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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro.

We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

• Clarity : Data presentations make complex information clear and concise.
• Engagement : Visuals, such as charts and graphs, grab your audience's attention.
• Comprehension : Visual data is easier to understand than long, numerical reports.
• Decision-making : Well-presented data aids informed decision-making.
• Impact : It leaves a lasting impression on your audience.

Types of data presentation

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

• Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
• Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
• Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
• Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation

When creating your data presentation, remember these key components:

• Data points : Clearly state the data points you're presenting.
• Comparison : Highlight comparisons and trends in your data.
• Graphical methods : Choose the right chart or graph for your data.
• Infographics : Use visuals like infographics to make information more digestible.
• Numerical values : Include numerical values to support your visuals.
• Qualitative information : Explain the significance of the data.
• Source citation : Always cite your data sources.

How to structure an effective data presentation

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

• Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
• High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
• Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
• Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

• Use visuals : Incorporate charts and graphs to enhance understanding.
• Keep it simple : Avoid clutter and complexity.
• Highlight key points : Emphasize crucial data.
• Engage the audience : Encourage questions and discussions.
• Practice : Rehearse your presentation.

Don'ts:

• Overload with data : Less is often more; don't overwhelm your audience.
• Fit Unrelated data : Stay on topic; don't include irrelevant information.
• Neglect the audience : Ensure your presentation suits your audience's level of expertise.
• Read word-for-word : Avoid reading directly from slides.
• Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

• Definition : Data presentation is the art of visualizing complex data for better understanding.
• Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
• Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
• Choosing methods : Select the right method based on data, audience, and purpose.
• Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
• Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
• Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

1. What is data presentation, and why is it important in 2023?

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

• Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
• On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
• Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
• Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
• Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive!

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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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1.3: Presentation of Data

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Learning Objectives

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array} \nonumber \]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \nonumber \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array} \nonumber \]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

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When and how should you use data in a presentation?

The answer is that you should use figures and numbers whenever they give the best evidence to back up your argument, or to tell your story. But how to present that data is more difficult.

Many people are not interested in tables of numbers, and may struggle to understand graphs. How can you help walk them through the data?

This page is designed to help you to answer that question by setting out some simple rules for presenting data.

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

Human beings have been hard-wired, over millions of years of evolution, to enjoy and respond to stories. It’s best to work with it, not fight it, because if you tell your audience a story, they are likely to listen much more carefully, and also move towards a logical conclusion: the insight to which you are trying to lead them.

Once you understand this, the issue of using data falls into place: it is to provide evidence of how your story unfolds.

Use Data to Tell the Story

You are not presenting data as such, you are using data to help you to tell your story in a more meaningful way.

This means that whenever you are required to present data, you should be asking yourself:

‘ What is the story in this data? ’,

‘ How best can I tell this story to my audience? ’

A Picture Tells a Thousand Words

90% of the information sent to the brain is visual and over 90% of all human communication is visual. Processing text requires our brains to work much harder than when processing images. In fact, the brain can process pictorial information 60,000 times faster than written information.

There is considerable truth in the saying ‘a picture tells a thousand words’ . It may not be literally a thousand, but it is often much easier to use a picture than to describe numerical information in words.

The data itself may be vitally important, but without a visual presentation of that data, its impact (and therefore your message) may be lost.

There are many people in the world who do not find it easy to understand numbers.

There are also many people who will simply switch off if you show them figures in a table. But if you present data in a graph or pie chart, you make a pictorial representation of the data. It makes the numbers much easier to understand. Trends and proportions become more obvious.

Consider this set of data:

Even for the highly numerate, the immediate point is only that there are lot more sales in the first quarter. You would have to do some adding up and dividing to work out the relationships between the four numbers. It also requires much more concentration to read and absorb the information in this format.

Now consider the same data in a pie chart:

It is immediately and shiningly obvious, even for those who struggle with numbers, that more than half of all sales were in the first quarter, and that over 75% were in the first two quarters.

What’s more, nobody is going to be straining from the back of the room to read your figures. You really can see a lot more from a picture.

But, and this is important, make sure that the graph is a good one.

Check that your graph or chart is visually appealing, that all the labels are clear, and that you have used an appropriate type of graph or chart. Poor graph-making is always obvious and can lead to confusion. Your message will also have much more impact if you choose the right type of graph or chart.

For more about this, see our page on Graphs and Charts .

KISS: Keep It Simple, Stupid!

When you’re good at statistics, it’s very tempting to do some really whizzy analysis. And once you’ve done that, you really want to show everyone how clever you are, and how much work you’ve done.

But does it really help to make your point?

Then don’t present it.

In the (relatively rare) cases when you actually need some really whizzy analysis, you then need to ask yourself whether everyone will understand it. And, in these days of presentations being posted on the internet, will the casual reader of your slides understand it later?

Once again, if the answer is ‘probably not’, then don’t use it.

Leave It Out...

If you can’t summarise your analysis in one or two brief and clear sentences, then don’t include it.

It also follows that if you don’t need to include data to make your point, then it may be best not to do so. A slide that is likely to be misunderstood or produce confusion is worse than no slide at all. So cut out all unnecessary data and focus on what you really need  to tell your story .

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

We’re not suggesting that you should ‘ dumb down ’ your presentation, but there is no harm in highlighting the key features, as well as cutting out unnecessary data.

Suppose once again that you are using the sales figures from the last four quarters. You want to show the actual figures. Why not use a highlighting tool to emphasise that the first quarter is more than half?

With PowerPoint and other presentation software, you can make each circle appear separately, as you make your point and discuss the insights.

A little creative use of the technology can help you to highlight certain figures, and once again, make the story clearer.

Take-home message

Paradoxically, your presentation of any data should be designed to move the conversation away from the data and into the insight and action that should result from it.

In other words:

‘What happened there?’

‘What are we going to do about it?’

If you look at your presentation, data and all, and it’s not clear how you would get from the data to the insight and then the action, it’s probably a good idea to look at it again.

Remember, it’s the story that matters… and then what happens as a result.

Continue to: Writing Your Presentation Working with Visual Aids

See also: What is Your Story? How to Identify Your Story from Raw Data Crisis Communications Presenting to Large Groups Simple Statistical Analysis

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More Than Numbers: Effectively Presenting and Sharing Data

Estimated Reading Time : 4 mins

It’s a data-driven business world out there, which is great, right? But with so much input now available, presenting data effectively has become its own challenge. You need to do more than just display numbers and charts. You need to convey a clear, compelling story that communicates the key takeaways and resonates with your audiences. This article’s best practices can help ensure your data presentation communicates your intended message.

Know your audience

Any communication is more effective if you understand who you are presenting to. By considering the audience’s background, knowledge level, interests, and objectives you can better tailor your presentation to match their needs and expectations.

It’s your job to simplify and focus the data around a main message. Your entire presentation should revolve around this central theme. Understanding your audience can help you choose the most relevant and impactful data points that support the main message.

Tell a story

It’s best to use the data to tell a story. A picture may be worth a thousand words, but as one data analyst notes in the Harvard Business Review , “Few forms of communication are as persuasive as a compelling narrative.”

Data storytelling constructs a story from the insights hidden in the data. Taking a storytelling approach forces you to interpret and simplify complex information in an engaging way that offers value to your audience. This empowers you to offer a narrative which makes the data more relatable and engaging, thereby helping build your credibility as well.

Start with an executive summary

An oft-cited study statistic from the National Statistics Council tells us the average employee spends 37% of their time in meetings. Help your audience benefit from the time spent meeting about your data by beginning your presentation with an executive summary.

Executive summaries provide a concise overview offering a quick understanding of the key takeaways without delving into all the details. When you craft an effective executive summary , you capture your main message and distill complex concepts into simple, easy-to-understand language to help your audience grasp the main points.

Use visuals wisely

Data visualization is an essential part of data presentation. Visual elements like charts, graphs, and infographics are powerful tools for effectively presenting and sharing data. Still, you’ll want to choose the right type of data visualization to ensure that you are communicating your findings for maximum impact:

• Bar charts with vertical or horizontal bars are useful for showing comparisons, trends, and distribution.
• Line graphs connecting data points with lines are great for showing trends and changes over time.
• Pie charts displaying parts of a whole to help illustrate percentages or proportions.
• Scatter plots show the relationship between two continuous variables by plotting data points on a graph.
• Heat maps use color variations in a grid to show patterns, correlations, or clustering within large datasets.
• Tree maps can present hierarchical data as nested rectangles to show proportions and relationships.
• Word clouds let you show textual data with word size representing frequency, which makes it easy to identify common terms.

It’s also critical to avoid clutter and ensure that your visuals are easy to read. Using descriptive headlines and labels can help your audience quickly grasp what the data is about and what it’s showing.

It can also help to use color, annotation, or emphasis to draw attention to important data points or trends. This guides your audience’s focus to the most critical aspects of your message. At the same time, don’t undervalue the power of white space to reduce clutter and improve readability,

Provide context

Without proper context, data can be interpreted in various ways. Be sure to include background information, explanations, and comparisons that help your audience understand the significance of the data .

Providing context by explaining the background and circumstances, strengthens the conclusions you draw from the data. To effectively situate your audience in the data:

• Introduce the topic or problem your data addresses.
• Explain why this data is relevant and give background into what prompted the analysis.
• Define any technical terms or industry-specific jargon
• Describe how the data was collected, analyzed, and processed
• Outline where the data comes from, its quality, and any potential biases
• Highlight any relevant trends or patterns that the data reveals
• Compare current and historical data to showcase changes over time or to benchmarks, industry standards, or similar scenarios to put the data into perspective.
• Discuss any external factors that might have influenced the data, such as economic conditions or regulatory changes
• Address any outliers or anomalies, explain why they occurred and whether they impact the overall trends
• Explain the implications of the data sharing real-world scenarios or anecdotes that illustrate how the data translates into practical situations

Rehearse your presentation

Effective presenting and sharing of your data takes effort. You should build in time to rehearse your delivery. Practice your pacing, tone, and transitions between different parts of your story.

It can help also to anticipate potential questions your audience might have about the data. Being prepared to address queries demonstrates your expertise and enhances credibility.

Effectively share and present your data

Remember, your goal is to make your data not only informative but also engaging and memorable. By following these best practices for your data presentations, you can increase the likelihood that your audience will understand and remember the insights you’re sharing.

At Sogolytics we know your data isn’t very valuable if you can’t make sense of it. Our powerful reporting tools give you many ways to share and present your data, even in real-time. Learn more .

If you’re looking for a platform that helps you decode the meaning behind your data with powerful analytics and presentation-ready reports, Sogolytics has your back!

Ready to learn more? Let’s get on a call and we’d love to share how our platform can help!

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1.3: Presentation of Data

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Skills to Develop

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array}\]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array}\]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

Contributor

• Template:ContribShaferZhang

Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

• The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
• Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

To learn more Maths-related concepts, stay tuned with BYJU’S – The Learning App and download the app today!

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10 Data Presentation Tips

There’s a popular joke in data circles that you might have already heard: Data practitioners spend 80% of their time preparing data and 20% complaining about preparing data. The truth is, there’s much more to being a data professional than this. Sure, you’ll prepare data — and complain about it sometimes — but you’ll also need to make data presentations to key stakeholders in your company. Remember, data doesn’t mean much until you provide context and present it clearly.

Thankfully, we’re here to help. Here are 10 data presentation tips to effectively communicate with executives, senior managers, marketing managers, and other stakeholders.

1. Choose a Communication Style

Every data professional has a different way of presenting data to their audience. Some people like to tell stories with data, illustrating solutions to existing and potential business problems. Others enjoy using personas to demonstrate how their data findings impact real people. And then some like to present data more conventionally and simply explain what different figures and statistics mean in a business context.

Whatever style you choose, think about the words you will use and how you will present your information. You’ll want to engage your audience as much as possible, even if your findings aren’t particularly interesting.

2. Break Down Complicated Information

Not everyone will comprehend data as well as you do. As a data practitioner, you’ll understand the nuances of data, such as how different data sets correlate with each other and how outliers can impact analysis. However, most people lack knowledge of these concepts.

That’s why you should simplify your data presentations and focus on key takeaways from your findings that stakeholders will understand. For example, instead of showing your audience a spreadsheet with lots of numbers, explain what those numbers  prove and what they mean for the company you will work for.

3. Choose the Right Data Visualizations

Sharing cold, hard data sets with people won’t be very effective. Instead, use different data visualizations so your audience can understand the relationships between data sets and the context behind them. There are lots of  different visualizations  that help you communicate important information:

• Line graphs
• Scatter plots

The type of  visualizations you choose depends on what information you’re trying to convey. Graphs, for example, help you showcase potential business outcomes to stakeholders clearly and consistently. Heat maps, on the other hand, let you highlight the most critical data values your audience should know about.

4. Choose the Right Visualization Tools

Numerous  data visualization tools  on the market will help you communicate data to people in your company. These tools include:

• Microsoft Power BI
• Google Charts

All of these tools are inherently better than presenting data in Excel. You’ll be able to communicate patterns and trends in data more effectively and encourage your audience to interact with your findings.

5. Get Your Audience Involved

Communication is a two-way process, so encourage those at your future data presentations to interact with your content. Before you begin your data presentation, you might want to tell your audience to interrupt you if they want more clarification about a particular data point or insight.

Alternatively, people can ask you questions at the end of your presentation if they don’t understand something or require additional context.

6. Be Authoritative

You’ll almost always present your data findings to key stakeholders in your business. Project confidence when sharing insights and make it clear you know what you’re talking about. Otherwise, your audience might lack confidence in your abilities. Ultimately, explain how you came to a particular conclusion and why you think it’s important to share.

Of course, there will be times when the data you present won’t be what your audience wants to hear. For example, a line graph might reveal that a business will lose revenue over time . In these scenarios, always communicate the facts, even if doing so puts you in an uncomfortable position.

7. Label Your Data Clearly

This point goes back to the fact that your audience won’t know as much about your data as you do. So, avoid using unfamiliar acronyms to label charts or complicated jargon that only other data practitioners would understand. Your role is to present information in a clear and visually compelling way to help stakeholders make better data-driven decisions .

8. Practice Your Data Presentation With Other Team Members

You can always have a dress rehearsal for a presentation before walking into the boardroom. Delivering your findings to other data practitioners on your team, data scientists, data engineers, or other data professionals in your department will help you identify any weak spots in your presentation and ensure you use the right communication style for your audience.

9. Allow Your Audience to Access Your Findings After Your Presentation

A 30- or 60-minute meeting normally won’t be long enough to communicate all your findings or receive stakeholder feedback. Audience members might also forget key points after it’s finished. So, share your insights after your presentation, perhaps in a document. You’ll be able to email colleagues your report so they can review important information. Alternatively, you can upload your presentation slides to Dropbox or your company’s intranet.

Data practitioners often worry about presenting their data to an audience, which is understandable. But you’ll develop a unique communication style and become more confident as the months and years go by. Just remember you’re not a doctor breaking bad news about an incurable health condition. You’re helping businesses understand data, which can be an exciting thing, so try to relax and enjoy yourself!

The Pragmatic Editorial Team comprises a diverse team of writers, researchers, and subject matter experts. We are trained to share Pragmatic Institute’s insights and useful information to guide product, data, and design professionals on their career development journeys. Pragmatic Institute is the global leader in Product, Data, and Design training and certification programs for working professionals. Since 1993, we’ve issued over 250,000 product management and product marketing certifications to professionals at companies around the globe. For questions or inquiries, please contact [email protected] .

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Types of Data Presentation

Data presentation refers to the process of organizing and displaying data in a clear and understandable manner. It focuses on effectively conveying information, insights, and key findings derived from the data in a concise and meaningful way. It may involve using text, visuals, and other elements to provide context, summarize data, and communicate the main points to the audience.

Data presentations can take various forms, including verbal and written formats. Here's a breakdown of the two –

Verbal Data Presentation

Verbal data presentations involve delivering information and insights orally, typically in the form of a speech, presentation, or discussion. This can be done in person, through video conferencing, or via recorded audio. Verbal presentations often involve accompanying visual aids, such as slides or multimedia elements, to support the delivery of the data. Verbal presentations allow for real-time interaction, the opportunity to address audience questions, and the flexibility to adapt the presentation based on audience feedback.

Written Data Presentation

Written data presentations involve conveying information and insights through written documents, reports, articles, or other written formats. These presentations are typically static and can be shared electronically or in print. Written presentations rely heavily on text, but they may also include visual elements, such as charts, graphs, tables, or infographics, to enhance data representation and understanding. Written presentations offer the advantage of being easily distributable and referenceable, allowing readers to review the information at their own pace.

Both verbal and written data presentations have their own advantages and are suitable for different contexts. Verbal presentations are effective for engaging the audience, facilitating discussions, and delivering real-time explanations. On the other hand, written presentations provide a more permanent record of the data, allow for careful review and analysis, and can be easily shared with a wider audience.

Here’s a breakdown of the advantages, disadvantages and use case of each data presentation format –

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• CBSE Class 11 Statistics for Economics Notes

Chapter 1: Concept of Economics and Significance of Statistics in Economics

• Statistics for Economics | Functions, Importance, and Limitations

Chapter 2: Collection of Data

• Data Collection & Its Methods
• Sources of Data Collection | Primary and Secondary Sources
• Direct Personal Investigation: Meaning, Suitability, Merits, Demerits and Precautions
• Indirect Oral Investigation : Suitability, Merits, Demerits and Precautions
• Difference between Direct Personal Investigation and Indirect Oral Investigation
• Information from Local Source or Correspondents: Meaning, Suitability, Merits, and Demerits
• Questionnaires and Schedules Method of Data Collection
• Difference between Questionnaire and Schedule
• Qualities of a Good Questionnaire and types of Questions
• What are the Published Sources of Collecting Secondary Data?
• What Precautions should be taken before using Secondary Data?
• Two Important Sources of Secondary Data: Census of India and Reports & Publications of NSSO
• What is National Sample Survey Organisation (NSSO)?
• What is Census Method of Collecting Data?
• Sample Method of Collection of Data
• Methods of Sampling
• Father of Indian Census
• What makes a Sampling Data Reliable?
• Difference between Census Method and Sampling Method of Collecting Data
• What are Statistical Errors?

Chapter 3: Organisation of Data

• Organization of Data
• Objectives and Characteristics of Classification of Data
• Classification of Data in Statistics | Meaning and Basis of Classification of Data
• Concept of Variable and Raw Data
• Types of Statistical Series
• Difference between Frequency Array and Frequency Distribution
• Types of Frequency Distribution

Chapter 4: Presentation of Data: Textual and Tabular

Textual presentation of data: meaning, suitability, and drawbacks.

• Tabular Presentation of Data: Meaning, Objectives, Features and Merits
• Different Types of Tables
• Classification and Tabulation of Data

Chapter 5: Diagrammatic Presentation of Data

• Diagrammatic Presentation of Data: Meaning , Features, Guidelines, Advantages and Disadvantages
• Types of Diagrams
• Bar Graph | Meaning, Types, and Examples
• Pie Diagrams | Meaning, Example and Steps to Construct
• Histogram | Meaning, Example, Types and Steps to Draw
• Frequency Polygon | Meaning, Steps to Draw and Examples
• Ogive (Cumulative Frequency Curve) and its Types
• What is Arithmetic Line-Graph or Time-Series Graph?
• Diagrammatic and Graphic Presentation of Data

Chapter 6: Measures of Central Tendency: Arithmetic Mean

• Measures of Central Tendency in Statistics
• Arithmetic Mean: Meaning, Example, Types, Merits, and Demerits
• What is Simple Arithmetic Mean?
• Calculation of Mean in Individual Series | Formula of Mean
• Calculation of Mean in Discrete Series | Formula of Mean
• Calculation of Mean in Continuous Series | Formula of Mean
• Calculation of Arithmetic Mean in Special Cases
• Weighted Arithmetic Mean

Chapter 7: Measures of Central Tendency: Median and Mode

• Median(Measures of Central Tendency): Meaning, Formula, Merits, Demerits, and Examples
• Calculation of Median for Different Types of Statistical Series
• Calculation of Median in Individual Series | Formula of Median
• Calculation of Median in Discrete Series | Formula of Median
• Calculation of Median in Continuous Series | Formula of Median
• Graphical determination of Median
• Mode: Meaning, Formula, Merits, Demerits, and Examples
• Calculation of Mode in Individual Series | Formula of Mode
• Calculation of Mode in Discrete Series | Formula of Mode
• Grouping Method of Calculating Mode in Discrete Series | Formula of Mode
• Calculation of Mode in Continuous Series | Formula of Mode
• Calculation of Mode in Special Cases
• Calculation of Mode by Graphical Method
• Mean, Median and Mode| Comparison, Relationship and Calculation

Chapter 8: Measures of Dispersion

• Measures of Dispersion | Meaning, Absolute and Relative Measures of Dispersion
• Range | Meaning, Coefficient of Range, Merits and Demerits, Calculation of Range
• Calculation of Range and Coefficient of Range
• Interquartile Range and Quartile Deviation
• Partition Value | Quartiles, Deciles and Percentiles
• Quartile Deviation and Coefficient of Quartile Deviation: Meaning, Formula, Calculation, and Examples
• Quartile Deviation in Discrete Series | Formula, Calculation and Examples
• Quartile Deviation in Continuous Series | Formula, Calculation and Examples
• Mean Deviation: Coefficient of Mean Deviation, Merits, and Demerits
• Calculation of Mean Deviation for different types of Statistical Series
• Mean Deviation from Mean | Individual, Discrete, and Continuous Series
• Mean Deviation from Median | Individual, Discrete, and Continuous Series
• Standard Deviation: Meaning, Coefficient of Standard Deviation, Merits, and Demerits
• Standard Deviation in Individual Series
• Methods of Calculating Standard Deviation in Discrete Series
• Methods of calculation of Standard Deviation in frequency distribution series
• Combined Standard Deviation: Meaning, Formula, and Example
• How to calculate Variance?
• Coefficient of Variation: Meaning, Formula and Examples
• Lorenz Curveb : Meaning, Construction, and Application

Chapter 9: Correlation

• Correlation: Meaning, Significance, Types and Degree of Correlation
• Methods of measurements of Correlation
• Calculation of Correlation with Scattered Diagram
• Spearman's Rank Correlation Coefficient
• Karl Pearson's Coefficient of Correlation
• Karl Pearson's Coefficient of Correlation | Methods and Examples

Chapter 10: Index Number

• Index Number | Meaning, Characteristics, Uses and Limitations
• Methods of Construction of Index Number
• Unweighted or Simple Index Numbers: Meaning and Methods
• Methods of calculating Weighted Index Numbers
• Fisher's Index Number as an Ideal Method
• Fisher's Method of calculating Weighted Index Number
• Paasche's Method of calculating Weighted Index Number
• Laspeyre's Method of calculating Weighted Index Number
• Laspeyre's, Paasche's, and Fisher's Methods of Calculating Index Number
• Consumer Price Index (CPI) or Cost of Living Index Number: Construction of Consumer Price Index|Difficulties and Uses of Consumer Price Index
• Methods of Constructing Consumer Price Index (CPI)
• Wholesale Price Index (WPI) | Meaning, Uses, Merits, and Demerits
• Index Number of Industrial Production : Characteristics, Construction & Example
• Inflation and Index Number

Important Formulas in Statistics for Economics

• Important Formulas in Statistics for Economics | Class 11

Presentation of Data refers to the exhibition of data in such a clear and attractive way that it is easily understood and analysed. Data can be presented in different forms, including Textual or Descriptive Presentation, Tabular Presentation, and Diagrammatic Presentation.

Textual Presentation

Textual or Descriptive Presentation of Data is one of the most common forms of data presentation. In this, data is a part of the text of the study or a part of the description of the subject matter of the study. It is usually preferred when the quantity of data is not very large. For example, there are 50 students in a class, among them 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Suitability

Textual Presentation of Data is suitable when the quantity of data is not large. It means that a small portion of data that is presented as a part of the subject matter of study can become useful supportive evidence to the given text. Therefore, instead of saying that the price of petrol is skyrocketing, it can be said that the price of petrol has increased by 20% in the last 2 years, and this statement will be more meaningful and precise. Under textual presentation of data, an individual does not have to support the text with the help of a diagram or table as the text in itself is very small and has few observations. 

Advantages of Textual Presentation of Data

Textual Presentation of Data has the following benefits:

1. It allows the researcher to make an elaborate interpretation of data during the presentation. 

2. A researcher can easily present qualitative data that cannot be presented in tabular or graphical form using the textual presentation of data. 

3. If the data is present in small sets, a textual presentation can be easily used. For example, there are 50 students in a class, among them, 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Disadvantages of Textual Presentation of Data

Textual Presentation of Data has the following drawbacks:

1. One of the major drawbacks of the textual presentation of data is that it provides extensive data in the form of text and paragraphs which makes it difficult for the user of data to draw a proper conclusion at a glance. This facility is provided in tabular or diagrammatic presentation of data.

2. This method of presenting data is not suitable for large sets of data as these sets contain too many details. 

3. Besides, one has to read through the whole text in order to understand and comprehend the main point of the data.

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How to Present to an Audience That Knows More Than You

• Deborah Grayson Riegel

Lean into being a facilitator — not an expert.

What happens when you have to give a presentation to an audience that might have some professionals who have more expertise on the topic than you do? While it can be intimidating, it can also be an opportunity to leverage their deep and diverse expertise in service of the group’s learning. And it’s an opportunity to exercise some intellectual humility, which includes having respect for other viewpoints, not being intellectually overconfident, separating your ego from your intellect, and being willing to revise your own viewpoint — especially in the face of new information. This article offers several tips for how you might approach a roomful of experts, including how to invite them into the discussion without allowing them to completely take over, as well as how to pivot on the proposed topic when necessary.

I was five years into my executive coaching practice when I was invited to lead a workshop on “Coaching Skills for Human Resource Leaders” at a global conference. As the room filled up with participants, I identified a few colleagues who had already been coaching professionally for more than a decade. I felt self-doubt start to kick in: Why were they even here? What did they come to learn? Why do they want to hear from me?

• Deborah Grayson Riegel is a professional speaker and facilitator, as well as a communication and presentation skills coach. She teaches leadership communication at Duke University’s Fuqua School of Business and has taught for Wharton Business School, Columbia Business School’s Women in Leadership Program, and Peking University’s International MBA Program. She is the author of Overcoming Overthinking: 36 Ways to Tame Anxiety for Work, School, and Life and the best-selling Go To Help: 31 Strategies to Offer, Ask for, and Accept Help .

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What is climate change mitigation and why is it urgent?

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• Climate change mitigation involves actions to reduce or prevent greenhouse gas emissions from human activities.
• Mitigation efforts include transitioning to renewable energy sources, enhancing energy efficiency, adopting regenerative agricultural practices and protecting and restoring forests and critical ecosystems.
• Effective mitigation requires a whole-of-society approach and structural transformations to reduce emissions and limit global warming to 1.5°C above pre-industrial levels.
• International cooperation, for example through the Paris Agreement, is crucial in guiding and achieving global and national mitigation goals.
• Mitigation efforts face challenges such as the world's deep-rooted dependency on fossil fuels, the increased demand for new mineral resources and the difficulties in revamping our food systems.
• These challenges also offer opportunities to improve resilience and contribute to sustainable development.

What is climate change mitigation?

Climate change mitigation refers to any action taken by governments, businesses or people to reduce or prevent greenhouse gases, or to enhance carbon sinks that remove them from the atmosphere. These gases trap heat from the sun in our planet’s atmosphere, keeping it warm. 

Since the industrial era began, human activities have led to the release of dangerous levels of greenhouse gases, causing global warming and climate change. However, despite unequivocal research about the impact of our activities on the planet’s climate and growing awareness of the severe danger climate change poses to our societies, greenhouse gas emissions keep rising. If we can slow down the rise in greenhouse gases, we can slow down the pace of climate change and avoid its worst consequences.

Reducing greenhouse gases can be achieved by:

• Shifting away from fossil fuels : Fossil fuels are the biggest source of greenhouse gases, so transitioning to modern renewable energy sources like solar, wind and geothermal power, and advancing sustainable modes of transportation, is crucial.
• Improving energy efficiency : Using less energy overall – in buildings, industries, public and private spaces, energy generation and transmission, and transportation – helps reduce emissions. This can be achieved by using thermal comfort standards, better insulation and energy efficient appliances, and by improving building design, energy transmission systems and vehicles.
• Changing agricultural practices : Certain farming methods release high amounts of methane and nitrous oxide, which are potent greenhouse gases. Regenerative agricultural practices – including enhancing soil health, reducing livestock-related emissions, direct seeding techniques and using cover crops – support mitigation, improve resilience and decrease the cost burden on farmers.
• The sustainable management and conservation of forests : Forests act as carbon sinks , absorbing carbon dioxide and reducing the overall concentration of greenhouse gases in the atmosphere. Measures to reduce deforestation and forest degradation are key for climate mitigation and generate multiple additional benefits such as biodiversity conservation and improved water cycles.
• Restoring and conserving critical ecosystems : In addition to forests, ecosystems such as wetlands, peatlands, and grasslands, as well as coastal biomes such as mangrove forests, also contribute significantly to carbon sequestration, while supporting biodiversity and enhancing climate resilience.
• Creating a supportive environment : Investments, policies and regulations that encourage emission reductions, such as incentives, carbon pricing and limits on emissions from key sectors are crucial to driving climate change mitigation.

Photo: Stephane Bellerose/UNDP Mauritius

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What is the 1.5°C goal and why do we need to stick to it?

In 2015, 196 Parties to the UN Climate Convention in Paris adopted the Paris Agreement , a landmark international treaty, aimed at curbing global warming and addressing the effects of climate change. Its core ambition is to cap the rise in global average temperatures to well below 2°C above levels observed prior to the industrial era, while pursuing efforts to limit the increase to 1.5°C.

The 1.5°C goal is extremely important, especially for vulnerable communities already experiencing severe climate change impacts. Limiting warming below 1.5°C will translate into less extreme weather events and sea level rise, less stress on food production and water access, less biodiversity and ecosystem loss, and a lower chance of irreversible climate consequences.

To limit global warming to the critical threshold of 1.5°C, it is imperative for the world to undertake significant mitigation action. This requires a reduction in greenhouse gas emissions by 45 percent before 2030 and achieving net-zero emissions by mid-century.

What are the policy instruments that countries can use to drive mitigation?

Everyone has a role to play in climate change mitigation, from individuals adopting sustainable habits and advocating for change to governments implementing regulations, providing incentives and facilitating investments. The private sector, particularly those businesses and companies responsible for causing high emissions, should take a leading role in innovating, funding and driving climate change mitigation solutions. 

International collaboration and technology transfer is also crucial given the global nature and size of the challenge. As the main platform for international cooperation on climate action, the Paris Agreement has set forth a series of responsibilities and policy tools for its signatories. One of the primary instruments for achieving the goals of the treaty is Nationally Determined Contributions (NDCs) . These are the national climate pledges that each Party is required to develop and update every five years. NDCs articulate how each country will contribute to reducing greenhouse gas emissions and enhance climate resilience.   While NDCs include short- to medium-term targets, long-term low emission development strategies (LT-LEDS) are policy tools under the Paris Agreement through which countries must show how they plan to achieve carbon neutrality by mid-century. These strategies define a long-term vision that gives coherence and direction to shorter-term national climate targets.

Photo: Mucyo Serge/UNDP Rwanda

Photo: William Seal/UNDP Sudan

At the same time, the call for climate change mitigation has evolved into a call for reparative action, where high-income countries are urged to rectify past and ongoing contributions to the climate crisis. This approach reflects the UN Framework Convention on Climate Change (UNFCCC) which advocates for climate justice, recognizing the unequal historical responsibility for the climate crisis, emphasizing that wealthier countries, having profited from high-emission activities, bear a greater obligation to lead in mitigating these impacts. This includes not only reducing their own emissions, but also supporting vulnerable countries in their transition to low-emission development pathways.

Another critical aspect is ensuring a just transition for workers and communities that depend on the fossil fuel industry and its many connected industries. This process must prioritize social equity and create alternative employment opportunities as part of the shift towards renewable energy and more sustainable practices.

For emerging economies, innovation and advancements in technology have now demonstrated that robust economic growth can be achieved with clean, sustainable energy sources. By integrating renewable energy technologies such as solar, wind and geothermal power into their growth strategies, these economies can reduce their emissions, enhance energy security and create new economic opportunities and jobs. This shift not only contributes to global mitigation efforts but also sets a precedent for sustainable development.

What are some of the challenges slowing down climate change mitigation efforts?

Mitigating climate change is fraught with complexities, including the global economy's deep-rooted dependency on fossil fuels and the accompanying challenge of eliminating fossil fuel subsidies. This reliance – and the vested interests that have a stake in maintaining it – presents a significant barrier to transitioning to sustainable energy sources.

The shift towards decarbonization and renewable energy is driving increased demand for critical minerals such as copper, lithium, nickel, cobalt, and rare earth metals. Since new mining projects can take up to 15 years to yield output, mineral supply chains could become a bottleneck for decarbonization efforts. In addition, these minerals are predominantly found in a few, mostly low-income countries, which could heighten supply chain vulnerabilities and geopolitical tensions.

Furthermore, due to the significant demand for these minerals and the urgency of the energy transition, the scaled-up investment in the sector has the potential to exacerbate environmental degradation, economic and governance risks, and social inequalities, affecting the rights of Indigenous Peoples, local communities, and workers. Addressing these concerns necessitates implementing social and environmental safeguards, embracing circular economy principles, and establishing and enforcing responsible policies and regulations .

Agriculture is currently the largest driver of deforestation worldwide. A transformation in our food systems to reverse the impact that agriculture has on forests and biodiversity is undoubtedly a complex challenge. But it is also an important opportunity. The latest IPCC report highlights that adaptation and mitigation options related to land, water and food offer the greatest potential in responding to the climate crisis. Shifting to regenerative agricultural practices will not only ensure a healthy, fair and stable food supply for the world’s population, but also help to significantly reduce greenhouse gas emissions.  

Photo: UNDP India

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What are some examples of climate change mitigation?

In Mauritius , UNDP, with funding from the Green Climate Fund, has supported the government to install battery energy storage capacity that has enabled 50 MW of intermittent renewable energy to be connected to the grid, helping to avoid 81,000 tonnes of carbon dioxide annually. 

In Indonesia , UNDP has been working with the government for over a decade to support sustainable palm oil production. In 2019, the country adopted a National Action Plan on Sustainable Palm Oil, which was collaboratively developed by government, industry and civil society representatives. The plan increased the adoption of practices to minimize the adverse social and environmental effects of palm oil production and to protect forests. Since 2015, 37 million tonnes of direct greenhouse gas emissions have been avoided and 824,000 hectares of land with high conservation value have been protected.

In Moldova and Paraguay , UNDP has helped set up Green City Labs that are helping build more sustainable cities. This is achieved by implementing urban land use and mobility planning, prioritizing energy efficiency in residential buildings, introducing low-carbon public transport, implementing resource-efficient waste management, and switching to renewable energy sources. 

UNDP has supported the governments of Brazil, Costa Rica, Ecuador and Indonesia to implement results-based payments through the REDD+ (Reducing emissions from deforestation and forest degradation in developing countries) framework. These include payments for environmental services and community forest management programmes that channel international climate finance resources to local actors on the ground, specifically forest communities and Indigenous Peoples. 

UNDP is also supporting small island developing states like the Comoros to invest in renewable energy and sustainable infrastructure. Through the Africa Minigrids Program , solar minigrids will be installed in two priority communities, Grand Comore and Moheli, providing energy access through distributed renewable energy solutions to those hardest to reach.

And in South Africa , a UNDP initative to boost energy efficiency awareness among the general population and improve labelling standards has taken over commercial shopping malls.

What is UNDP’s role in supporting climate change mitigation?

UNDP aims to assist countries with their climate change mitigation efforts, guiding them towards sustainable, low-carbon and climate-resilient development. This support is in line with achieving the Sustainable Development Goals (SDGs), particularly those related to affordable and clean energy (SDG7), sustainable cities and communities (SDG11), and climate action (SDG13). Specifically, UNDP’s offer of support includes developing and improving legislation and policy, standards and regulations, capacity building, knowledge dissemination, and financial mobilization for countries to pilot and scale-up mitigation solutions such as renewable energy projects, energy efficiency initiatives and sustainable land-use practices. 

With financial support from the Global Environment Facility and the Green Climate Fund, UNDP has an active portfolio of 94 climate change mitigation projects in 69 countries. These initiatives are not only aimed at reducing greenhouse gas emissions, but also at contributing to sustainable and resilient development pathways.

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What Is Data Ethics & Why Is It Important in Business?

• May 9, 2024

Data is powerful — every business on earth uses data. But some are leveraging it more than others.

The problem?

Not all businesses are using data ethically.

You need to collect , store, and analyse data to grow your business. But, if you aren’t careful, you could be crossing the line with your data usage into unethical territories.

In a society where data is more valuable than ever, it’s crucial you perform ethical practices.

In this article, we break down what data ethics is, why it’s important in business and how you can implement proper data ethics to ensure you stay compliant while growing your business.

What is data ethics?

Data ethics are how a business collects, protects and uses data.

It’s one field of ethics focused on organisations’ moral obligation to collect, track, analyse and interpret data correctly.

Data ethics analyses multiple ways we use data:

• Collecting data
• Generating data
• Tracking data
• Analysing data
• Interpreting data
• Implementing activities based on data

Data ethics is a field that asks, “Is this right or wrong?”

And it also asks, “Can we use data for good?”

If businesses use data unethically, they could get into serious hot water with their customers and even with the law.

You need to use data to ensure you grow your business to the best of your ability. But, to maintain a clean slate in the eyes of your customers and authorities, you need to ensure you have strong data ethics.

Why you need to follow data ethics principles

In 2018, hackers broke into British Airways’ website by inserting harmful code, leading website visitors to a fraudulent site. 

The result? 

British Airways customers gave their information to the hackers without realising it: credit cards, personal information, login information, addresses and more.

While this was a malicious attack, the reality is that data is an integral part of everyday life. Businesses need to do everything they can to protect their customers’ data and use it ethically.

Data ethics is crucial to understand as it sets the standard for what’s right and wrong for businesses. Without a clear grasp of data ethics, companies will willingly or neglectfully misuse data.

With a firm foundation of data ethics, businesses worldwide can make a collective effort to function smoothly, protect their customers, and, of course, protect their own reputation. 

3 benefits of leaning into data ethics

We’re currently transitioning to a new world led by artificial intelligence.

While AI presents endless opportunities for innovation in the business world, there are also countless risks at play, and it’s never been more important to develop trust with your customers and stakeholders.

With an influx of data being created and tracked daily, you need to ensure your business is prioritising data ethics to ensure you maintain trust with your customers moving forward.

Here are three benefits of data ethics that will help you develop trust, maintain a solid reputation and stay compliant to continue growing your business:

1. Compliance with data privacy

Privacy is everything. 

In a world where our data is being collected nonstop, and we live more public lives than ever with social media, AI and an influx of recording and tracking in everyday life, you need to protect the privacy of your customers.

One crucial way to protect that privacy is by complying with major data privacy regulations.

Some of the most common regulations you need to remain compliant with include:

• General Data Protection Regulation (GDPR)
• California Consumer Privacy Act (CCPA)
• Health Insurance Portability and Accountability Act (HIPAA)
• General Personal Data Protection Law (LGPD)
• Privacy and Electronic Communications (EC Directive) Regulations (PECR)

While these regulations don’t directly address ethics, there’s a core overlap between privacy requirements like accountability, lawfulness and AI ethics.

Matomo ensures you protect the privacy of your web and app users so you can track and improve your website performance with peace of mind.

2. Maintain a good reputation

While data ethics can help you maintain data privacy compliance, it can also help you maintain a good reputation online and offline.

All it takes is one bad event like the British Airways breach for your company’s reputation to be ruined.

If you want to keep a solid reputation and maintain trust with your stakeholders, customers and lawmakers, then you need to focus on developing strong data ethics.

Businesses that invest time in establishing proper data ethics set the right foundation to protect their reputation, develop trust with stakeholders and create goodwill and loyalty.

3. Increased trust means greater revenue

What happens when you establish proper data ethics?

You’ll gain the trust of your customers, maintain a solid reputation and increase your brand image.

Customers who trust you to protect their privacy and data want to keep doing business with you.

So, what’s the end result for a business that values data ethics?

You’ll generate more revenue in the long run. Trust is one thing you should never put on the back burner if you have plans to keep growing your business. By leaning more into data ethics, you’ll be able to build that brand reputation that helps people feel comfortable buying your products and services on repeat.

While spending time and money on data ethics may seem like an annoyance, the reality is that it’s a business investment that will pay dividends for years to come.

5 core data ethics principles

So, what exactly is involved in data ethics?

For most people, data ethics is a pretty broad and vague term. If you’re curious about the core pillars of data ethics, then keep reading.

Here are five core data ethical principles you need to follow to ensure you’re protecting your customers’ data and maintaining trust:

1. Data ownership

The individual owns the data, not you. This is the first principle of data ethics. You don’t have control over someone else’s data. It’s theirs, and they have full ownership over it.

Just as stealing a TV from an electronics store is a crime, stealing (or collecting) someone’s personal data without their consent is considered unlawful and unethical.

Consent is the only way to ethically “own” someone’s data.

How can you collect someone’s data ethically?

• Digital privacy policies
• Signed, written agreements
• Popups with checkboxes that allow you to track users’ behaviour

Essentially, anytime you’re collecting data from your website or app users, you need to ensure you’re asking permission for that data.

You should never assume a website visitor or customer is okay with you collecting your data automatically. Instead, ask permission to collect, track and use their data to avoid legal and ethical issues.

2. Transparency

The second core principle of data ethics within business is transparency. This means you need to be fully transparent on when, where and how you:

• Collect data

In other words, you need to allow your customers and website visitors to have a window inside your data activities.

They need to be able to see exactly how you plan on using the data you’re collecting from them.

For example, imagine you implemented a new initiative to personalise the website experience for each user based on individual behaviour. To do this, you’ll need to track cookies. In this case, you’d need to write up a new policy stating how this behavioural data is going to be collected, tracked and used.

It’s within your website visitors’ rights to access this information so they can choose whether or not they want to accept or decline your website’s cookies.

With any new data collection or tracking, you need to be 100% clear about how you’re going to use the data. You can’t be deceptive, misleading, or withholding any information on how you will use the data, as this is unethical and, in many cases, unlawful.

Another important branch of ethics is privacy. The ethical implications of this should be obvious.

When your users, visitors, or customers enter your sphere of influence and you begin collecting data on them, you are responsible for keeping that data private.

When someone accepts the terms of your data usage, they’re not agreeing to have their data released to the public. They’re agreeing to let you leverage that data as their trusted business provider to better serve them. They expect you to maintain privacy .

You can’t spread private information to third parties. You can’t blast this data to the public. 

This is especially important if someone allows you to collect and use their personally identifiable information ( PII ), such as:

• First and last name
• Email address
• Date of birth
• Home address
• Phone number

To protect your audience’s data, you should only store it in a secure database. 

For example, Matomo’s web analytics solution guarantees the privacy of both your users and analytics data.

With Matomo, you have complete ownership of your data. Unlike other web analytics solutions that exploit your data for advertising purposes, Matomo users can use analytics with confidence, knowing that their data won’t be sold to advertisers.

Learn more about data privacy with Matomo here .

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Get the web insights you need, while respecting user privacy.

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4. Intention

When you collect and store data, you need to tell your users why you’re collecting their data. But there’s another principle of data ethics that goes beyond the reason you give your customers.

Intention is the reason you give yourself for collecting and using the data.

Before you start collecting and storing data, you should ask yourself the following:

• Why you need it
• What you’ll gain from it
• What changes you’ll be able to make after you analyse the data

If your intention is wrong in any way, it’s unethical to collect the data:

• You’re collecting data to hurt others
• You’re collecting data to profit from your users’ weaknesses
• You’re collecting data for any other malicious reason

When you collect data, you need to have the right intentions to maintain proper data ethics; otherwise, you could harm your brand, break trust and ruin your reputation.

5. Outcomes

You may have the best intentions, but sometimes, there are negative outcomes from data use.

For example, British Airways’ intention was not to allow hackers to gain access and harm their users. But the reality is that their customers’ data was stolen and used for malicious purposes. While this isn’t technically unlawful, the outcome of collecting data ended badly.

To ensure proper data ethics, you must have good standing with your data. This means protecting your users at all costs, maintaining a good reputation and ensuring proper privacy measures are set up .

How to implement data ethics as a business leader

As a business leader, CTO or CEO, it’s your responsibility to implement data ethics within your organisation. Here are some tips to implement data ethics based on the size and stage of your organisation:

If you’re a startup, you need to be mindful of which technology and tools you use to collect, store and use data to help you grow your business.

It can be a real challenge to juggle all the moving parts of a startup since things can change so quickly. However, it’s crucial to establish a leader and allow easy access to ethical analysis resources to maintain proper data ethics early on.

Small and medium-sized businesses

As you begin scaling, you’ll likely be using even more technology. With each new business technique you implement, there will be new ways you’ll be collecting user data. 

One of the key processes involved in managing data as you grow is to hire engineers who build out different technologies. You must have protocols, best practices and management overseeing the new technologies being built to ensure proper data ethics.

Global businesses

Have you scaled internationally?

There will be even more rules, laws, regulations and organisations to answer to if you start managing data unethically.

You should have established teams or departments to ensure you follow proper privacy and data protocols worldwide. When you have a large organisation, you have more money and vast amounts of data. This makes you a bigger target for leaks, ransomware and hackers.

You should ensure you have cross-departmental groups working to establish ongoing protocols and training to keep your data management in good standing.

Leverage data ethically with Matomo

Data is powerful.

It’s a crucial point of leverage that’s required to stay competitive.

However, improper use and management of data can give you a bad reputation, break trust and even cause you legal trouble.

That’s why you must maintain good data ethics within your organisation.

One of the most important places to set up proper data ethics and privacy measures is with your website analytics.

Matomo is the leading, privacy-friendly web analytics solution in the world. It automatically collects, stores, and tracks data across your website ethically.

With over 1 million websites using Matomo, you get to take full control over your website performance with:

• Accurate data (no data sampling)
• Privacy-friendly and GDPR-compliant analytics
• Open-source for transparency and to create a custom solution for you

Try Matomo free for 21-days. No credit card required.

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Data Privacy: Your Phone Carrier Knows More Than You Think, How to Take Back Control

Here's how to protect your privacy and turn off tracking on AT&T, T-Mobile and Verizon.

Most cellphone users in the US are served by three major wireless carriers. 

Data tracking in 2024 seems inevitable. Whether you're using an iPhone or Android phone , your carrier is likely gathering all sorts of data about how, where and when you use your cellphone. 

This month T-Mobile quietly began rolling out a new tracking method called "profiling and automated decisions." Spotted by Reddit users and The Mobile Report , the new option is enabled by default. While the company says it isn't using information gleaned from such tracking today, it could be used later on for "future decisions that produce legal or similarly significant effects about you."

Read more:  Best Cellphone Plans of 2024

But the self-proclaimed "un-carrier" isn't alone. All three major US wireless providers collect data, here's what they gather and how you can turn it off. It is also worth noting that some of this you should want to keep on, particularly identity verification.

While we're focusing on the three main wireless carriers that make up a bulk of the US wireless market, it is likely smaller providers and even home internet services are engaging in similar collections. Heading to an account's profile or privacy page should help you figure out what is being collected and how you can adjust it. 

Read more: How to Adjust Your Streaming Stick or Smart TV's Privacy Settings

As we mentioned above, T-Mobile is not the only one collecting data about how you use their services. You can check your AT&T privacy settings by logging into your account and going to Profile followed by Privacy Choices .

The carrier has four main privacy toggles: 

• Personalized Plus:  This setting "uses data like web browsing and precise location for customization" including for offers and ads. This data includes your precise location, web browsing, viewership history, apps as well as data AT&T collects from advertisers like demographic information, ZIP code and age ranges. The carrier says this data "does not access or use the contents of your texts, emails or calls." 
• Personalized:  This option "allows the use of your data for automated decision making" including using AI to generate more customized ads and marketing tailored and "relevant to your interests." It's on by default. 
• Identity Verification: This "allows us to help non-AT&T companies perform identity verification and fraud prevention," such as a bank to "help protect your accounts from fraud, verify your identity and make sure you authorize certain transactions." AT&T says it does not let companies use this verification "for any other purpose than those services." This is on by default. 
• Share or sell my personal information: This lets AT&T "share limited information to create, deliver and measure advertisements for things you might like" including targeted ads. This is on by default. 

Of the four, you can easily toggle off all settings, though I would recommend keeping identity verification on for all lines on your account.

T-Mobile's privacy options are a bit more varied. To access the company's Privacy Center , log into your T-Mobile account and then click My account in the upper right corner, followed by Profile . From there scroll all the way to the bottom and select privacy and notifications and privacy dashboard . 

From there you will see the following: 

• Share data for public and scientific research:  T-Mobile says that this option allows it to "help support research initiatives for the public good, such as pandemic response and the development of new technologies." Created last year, the carrier says this program is in response to requests from "outside researchers or researching institutions." T-Mobile says this data will be "de-identified as much as possible before being shared" and will not include personal information such as your name, though it can share location, demographic and usage data. It also says that data shared here won't be given to law enforcement.    
• Analytics and reporting: This takes data from your device, including app usage information such as how often it was opened and zip code and combines it with "self-declared data like age range (e.g., 25 to 34) and gender, to prepare aggregated business and marketing reports," which T-Mobile can then use and share with other companies. The carrier says that these reports do not identify individuals. This is on by default. 
• Advertising options: This is for personalized ads , with the carrier collecting details about apps you download and how long you've used them, combining it with other data it collects such as your location and demographic. As with similar options from other providers and sites, turning this off doesn't stop you from seeing ads but the carrier says that you are "more likely to see ads about things you like" if you keep these options on. This is on by default. 
• Product development: This lets T-Mobile use your personal data, including your precise location, phone numbers you call, apps and websites you use, as well as helping advertisers "make the ads they show you better."
• Profiling and automated decisions: This is T-Mobile's seemingly latest privacy option that we mentioned above, and while the carrier says it isn't doing anything with this today, it appears to be giving itself the option to do so going forward. On its site it describes this as taking personal data it collects about you "to evaluate, analyze, or predict certain personal aspects about your performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements." It is on by default. 
• Do not sell or share my personal information:  This allows you to tell T-Mobile not to sell or share the data it has about you with other companies. The company notes on its site that it still may share "some personal information with our service providers so you can, for example, receive products and services from us." In an annoying extra step, the carrier notes on its website that you can limit what data third-party advertisers share with T-Mobile through its Magenta Advertising Platform, but to opt out of that you need to download a separate app .

Of these options, I would recommend turning all of them off. 

In addition to the dashboard, T-Mobile's Privacy Center website  details something called "phone privacy." T-Mobile says that it uses information gathered from here to "identify offers for T-Mobile calling plans, protect against fraud, and respond to emergencies" but that under this policy it is not collecting "your name, address, phone number or the content of your phone calls." It also says it's not sharing this data with other companies so that it could "help them market their stuff." 

Verizon 

Like T-Mobile, Verizon has a host of different options when it comes to privacy. This can be found by logging in, clicking on account then account overview . From there, select edit profile and settings and choose manage privacy settings . 

• Customer Proprietary Network Info:  This lets Verizon use the information it has about you to sell you other services beyond whatever you're already paying for. This is on by default. 
• Business and Marketing Insights:  This takes information about location, web browsing, "app/feature use" as well as your demographic and then combines that with information Verizon gets from other companies to help Verizon create "insights" like if many people are often traveling to a particular place at a particular time. The carrier says this information is collected "in aggregate" so that it can help it and "others better understand consumer actions." This is on by default. 
• Custom Experience: This takes information about "websites you visit and apps you use" to "help us better understand your interests" such as if you like sports or gaming. Verizon says it does not share or sell this information with others. It's on by default. 
• Custom Experience Plus: This combines the information gathered from the CPNI and Custom Experience sections so that Verizon can "personalize" its marketing and recommendations for you as well as "develop plans, services and offers" that would be "more appealing to you." This is an opt-in and the carrier says that it doesn't sell this data to other companies for their own respective advertising, though opting in here will automatically enable the Business and Marketing Insights . 
• Identity Verification:  This shares "certain account, device and profile data" with third parties to help "verify your identity and help protect you against identity theft and account takeover." This is on by default. 

There is also a Custom Experience tab that lets you reset your Custom Experience and Custom Experience Plus options, and doing so will have Verizon "stop using the web browsing and location data" that it previously collected as part of the program. 

Of all of Verizon's options, I would recommend turning off all but Identity Verification. That should remain on. 

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How 5 N.Y.C. Neighborhoods Are Struggling With Climate Change

New data projects are linking social issues with global warming. Here’s what that means for these New York communities.

By Hilary Howard

Photographs by Jade Doskow

Some of the effects of climate change on New York City neighborhoods are clear: extreme heat. Persistent flooding.

But as city leaders explore which neighborhoods are most vulnerable to a warming world, they are also focusing on less obvious factors like poverty, chronic health conditions and language barriers that can deepen the impact of climate change.

Several new data-gathering efforts are helping shed light on how socioeconomic issues can add to a community’s overall risk as droughts, floods and wildfires become more extreme and sea levels rise.

The findings indicate that in the city, the neighborhoods most unprepared for climate change have a lot in common: They are poor; have congestion and histories of redlining or industrial pollution; and for many of their residents, English is a second language.

“You find these same situations in all these locales: very little tree covering, heavily exposed pollutants and projects and industry that’s been zoned to be placed there,” said Mychal Johnson, a founding member of the nonprofit South Bronx Unite , which helped develop the U.S. Climate Vulnerability Index , an expansive mapping project that compiled public data from across the country.

And in April, the New York City Mayor’s Office of Climate & Environmental Justice published a similar project and interactive map .

Using these tools and other similar indexes, here are some of the most vulnerable regions in the city.

‘A very vicious cycle’

Congestion in the south bronx.

The Cross-Bronx Expressway cuts off the South Bronx from the rest of the borough, with cars and trucks — over 187,000 daily — spewing pollution.

The construction of the thoroughfare in the middle of the last century displaced 60,000 residents and helped condemn much of the area around it to poverty, as well as elevated rates of asthma.

Disproportionate levels of health consistent with high levels of poverty make climate change harder on residents of the South Bronx, said Earle Chambers, an epidemiologist at the Albert Einstein College of Medicine.

Extreme heat, a major issue in the South Bronx , is especially tough on those with chronic illnesses. And New Yorkers with asthma were in danger last summer when wildfires in Canada turned the skies red over New York. Those with financial hardships were further challenged, visiting emergency rooms — a guaranteed way to seek treatment regardless of income or insurance — in record numbers.

In the South Bronx, where 94 percent of residents are Black or Hispanic, the percentage of residents living below the poverty level is about twice the city average, as is the percentage of adults 25 and over who did not graduate from high school, according to a census analysis of neighborhoods in the South Bronx region, including Grand Concourse, Melrose, Mott Haven, Point Morris and Hunts Point, by Social Explorer , a demographic data firm.

Adult asthma rates in the South Bronx are significantly higher than the city average — 6 percent compared with 3.8 percent citywide — and over a third of residents are obese and considered to be at risk for diabetes and heart disease.

Living near a congested highway can produce a domino effect of challenges, said Arif Ullah, the executive director of South Bronx Unite.

“If a child has asthma or heart disease, there are more absences from school, which means a risk of not graduating, which could affect job prospects,” Mr. Ullah said. “It’s just a very vicious cycle.”

Ritchie Torres, the Democratic congressman who represents the area, along with Senator Chuck Schumer, Democrat of New York, secured $2 million for the city and state to study covering parts of the expressway with parks and other amenities.

Such a project would help “right the historical wrong” of the expressway being built in the South Bronx to begin with, Dr. Chambers said.

‘Trees as a high-leverage solution’

The lack of forest cover in red hook, brooklyn.

Red Hook, an isolated, low-lying waterfront neighborhood, still affected by an industrial history and by emissions from a nearby cruise ship terminal , also has a shortage of trees.

In 2012, hundreds of trees were felled or damaged by Hurricane Sandy, which flooded the area and knocked out the power and water at the Red Hook Houses, New York City’s second-largest public housing complex. In order to make repairs there , officials cut down about an additional 400 trees.

Trees serve as a buffer for storm water, filter the air, provide oxygen and store carbon dioxide. In addition to shading people, they also shade buildings, which helps reduce energy consumption.

But trees struggle to thrive in Red Hook. The water table is high, meaning the ground is often saturated, and most of the soil is red clay, which can be dense, making it difficult for trees to take root.

(NYC Parks, which is behind a citywide tree-planting and maintenance effort , has planted 565 trees in the neighborhood since 2015, and intends to plant 40 more this spring.)

Some residents have taken it upon themselves to nurture the street trees. Red Hook Conservancy , a nonprofit, organizes groups to clean out tree beds and nourish them with mulch or compost.

Students are doing their part, too. Six graders at nearby Harbor Middle School undertook a project to design and build guards to protect tree beds.

Lynn Shon, a teacher at the school, led the project. “Students looked at data and discovered that flooding, sea level rise and extreme heat were problems disproportionately impacting Red Hook, along with the urban heat island effect ” (when cities tend to be warmer than rural areas), she said. “They were able to identify trees as a high-leverage solution.”

A food desert, surrounded by water

Edgemere, queens, lacks fresh produce (but has plenty of flooding)..

Shantae Johnson moved to Edgemere five years ago because of the cheap rent, she said. Ms. Johnson, a single mother, was on a tight budget, which revolved around feeding her seven children.

She soon realized there were no grocery stores in the flood-prone neighborhood. In Edgemere, a beach community, a simple chore like food shopping is already a major operation. But as flooding becomes more commonplace, navigating the waterlogged areas makes the task even more onerous.

“We have the double whammy effect,” said Sonia Moise, president of the area’s civic association , referring to flooding from two directions: the Atlantic Ocean to the south and Jamaica Bay to the north.

Every week, Ms. Johnson would lug her shopping cart onto the subway and travel from the Rockaway Peninsula in southern Queens to Union Square in Manhattan (over an hourlong trip) to do her grocery shopping.

“It took a toll on me,” she said.

But two years ago, Ms. Johnson caught a break. She stumbled upon a community garden during a walk. Soon, she had her own patch of land and was growing spinach and basil. She harvested so much squash last summer that she filled her freezer and gave away the rest.

The garden changed her life, Ms. Johnson said. “I get friendship, community, food and an oasis,” she said.

The Garden by the Bay is a precious resource in amenity-poor Edgemere, where the closest grocery store is over a mile away, Ms. Moise said.

The food desert here is just one problem, said Jackie Rogers, the president of the 15,000-square-foot garden, which has five community plots and 23 for individual use. “We check all the boxes when it comes to deserts,” she said. “Food, transportation, education, recreation, lack of infrastructure.”

On the food front, there is some good news: This fall, a 20,000 square-foot grocery store is scheduled to open. It will be part of a mixed-use affordable housing complex with over 2,000 apartments.

Ms. Rogers would like to see more amenities and infrastructure upgrades — like more raised streets — first. “I’m sounding the alarm,” she said. “We need resiliency here.”

A need for English classes and information

In throgs neck, the bronx, big demands on a little library.

During extreme weather, staying informed is key to staying safe. But for New Yorkers who do not speak English or lack internet services, doing so can be a challenge.

Public libraries can help. And in the event of a storm or flood, many libraries go into disaster relief mode, becoming communications hubs and distribution centers for clothing, food and medicine.

“Librarians are always collaborating to connect people to resources, that’s what we do,” said Emily Drabinski, president of the American Library Association.

But in Throgs Neck, an isolated community with little public transit, there is just one library for tens of thousands of people. The Throgs Neck Library, housed in a squat one-story building in the poorest part of the neighborhood just off the Cross Bronx Expressway, offers limited services.

Yet the need is there, said Leida Velazquez, the branch manager. Over the past year, she has seen an increase in patrons using the computers, as well as requests for assistance in applying for identification cards, jobs and food stamps benefits. “I’ll print applications for them,” she said.

With the recent influx of migrants, there is also a strong demand for English classes at the branch. But the building is too small to offer them, Ms. Velazquez said, so she often refers people to the Bronx Library Center. Getting there requires two buses and takes over an hour.

The demand for library services and support in this area of Throgs Neck underscores its need. According to Social Explorer, nearly a third of residents in the census tract closest to the library are below poverty level. And about one out of four residents has no other computing device besides a smartphone. Nearly half of people 5 and older speak a language other than English at home.

Across the city, budget cuts have caused many branches to make do with skeletal staffs and outdated HVAC systems, which could hamper their ability to function as cooling centers , said Lauren Comito, the executive director of Urban Librarians Unite. And more cuts could be on the way .

“If we want libraries to prepare for climate disaster, we will need more funding and to train staff,” she said.

‘Volatile and Dangerous’

A legacy of toxins in east williamsburg and greenpoint, brooklyn.

In the late 19th century, more than 50 oil refineries sat on the banks of Newtown Creek, a 3.8 mile waterway between Brooklyn and Queens. Now, the Brooklyn side of the creek is home to one of the largest oil spills in American history, and of two of the city’s four Superfund Sites (areas so toxic they qualify for government intervention).

But for Willis Elkins, the executive director of Newtown Creek Alliance , an environmental nonprofit, the most urgent threat to the area is a 117-acre storage facility.

There, two large tanks store liquefied natural gas, which can be converted to fuel for heating during cold-weather emergencies. “Liquefied gas is incredibly volatile and dangerous to store and transport,” Mr. Elkins said.

“The liquid gas is not even 1,000 yards from where we live,” said Elisha W. Fye, the vice president of the resident council of Cooper Park Houses, a public housing complex that sits next to the site.

Area residents have concerns about groundwater flooding people’s homes with toxins. Remnants of coal tar , a substance that was used when the site was an oil refinery, still bubble up at low tide, said Mr. Elkins, who added that other chemicals have also been detected around the site, which sits in a flood zone.

Mr. Fye, 70, has been part of several successful community efforts to block upgrades to the site, which is owned by National Grid, a company that provides gas to 1.9 million customers in New York City and on Long Island.

Several activists and energy experts want the site to shut down. But National Grid maintains that the site provides energy reliability in the event of extreme weather, and that the Greenpoint facility “meets or exceeds all safety regulations,” Karen Young, a spokeswoman for the company, said.

National Grid is investing millions in a new fire suppression system for the site; its old one was flooded and destroyed during Hurricane Sandy. And it is seeking millions more in proposed rate hikes for other upgrades.

If approved, residents in Brooklyn, Queens and Staten Island could see their monthly heating bills increase by more than $65 by 2026, and local gas infrastructure would remain in place well into the 2080s, which is against the state’s climate goals, said Kim Fraczek, the director of the Sane Energy Project , a group that has helped shut down several of National Grid’s expansion efforts.

Ms. Young said that most of the revenue from increased rates would cover federal and state safety mandates .

But Ms. Fraczek would like to see a more specific accounting, she said. “It’s an economic issue, it’s an environmental justice issue.”

Hilary Howard is a Times reporter covering how the New York City region is adapting to climate change and other environmental challenges. More about Hilary Howard

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Have questions about climate change? Our F.A.Q. will tackle your climate questions, big and small .

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