58 ], and can be overwhelmed during oxidative stress resulting in dysfunction [ 59 , 60 ]. While we had no means of examining peroxisome function in this study, we were able to examine other metabolites associated with peroxisomal biogenesis disorders. These included plasmalogens, synthesized in peroxisomes and terminated in the ER [ 61 ], and other VLCFAs [ 62 ]. A notable difference in this class of metabolites was observed between the groups. Others have demonstrated similar findings associated with peroxisomal dysfunction in numerous diseases including Alzheimer’s disease [ 63 ], Zellweger syndrome [ 64 ], and various types of cancers [ 65 ].

As noted, oxidative stress induces a wide array of cellular responses. The majority of ROS are generated by the mitochondria (70%), with peroxisomes providing much of the remaining 30% [ 66 ]. There is well-recognized cross-talk between peroxisomes and mitochondria in terms of fatty acid metabolism for energy and redox homeostasis among others [ 67 ]. Peroxisomes and mitochondria are in close proximity to the ER and contact the ER through mitochondrial-associated membranes. ROS generated by these organelles diffuse to the ER and induce a stress response [ 55 ], including the accumulation of misfolded proteins within the cell. ER stress initiates a highly conserved cell-signaling pathway referred to as the unfolded protein response pathway (UPR). The UPR attempts to restore protein folding capacity of the cell via a series of cell signaling transduction events with 4 goals that vary based on the severity of oxidative stress and other factors. These goals are (1) a global decrease in protein synthesis, (2) increased ER folding capacity, (3) increased degradation of misfolded proteins, or (4) cell death, if uncorrected. Clearly, these diverse and broad cellular responses involve a complex interaction of many genes, proteins, and transcription factors.

The host genes selected in our model can be grouped based on cellular function into 4 distinct, but overlapping outcomes: (1) ER to Golgi trafficking (TRIP11, LMAN1); (2) protein degradation through autophagy and ubiquitination (ERBIN, PSMD1); (3) cell cycle regulation (KMT2E); (4) transcription regulation (ZNF782, PRP4FB); and (5) EIF2 signaling (RPL15, RPL7). Each of these functions is well described events associated with the UPR and suggests that NSAIDs induce ER stress, possibly through imbalance of redox homeostasis, and induction of UPR with associated downstream functions. While these findings are known to occur with oxidative stress in general, NSAIDs have been shown to induce ER stress [ 68 ] and associated downstream effects including UPR [ 69 ]. The mechanisms by which NSAIDs induce ER stress are unclear but oxidative stress with mitochondrial dysfunction and effects of NSAIDs on cell membranes have been implicated [ 70 ].

One outcome of activation of UPR is enhanced protein degradation though proteosomal degradation and/or autophagy. Many classes of NSAIDs have been shown to impact macroautophagy although whether NSAIDs inhibit or induce macroautophagy is unclear. Interestingly, ERBIN expression is decreased in people with IBD, and ERBIN inhibits autophagy and subsequent autophagic cell death in murine models of DSS-induced colitis [ 71 ]. In our study, the expression of ERBIN was downregulated in NSAID-treated horses, consistent with other GI inflammatory diseases. p62 is a protein that is critically involved in the intersection of autophagy and proteosomal degradation. This protein binds to ubiquitinated targets resulting in autophagic degradation. p62 is also important for pexophagy, the process by which cells remove dysfunctional peroxisomes. Notably, our data demonstrate that phenylbutazone increases the amount of p62 and results in nuclear translocation of p62. This is noteworthy, as p62 is specifically required for pexophagy during oxidative stress conditions [ 72 , 73 ]. Taken together, our metabolomic and exfoliomic data highlight the role of redox homeostasis and subsequent ER stress in phenylbutazone-induced intestinal injury in horses.

While redox homeostasis is important in all cells, the impact of imbalances is pronounced in the GI tract and has been associated with many GI diseases. One reason for this pronounced effect is that, unlike other tissues in the body, the GI mucosa is hypoxic under homeostatic conditions. There are structural and physiological reasons for this, including the maintenance of an anaerobic environment in the lumen of the GI tract. The GI microbiota is a large and diverse system with the vast majority of the bacteria known to be facultative or obligate anaerobes [ 74 ]. Redox imbalances can result in increased oxygen levels within both the GI mucosa and lumen allowing for increased growth of aerobic bacteria and loss of obligate anaerobes [ 75 ]. In our study, one of the bacterial genera identified by multiple computational approaches was Pseudobutyrivibrio , which was decreased in horses after NSAID treatment. The reasons for this decreased abundance are unclear and, while redox imbalance may have played a role, that cannot be the only explanation as other obligate anaerobes increased in relative abundance in response to NSAIDs. However, loss of Pseudobutyrivibrio may have contributed to disease severity due to loss of SCFA butyrate, which is produced in large quantities by Pseudobutyrivibrio [ 76 ]. We confirmed that loss of this genus was associated with loss of the SCFA butyrate. Butyrate is an important bacterial metabolite with a broad array of impacts on GI mucosal homeostasis including potent antioxidant activity [ 77 , 78 ]. Butyrate also directly impacts peroxisome proliferation and function in IECs [ 79 ]. Therefore, phenylbutazone-induced oxidative injury combined with decreases in one of the major antioxidant metabolites (i.e., butyrate) may have exacerbated redox imbalances in a vicious cycle and further induced peroxisome injury and ER stress, ultimately leading to cell death and intestinal injury.

Sarcina was another genus of bacteria that was identified by all computational methods and was the best discriminator of the groups. This genus is found in the feces of normal healthy humans [ 80 ] and other animals including horses [ 81 ]. However, it has also been implicated in severe gastritis and gastric rupture in humans [ 82 ] and animals [ 83 ]. Whether it causes these pathologies or is simply an opportunistic pathogen remains unclear. NSAID-induced gastric ulcers are common [ 17 , 84 ] and the horses in this study developed gastric ulcers as expected. Because Sarcina is a component of the normal flora of the equine stomach [ 85 ], it is possible that the increased abundance of Sarcina in NSAID-treated horses may be due to colonization of NSAID-induced gastric ulcers.

The role of the microbiota in GI diseases is well-recognized and many descriptive studies link microbiota changes with various GI diseases. Elucidating host-microbiota interactions from a cellular and molecular perspective, however, is challenging. We attempted to address this challenge by combining host, fecal metabolomic, and fecal microbiota data. The power of our study lies in the robust computational analysis used for integrating host and microbiota data. Identifying the features that are biologically important of high-dimensional data, especially with a small sample size, is challenging. Traditional approaches that attempt classification using all features in large datasets can be little more than a guess due to the noise inherently present in high-dimensional data [ 86 ]. Our approach employed multiple analytical methods followed by focusing on features that were commonly identified among these techniques. Ultimately, this methodology identified a sparse set of features from each of our datasets (i.e., metabolites, bacterial genera, and host genes) that were available for biological interpretation. Biological interpretation of our findings recapitulates many of the known or suspected initiators of NSAID-induced intestinal injury which leads credence to this analytical approach. Importantly, the mild degree of intestinal injury induced by our model allowed us to recognize of initiating events of NSAID enteropathy. Many studies utilize models of NSAID enteropathy that induce severe injury which has provided a wealth of information about mucosal injury and subsequent inflammatory cascade. In these studies, however, the severe inflammatory reaction can mask the initiating events. Identification of pathways involved in initiating events can lead to avenues for therapeutic intervention designed to prevent NSAID enteropathy and, perhaps, other GI diseases characterized by imbalances in intestinal redox homeostasis.

The majority of changes observed in all 3 datasets can be traced to oxidative stress within the GI tract and associated metabolite, microbiota, and gene expression changes. However, other changes in these datasets merit discussion. Changes were observed in 2 of the 8 selected tryptophan-derived metabolites (viz., kynurenine and anthranilate). Tryptophan and both its mammalian- and microbiota-derived metabolites have been extensively studied in the context of immunoregulation and various types of IBDs. While some results are conflicting, multiple authors have demonstrated increased levels of fecal tryptophan and increased tryptophan metabolites in cases of active IBD [ 87 ]. This likely reflects some combination of increased tryptophan metabolism, decreased absorption, or increased loss from injured GI tissues [ 87 , 88 ]. We and others have previously examined the effects of tryptophan metabolites in a murine model of NSAID-induced intestinal injury and demonstrated interactions between NSAID-induced injury and tryptophan metabolites [ 16 ]. The congruency of these findings further supports the importance of this family of metabolites in GI inflammation.

There were several limitations to our study that should be recognized. First, the sample size for this study was small ( n  = 9 for fecal-based assays) and in some readouts (e.g., exfoliome) the sample size was further decreased by logistical issues such as poor RNA quality. Due to our small sample size, we attempted to remove as many other variables between the groups as possible. For example, 75 days of acclimation, same diet, and side-by-side housing to name a few. Despite these steps, there were still differences between the groups at the start of this study as evidenced by differences at day 0 for both the metabolome and microbiota. While we attempted to house horses as similar as possible, it is possible that housing differences, even as minimal as adjacent pens, resulted in different baseline findings. The combination of small sample size and starting with a different baseline metabolome and microbiota may have limited our ability to detect other important biological signatures. Another point that is related to group assignment is the fact that there were also changes in the control group over the study period. This suggests that NSAIDs were not the only cause some of the changes we observed. The reasons for the changes in the control group are unclear but may be related withholding feed twice within the 10 day treatment period as diet changes have been shown to alter the fecal microbiota [ 89 ].

Other limitations were related to our sample acquisition and analyses. We used 16S rRNA gene sequencing of fecal samples for microbiota analysis. There are many well-described limitations to this approach related to taxonomic resolution, lack of functional information, and inability to assign taxonomy to a large proportion of ASVs [ 90 ]. This is further compounded by the fact that we used fecal samples only. The ability of fecal samples to represent the microbiota of the proximal GI tract is questionable at best. Relatedly, we propose a mechanistic hypothesis for NSAID-induced intestinal injury in horses, but no additional readouts were performed to confirm our hypothesis. The primary reason for that is that samples from horses were acquired non-invasively and therefore no tissues were available for confirmation nor were microbiota samples representative of the proximal GI tract available. Further, host transcriptomic data was based on analysis of the equine exfoliome. We and others have used this approach in rodents [ 5 ], pigs [ 6 ], people [ 7 ], human neonates [ 8 ], and horses [ 9 ], and demonstrated that exfoliomic data mimic those in tissues but further validation of the exfoliomic methods in horses is warranted. Finally, the portion of our mechanistic hypothesis generated by evaluation of the exfoliome is based on the function of a small number of genes that are not master regulators of the pathways we identified. For example, the well-described initiators of UPR are inositol-requiring enzyme 1α (IRE1α), pancreatic endoplasmic reticulum kinase (PERK), and activating transcription factor 6 (ATF6) [ 91 ]; thus, it is logical that our analysis should have identified an association between these master regulators and NSAID administration. None of these canonical drivers of UPR, however, was selected by our methodology. This might be attributable to the fact that interrogated changes in mRNA expression; RNA-Seq does not identify initiation events such as nuclear translocation or phosphorylation. This might also be attributed to some of the other limitations mentioned above related to the concern that the samples we collected (i.e., feces) were not highly representative of the host and microbiota responses that occurred in the proximal GI tract resulting in missing some key cellular and molecular signatures.

In summary, our work demonstrates the power of non-invasive, multiomic approaches and robust computational analyses to integrate omic methods to interrogate host-microbiota interactions. Our findings recapitulate some of the known biology and pathophysiology of NSAID-induced intestinal injury, thereby adding confidence in the validity of our approach. By leveraging a mild model of injury, we have uncovered some of the initiating events of NSAID-induced intestinal injury which are often masked in more severe models. We propose a mechanistic hypothesis based on these findings. Importantly, our findings identify putative targets for therapeutics or preventatives in the treatment of NSAID enteropathy and other inflammatory GI diseases. Additional studies are needed to confirm our findings in horses and other animal species.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) within Bioproject # PRJNA909273 ( https://dataview.ncbi.nlm.nih.gov/object/PRJNA909273 ). Other data are included as additional information with this submission.

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This work was supported by the Grayson-Jockey Club Research Foundation; Platinum Performance; National Institutes of Health under Grant R35-CA197707 and P30-ES029067; and Allen Endowed Chair in Nutrition & Chronic Disease Prevention. These funding bodies had no role in the design of the study, sample collection, analysis, interpretation of data, or writing of this manuscript.

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CMWC: project design, data acquisition and analysis (all aspects), data interpretation, drafting and final review of manuscript. HCC: data analysis, data interpretation, drafting and final review of manuscript. MCC: project design, data acquisition, drafting and final review of manuscript. NDC: project design, data acquisition, drafting and final review of manuscript. AMCE: data acquisition, data interpretation, drafting and final review of manuscript. II: design data analysis approach, data analysis, data interpretation, final review of manuscript. JRG: data acquisition, data analysis, final review of manuscript. LAD: design data analysis approach, data interpretation, final review of manuscript. IG: design data analysis approach, data analysis, data interpretation, drafting and final review of manuscript. YN: design data analysis approach, data analysis data interpretation, final review of manuscript. RSC: project design, design data analysis approach, data interpretation, drafting and final review of manuscript.

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Whitfield-Cargile, C.M., Chung, H.C., Coleman, M.C. et al. Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury. Microbiome 12 , 74 (2024). https://doi.org/10.1186/s40168-024-01785-1

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DOI : https://doi.org/10.1186/s40168-024-01785-1

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