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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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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 storytelling with data, 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!

Sign up for our free trial or book a demo !

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Blog Data Visualization

10 Data Presentation Examples For Strategic Communication

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.

10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 Apr 2024 • 11 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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Presenting about data to your board: 6 tips from experts

Dylan Walsh

Sep 6, 2022

A strong data strategy is essential to be competitive. Companies refer to data nearly 80% more often in annual reports than they did in 2017, according to a recent report . And roughly half of companies surveyed had hired a chief data officer in the last two years — someone at the C-Suite level or just below who is responsible for the company’s strategic approach to data.

“Data is increasingly an asset that has both value and risk,” said Maria Villar, head of enterprise data strategy and transformation at German software company SAP.  Given the rapidly growing strategic importance of data, it is critical that CDOs not only do their job well but communicate effectively about their work. “Having an effective data strategy and then communicating it to important constituents, like your executive board, is a key to success,” Villar said.

At the recent MIT Chief Data Officer and Information Quality symposium , Villar moderated a panel featuring Ellen Nielsen, CDO at Chevron, and Denise Letcher, executive vice president and CDO at PNC Bank, discussing ways to craft a presentation about data strategy. The panelists stressed preparing early, catering data presentations to reach different audiences, and the importance of connecting to key business goals and telling compelling stories.

While the discussion focused on communication with boards, the key takeaways pertain to any set of important stakeholders.

Be prepared

Start early, said Letcher, who begins work on her annual business updates two months in advance. She begins by reviewing past presentations; Letcher has been PNC’s chief data officer for nearly seven years. “I have themes that I know the board likes to hear about,” she said. “I want to make sure I carry those forward.”

She also works closely with other teams to refine different dimensions of the presentation. Her boss provides “invaluable feedback” on the high-level topics; her managers review the content; the communications team helps her create a strong executive-level presentation.

Beyond the specific content of one’s own presentation, Nielsen pointed out the value of knowing where she fits during the meeting. Who is scheduled to present before and after? What might be the general mood of the meeting based on the topics under discussion?

“Typically, there are certain people who are preparing the content on the agenda, and they know very well what’s going on that day,” Nielsen said. She suggested finding this person and getting as much information as you can — it’s good to know what’s on the mind of the board members as you go in to talk.

CDOs must also stay abreast of salient issues beyond the company’s borders: How does data management fit with potential changes on the horizon? Letcher noted that board members, who tend to be active news consumers, often inquire about how CDOs are using their role to respond to industry shifts. Letcher, for instance, is keenly attuned to the overlap between her role and upcoming climate regulation.

Finally — it almost goes without saying — “practice, practice, practice,” Letcher said. Run through the presentation alone; test it on select groups for feedback. Be sure you have confidence in both your prepared remarks and your ability to answer questions.

Tell stories with broad relevance

It is important to connect the work of data and analytics to larger business objectives, Letcher and Nielsen said. Audiences like a board of directors are typically not interested in the details of specific projects or processes, and they don’t need to know what a CDO has been doing day-to-day or month-to-month. Rather, they care about outcomes — how the application of data and analytics is advancing business objectives.

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“You want to step back and say, ‘How is data helping the overall company?’” Letcher said. If one slice of data proved essential to a recent merger and acquisition, for instance, then tell that story and clarify the value that is generated by good data. You want to explain how data is enabling the business strategy, she said.

As the audience for these presentations moves deeper into the organization — from the board, to leadership, to lower managers — the need for detail increases. The outcomes also grow more specific: from top-level business strategy to how data and analytics are supporting a particular unit or function. Budgetary questions and financial details become more relevant.

Competitiveness is another important topic of discussion, Nielsen said. When describing the strategic role of data within your company, benchmark these descriptions against competitors. What are you doing better? In what ways do you need to catch up? When describing areas for improvement, be sure to outline the most effective levers of investment.

Regardless of audience, Nielsen and Letcher highlighted the importance of stories and anecdotes. “I put a lot of effort in [finding] the right stories to share,” Nielsen said. “I look for stories that tell about new things, or where the organization tried something new and really overcame an obstacle and created tremendous value. These are the best.”

Don't forget the finer points

Alongside big picture issues of how to prepare for and organize a presentation, Nielsen and Letcher provided tips on the fundamentals, from how to frame the conversation to how many slides to create.

• Be explicit about why you’re there. If you’re there to provide an update, say that. If you’re there to ask for approval, say that. Nielsen suggested that if you’re there for an “ask,” give the board options rather than asking for a single outcome.
• Assume 10 to 20 minutes for the key messages. This means on the order of 6 – 8 slides. Present an executive summary with the main points first. And, whatever you do, don’t read from the slides. That’s “the kiss of death,” Letcher said.
• Prepare for questions . Think about the questions you may get in advance. Have dates in mind so that you can speak to chronology. Don’t be afraid to ask for more time if you don’t have an answer: “I’ll get back to you,” is a perfectly fair response.
• Don’t use acronyms. If you absolutely need to use one, define it first.

It’s also important to remain confident — even if people come and go or appear distracted. “Recognize that you are the subject matter expert,” Letcher said. “They rely on you.”

Read next: The next chapter in analytics is data storytelling

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How to Create Slides That Suit Your Superiors: 11 Tips

When you’re pitching ideas or budgets to execs in your organization, you need to deliver slides that fit those particular people just right. This checklist identifies the key considerations.

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I recently interviewed 20 of my customers, all in senior roles at Fortune 100 companies, and asked them their biggest pain point in presenting to higher-ups and even colleagues. What I heard consistently was that it can feel like Goldilocks bouncing from one option to the next, testing to figure out what’s “just right.” Does the audience want deep reports? Sparse slides? Something in between? Like … what?

Teams often come to presentation meetings with vast amounts of backup content just in case an exec wants to take a deep dive on any given point. There’s often a struggle to anticipate every direction attendees might want to go. It’s frustrating, and it’s not efficient.

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There are many ways to build slides. I’m not just talking about crafting them well versus poorly. I’m talking about all of the important decisions regarding how to organize them, how much text to use, when to lean into a chart, the best ways to use bullets and color, and whether to include an appendix with additional information. Before you make your next proposal or request of the executive team, use this list of 11 tips for your next set of slides as a guide.

Four Things You Must Have in Every Exec’s Slides

Before we drill down into the harder aspects, the ones where your executives’ tastes may vary widely, let’s quickly cover four aspects that you can consider the building blocks — the basics you should never proceed without.

Start with an executive summary. Begin the slide deck with a tight executive summary that follows a three-act structure. First, start with stating the current realities. Second, clearly state the problem or opportunity your idea addresses and its potential impact. Third, explain how your recommendation solves the problem or exploits the opportunity and the next steps you’re proposing.

Have a logical organization. The arc of the deck — the package from beginning to end — should make sense. If your audience reads only the headline of every slide, the order should be coherent and make most of the case for you. The content below each slide’s headline must support the statement made in the title. Remove everything that doesn’t support your point; as writers will tell you, you sometimes need to “kill your darlings” when you’re editing.

Begin the slide deck with a tight executive summary that follows a three-act structure.

Make it skimmable. Help your audience to quickly grasp the point without getting bogged down in details. Create a clear visual hierarchy. Guide the reader’s eye through the content: Use bold headings, bullet points, and numbered lists to break down information into digestible pieces. Highlight key takeaways or conclusions in a different color or font size to draw attention to these critical points.

Focus on concise insights. Succinct statements with clear insights are everyone’s jam. Every slide should serve a purpose and contribute directly to the decision-making process. Distill complex information. Don’t use 100 words when 20 words will nail it. If you’re having difficulty trimming, consider using company-approved AI tools to help you take out the fluff.

Five Preferences to Confirm With the Person You Want to Reach

Now we’ll delve into what your particular audience does and does not want. If you haven’t yet, start by asking the person you’re presenting to what they generally prefer. They probably know themselves well but have not been asked to articulate how they like to receive information.

Ask how dense is too dense. Some executives prefer detailed slides with comprehensive data. Others favor a more high-level approach. You’re weighing how to balance informative content with readability, ensuring that slides are not overloaded yet are sufficiently detailed to support decision-making.

Confirm the delivery format and timing. Some execs like information presented to them. Others prefer a pre-read of the material followed by a discussion. I always recommend our tool Slidedocs (I’ve written a free e-book on them), which are visual documents using both words and images. The templates help presenters organize their thoughts into a document for a pre-read or a read-along. They are designed to be skimmable and able to travel through your organization without the help of a presenter.

I’m a huge fan of pre-reads and prefer to use my time in meetings to ask questions and build alignment. If your audience didn’t review your material in advance, ask at the top of the meeting whether they would like you to present it or would prefer to read through it and then discuss it.

Find out how much data visualization they prefer. Charts, graphs, photos, and illustrations often communicate complex data more clearly than words alone. When execs can see what you’re saying, they often can better understand the impact of your idea. Does the exec want to understand exact numbers? Bar charts allow them to move their eyes across a series of specifics. Does the exec want to know the shape of a trend over time? Line charts can show the pattern. (See “Classic Charts Communicate Data Quickly.”) Some prefer charts with annotations that draw attention to what you think is the most important point. Others want to make their own conclusions from the data.

One of my clients, the CEO of a massive commercial real estate company, doesn’t want anything visualized. He prefers numbers, only in a table, and only in two colors — black and red. You might think this is archaic. But the fact that he’s clear to his teams about what he wants takes all the mystery out of how to communicate with him.

When the stakes are high, have a conceptual thinker help with diagrams and concepts. If you don’t have one on your team, and when it’s high stakes, find an internal designer to help you or hire one. You can’t afford to have the baby (your idea) thrown out with the bathwater (terrible slides).

Identify which details need spelling out. How well do the people you’re presenting to know the landscape and function of the company and products you’re talking about? For example, if your engineering team threw a slide into a deck about an issue that requires executive approval, do the execs all speak geek? Or do you need to explain the technology so that they will really understand the ask? Either eliminate internal jargon and acronyms or unpack those bits, especially if your proposal deeply involves expertise outside of the executives’ domain.

Ask whether appendices will be useful. When you’re organizing a presentation, you often troll data, read through complicated reports, and even hire external experts to figure out what’s best for the company. Do your execs want access to that supporting data? You can add a document to the end of the presentation as an appendix to show all of the data and source material. This allows the main content of the slides to remain focused and accessible while still providing comprehensive background information for those who want more.

Two Tips to Improve Your Presentation Skills

Getting materials in place is the biggest step. They will be your best tools for selling your ideas. But there are two extra areas to pay attention to as a presenter: how you handle questions and how you use every experience to improve.

Anticipate questions, and practice your answers. Before you have your meeting, gather a small team to challenge every point you make. Invite colleagues you trust to role-play as “a rapidly inquisitive exec” or “the doubting naysayer exec” so you are prepared to present your idea well. They’re gonna grill you, and practicing will help you remain unruffled when it happens.

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Ask for feedback after the presentation. Establish a feedback loop with those you presented to. Ask what worked well and how you can improve. If attendees don’t have the time, find people who have had their ideas funded and talk to them about what they did that worked. Advice and some perspective will help you nail your performance even better next time.

Empathetically understanding your audience members and how they process information, whether it’s executives or peers, sets up your ideas for success. Clarity creates efficiency. When a presentation fits just right, you’ve given your great thinking the best chance of moving through your organization and having maximum impact.

About the Author

Nancy Duarte is CEO of Duarte Inc. , a communication company in the Silicon Valley. She’s the author of six books, including DataStory: Explain Data and Inspire Action Through Story (Ideapress Publishing, 2019).

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Three-tier architecture is a well-established software application architecture that organizes applications into three logical and physical computing tiers: the presentation tier, or user interface; the application tier, where data is processed; and the data tier, where application data is stored and managed.

The chief benefit of three-tier architecture is that because each tier runs on its own infrastructure, each tier can be developed simultaneously by a separate development team. And can be updated or scaled as needed without impacting the other tiers.

For decades three-tier architecture was the prevailing architecture for client-server applications. Today, most three-tier applications are targets for modernization that uses cloud-native technologies such as containers and microservices and for migration to the cloud.

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Presentation tier

The presentation tier is the user interface and communication layer of the application, where the end user interacts with the application. Its main purpose is to display information to and collect information from the user. This top-level tier can run on a web browser, as desktop application, or a graphical user interface (GUI), for example. Web presentation tiers are developed by using HTML, CSS, and JavaScript. Desktop applications can be written in various languages depending on the platform.

Application tier

The application tier, also known as the logic tier or middle tier, is the heart of the application. In this tier, information that is collected in the presentation tier is processed - sometimes against other information in the data tier - using business logic, a specific set of business rules. The application tier can also add, delete, or modify data in the data tier. 

The application tier is typically developed by using Python, Java, Perl, PHP or Ruby, and communicates with the data tier by using  API  calls. 

The data tier, sometimes called database tier, data access tier or back-end, is where the information that is processed by the application is stored and managed. This can be a  relational database management system  such as  PostgreSQL , MySQL, MariaDB, Oracle, Db2, Informix or Microsoft SQL Server, or in a  NoSQL  Database server such as Cassandra,  CouchDB , or  MongoDB . 

In a three-tier application, all communication goes through the application tier. The presentation tier and the data tier cannot communicate directly with one another.

Tier versus layer

In discussions of three-tier architecture,  layer  is often used interchangeably – and mistakenly – for  tier , as in 'presentation layer' or 'business logic layer'. 

They aren't the same. A 'layer' refers to a functional division of the software, but a 'tier' refers to a functional division of the software that runs on infrastructure separate from the other divisions. The Contacts app on your phone, for example, is a  three - layer  application, but a  single-tier  application, because all three layers run on your phone.

The difference is important because layers can't offer the same benefits as tiers.

Again, the chief benefit of three-tier architecture is its logical and physical separation of functionality. Each tier can run on a separate operating system and server platform - for example, web server, application server, database server - that best fits its functional requirements. And each tier runs on at least one dedicated server hardware or virtual server, so the services of each tier can be customized and optimized without impacting the other tiers. 

Other benefits (compared to single- or two-tier architecture) include:

• Faster development : Because each tier can be developed simultaneously by different teams, an organization can bring the application to market faster. And programmers can use the latest and best languages and tools for each tier.
• Improved scalability : Any tier can be scaled independently of the others as needed.
• Improved reliability : An outage in one tier is less likely to impact the availability or performance of the other tiers.
• Improved security : Because the presentation tier and data tier can't communicate directly, a well-designed application tier can function as an internal firewall, preventing SQL injections and other malicious exploits.

In web development, the tiers have different names but perform similar functions:

• The web server  is the presentation tier and provides the user interface. This is usually a web page or website, such as an ecommerce site where the user adds products to the shopping cart, adds payment details or creates an account. The content can be static or dynamic, and is developed using HTML, CSS, and JavaScript.
• The application server  corresponds to the middle tier, housing the business logic that is used to process user inputs. To continue the ecommerce example, this is the tier that queries the inventory database to return product availability, or adds details to a customer's profile. This layer often developed using Python, Ruby, or PHP and runs a framework such as Django, Rails, Symphony, or ASP.NET.
• The database server  is the data or backend tier of a web application. It runs on database management software, such as MySQL, Oracle, DB2, or PostgreSQL.

While three-tier architecture is easily the most widely adopted multitier application architecture, there are others that you might encounter in your work or your research.

Two-tier architecture 

Two-tier architecture is the original client-server architecture, consisting of a presentation tier and a data tier; the business logic lives in the presentation tier, the data tier or both. In two-tier architecture the presentation tier - and therefore the end user - has direct access to the data tier, and the business logic is often limited. A simple contact management application, where users can enter and retrieve contact data, is an example of a two-tier application. 

N-tier architecture

N-tier architecture - also called or multitier architecture - refers to  any  application architecture with more than one tier. But applications with more than three layers are rare because extra layers offer few benefits and can make the application slower, harder to manage and more expensive to run. As a result, n-tier architecture and multitier architecture are usually synonyms for three-tier architecture.

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Was this page helpful, estimating county-level regional price parities from public data.

The idea that prices differ by place is a well-known economic concept. Most consumers could easily identify that a day in New York City would cost more than an identical day in rural Texas. To quantify these price differences, the Bureau of Economic Analysis (BEA) publishes regional price parities (RPPs) . By using a weighted average of goods and services across places in a given year, RPPs allow place-to-place price comparisons and are available for all 50 states, the District of Columbia and 384 metropolitan areas. For example, RPPs show that goods and services in the most expensive state, California, cost nearly 30% more than in the least expensive state in 2022.

The ability to see these price differences at finer levels of geography may be useful for people making decisions about policy, planning, and growth. To further these goals, a new OUSEA working paper  documents the construction of experimental county-level RPPs based on publicly available data. In addition, a new experimental data set  with these county-level estimates are also now available. This novel data set is not a government statistical product nor is it meant to be interpreted as one. Instead, the data and methodology are meant to harness existing public data to explore how price-related economic experiences may differ at the county-level. The working paper  details the full methodology; this blog describes top-level findings from the experimental data set.

Expensive counties tend to be in metropolitan areas

Like BEA’s official RPP data, these experimental RPPs are meant to average to 100. RPPs above or below 100 are more or less expensive than the national average.

Ranking all counties from most expensive to least, 8 of the top 10 most expensive counties are in a metropolitan area as seen in Table 1. While cities are typically more expensive than their suburban or rural counterparts, prices in these counties are substantially high with New York, NY (Manhattan) leading at 32.6% above the national average. Yet of these 10 counties, they tend to be in the same metropolitan areas. Seven of the ten are in the New York, San Francisco or Washington, DC metropolitan areas. When extending to the top 20 most expensive counties, 13 counties are in these same three metropolitan areas.

Using the experimental RPPs to show price differentials within metros

Defined by the U.S. Office of Budget and Management, metropolitan areas are urbanized areas with at least 50,000 people along with a high degree of social and economic integration and commuting ties. Understandably some metropolitan areas can be quite large, which can lead to substantial price differences between counties in the same metropolitan area. This gap between a metro’s most expensive and least expensive county can be understood as a price differential; in other words, how much more expensive is the most expensive county compared to the least expensive.

As seen in Table 2, the largest price differentials within metropolitan areas are notable. For example, among counties in the Washington, D.C. metro area, Arlington County, VA, is 37% more expensive than Madison County, VA. Most metropolitan areas have smaller price gaps than this; 86% of metro areas have a price differential of less than 10% between their most and least expensive counties. However, there are 54 metros with a price differential of 10% or more.

Top non-metropolitan area price differentials

Price differentials also exist in states’ non-metropolitan counties. Table 3 compares the most and least expensive non-metropolitan counties within a state. Numerous factors can drive a state’s non-metro county price variation; one county may have luxury resorts while another, hundreds of miles away, could be agriculture-heavy. What is clear is that these price differentials highlight non-metro residents within the same state may face substantially different price experiences.

Some counties with notable resort towns appear among the top 5 largest differentials, as seen in Table 3. Monroe County, FL, includes Key West, and Summit County, CO, has multiple ski towns. Leelanau, MI, is located on the shore of Lake Michigan and has a variety of summer home rentals and wineries. Of all the states, Massachusetts has the smallest non-metro price differential at 3.7%. Dukes County, MA, and Nantucket, MA, are the only two Massachusetts counties not in a metropolitan area, are geographically close, and are both popular vacation spots.

What it means and what’s next

The experimental estimates suggest there is substantial county-level price variation both within metropolitan areas and across non-metro counties. This illustrates that residents within the same metropolitan area may face considerably different prices and thus may have different purchasing power. Similarly, residents of non-metros counties face varying price differentials. For example, one non-metro county may have high housing costs due to being a resort town, another may have low housing costs, but expensive goods given its remote location.

This experimental dataset  can have a variety of research applications to further understanding of how economic conditions vary across metro/non-metro counties and within metropolitan areas. From calculating county-level cost-of-living adjusted wages to grouping counties by similar price profiles, this dataset may be able to aid research on important and challenging questions.

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At michigan state university, frib professorial assistant earns goldwater scholarship.

Aaron Philip, a professorial assistant at FRIB, has earned a  Barry M. Goldwater Scholarship , becoming Michigan State University’s fifty-fifth Goldwater Scholar.

The Goldwater Foundation seeks sophomores and juniors committed to a research career in STEM fields with the potential for significant future contribution in their chosen field. The award provides $7,500 per year in funding for 51 students for undergraduate tuition and living expenses.

For the 2024 Goldwater Scholarship competition, 1,353 undergraduates were nominated by 446 institutions. Philip was among 438 scholars selected. The funding for the award is a collaboration between the U.S. Congress and the Department of Defense’s National Defense Education Program.

Philip is a second-year Michigan State University student from Los Alamos, New Mexico studying Physics and Advanced Mathematics in the College of Natural Science. He is also a member of the Honors College.

“I am honored and humbled to join the ranks of Spartan Goldwater Scholars. I share this recognition with my professors, research mentors, fellow students, and family who have all supported me and cultivated my passion to pursue a career in physics research,” Philip said. “Specifically, I would like to thank my research mentors over the past few years for their guidance, encouragement, and mentorship: Drs. Pablo Giuliani, Kyle Godbey, Witek Nazarewicz, Odelia Schwartz, Jianliang Qian, and Benjamin Nebgen.”

Philip is passionate about pursuing a career in research addressing micro-scale physics problems using analytic approaches, high performance computing, and AI. He has contributed to diverse research projects through his roles as a professorial assistant at FRIB, a Discovering America researcher with MSU’s Math Department, a student intern at the Theoretical Division of Los Alamos National Laboratory (LANL), and as a Computer Science Research Experience for Undergraduates (REU) student at the University of Miami.

“Aaron joined our nuclear theory research group at the Facility for Rare Isotope Beams at Michigan State University in August 2022 as an undergraduate research assistant. An incoming first-year undergraduate student, he came extremely well prepared to directly work in forefront research and quickly managed to get acquainted with the necessary tools and background knowledge,” Kyle Godbey, a research assistant professor at FRIB, and Witold Nazarewicz, John A. Hannah Distinguished Professor of Physics and chief scientist at FRIB, said.

“During the course of his work, Aaron was able to reach a level of mastery of theoretical and computational methods on par with the current experts in the field. We consider ourselves to be incredibly lucky to have Aaron as a member of our research group and we have no doubt that he will go on to have a successful research career,” Godbey and Nazarewicz said.

“Aaron’s research at the Facility for Rare Isotope Beams has been exemplary, and his mentorship activities embody the values of care and support that empower excellence at the MSU Honors College. We congratulate Aaron on being named a Goldwater Scholar,” Long said.

Philip has written two papers and presented at various conferences, including MSU’s Mathematics and Data Science Conferences, the University of Miami’s Computer Science REU Poster Presentation, and a LANL Lab Directed Research and Development Review. He also serves as a student tutor through the Mathematics Learning Center and at East Lansing High School.

“Congratulations to Aaron on this esteemed achievement,” said FRIB Laboratory Director Thomas Glasmacher. “Being named a Goldwater Scholar is a testament to Aaron’s dedication and outstanding efforts. We are so proud he is furthering his research pursuits at FRIB and honored to be part of his journey as he prepares to become a leader in our field.”

Read the original article on the  MSUToday website .

Michigan State University operates the Facility for Rare Isotope Beams as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics.

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• Published: 01 April 2024

Complexity of avian evolution revealed by family-level genomes

• Josefin Stiller   ORCID: orcid.org/0000-0001-6009-9581 1 ,
• Shaohong Feng   ORCID: orcid.org/0000-0002-2462-7348 2 , 3 , 4 , 5 ,
• Al-Aabid Chowdhury 6 ,
• Iker Rivas-González   ORCID: orcid.org/0000-0002-0515-0628 7 ,
• David A. Duchêne   ORCID: orcid.org/0000-0002-5479-1974 8 ,
• Qi Fang   ORCID: orcid.org/0000-0002-9181-8689 9 ,
• Yuan Deng 9 ,
• Alexey Kozlov   ORCID: orcid.org/0000-0001-7394-2718 10 ,
• Alexandros Stamatakis   ORCID: orcid.org/0000-0003-0353-0691 10 , 11 , 12 ,
• Santiago Claramunt   ORCID: orcid.org/0000-0002-8926-5974 13 , 14 ,
• Jacqueline M. T. Nguyen   ORCID: orcid.org/0000-0002-3076-0006 15 , 16 ,
• Simon Y. W. Ho   ORCID: orcid.org/0000-0002-0361-2307 6 ,
• Brant C. Faircloth   ORCID: orcid.org/0000-0002-1943-0217 17 ,
• Julia Haag   ORCID: orcid.org/0000-0002-7493-3917 10 ,
• Peter Houde   ORCID: orcid.org/0000-0003-4541-5974 18 ,
• Joel Cracraft   ORCID: orcid.org/0000-0001-7587-8342 19 ,
• Metin Balaban 20 ,
• Uyen Mai 21 ,
• Guangji Chen   ORCID: orcid.org/0000-0002-9441-1155 9 , 22 ,
• Rongsheng Gao 9 , 22 ,
• Chengran Zhou   ORCID: orcid.org/0000-0002-9468-5973 9 ,
• Yulong Xie 2 ,
• Zijian Huang 2 ,
• Zhen Cao 23 ,
• Zhi Yan   ORCID: orcid.org/0000-0003-2433-5553 23 ,
• Huw A. Ogilvie   ORCID: orcid.org/0000-0003-1589-6885 23 ,
• Luay Nakhleh   ORCID: orcid.org/0000-0003-3288-6769 23 ,
• Bent Lindow   ORCID: orcid.org/0000-0002-1864-4221 24 ,
• Benoit Morel 10 , 11 ,
• Jon Fjeldså   ORCID: orcid.org/0000-0003-0790-3600 24 ,
• Peter A. Hosner   ORCID: orcid.org/0000-0001-7499-6224 24 , 25 ,
• Rute R. da Fonseca   ORCID: orcid.org/0000-0002-2805-4698 25 ,
• Bent Petersen   ORCID: orcid.org/0000-0002-2472-8317 8 , 26 ,
• Joseph A. Tobias   ORCID: orcid.org/0000-0003-2429-6179 27 ,
• Tamás Székely   ORCID: orcid.org/0000-0003-2093-0056 28 , 29 ,
• Jonathan David Kennedy 30 ,
• Andrew Hart Reeve   ORCID: orcid.org/0000-0001-5233-6030 24 ,
• Andras Liker 31 , 32 ,
• Martin Stervander   ORCID: orcid.org/0000-0002-6139-7828 33 ,
• Agostinho Antunes   ORCID: orcid.org/0000-0002-1328-1732 34 , 35 ,
• Dieter Thomas Tietze   ORCID: orcid.org/0000-0001-6868-227X 36 ,
• Mads Bertelsen 37 ,
• Fumin Lei   ORCID: orcid.org/0000-0001-9920-8167 38 , 39 ,
• Carsten Rahbek   ORCID: orcid.org/0000-0003-4585-0300 25 , 30 , 40 , 41 ,
• Gary R. Graves   ORCID: orcid.org/0000-0003-1406-5246 30 , 42 ,
• Mikkel H. Schierup   ORCID: orcid.org/0000-0002-5028-1790 7 ,
• Tandy Warnow 43 ,
• Edward L. Braun   ORCID: orcid.org/0000-0003-1643-5212 44 ,
• M. Thomas P. Gilbert   ORCID: orcid.org/0000-0002-5805-7195 8 , 45 ,
• Erich D. Jarvis 46 , 47 ,
• Siavash Mirarab   ORCID: orcid.org/0000-0001-5410-1518 48 &
• Guojie Zhang   ORCID: orcid.org/0000-0001-6860-1521 2 , 3 , 5 , 49  

Nature ( 2024 ) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

• Evolutionary biology
• Genome evolution
• Molecular evolution
• Phylogenetics

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method, and the choice of genomic regions 1–3 . Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous–Paleogene (K–Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K–Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

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Authors and affiliations.

Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Josefin Stiller

Center for Evolutionary & Organismal Biology, & Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng, Yulong Xie, Zijian Huang & Guojie Zhang

Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

Shaohong Feng & Guojie Zhang

Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng

Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, China

School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia

Al-Aabid Chowdhury & Simon Y. W. Ho

Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

Iker Rivas-González & Mikkel H. Schierup

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

David A. Duchêne, Bent Petersen & M. Thomas P. Gilbert

BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, China

Qi Fang, Yuan Deng, Guangji Chen, Rongsheng Gao & Chengran Zhou

Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

Alexey Kozlov, Alexandros Stamatakis, Julia Haag & Benoit Morel

Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Greece

Alexandros Stamatakis & Benoit Morel

Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

Alexandros Stamatakis

Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada

Santiago Claramunt

Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia

Jacqueline M. T. Nguyen

Research Institute, Australian Museum, Sydney, New South Wales, Australia

Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA

Brant C. Faircloth

Department of Biology, New Mexico State University, Las Cruces, NM, USA

Peter Houde

Department of Ornithology, American Museum of Natural History, New York, NY, USA

Joel Cracraft

Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA

Metin Balaban

Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

Guangji Chen & Rongsheng Gao

Department of Computer Science, Rice University, Houston, TX, USA

Zhen Cao, Zhi Yan, Huw A. Ogilvie & Luay Nakhleh

Natural History Museum Denmark, University of Copenhagen, Copenhagen, Denmark

Bent Lindow, Jon Fjeldså, Peter A. Hosner & Andrew Hart Reeve

Center for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Peter A. Hosner, Rute R. da Fonseca & Carsten Rahbek

Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Bedong, Kedah, Malaysia

Bent Petersen

Department of Life Sciences, Imperial College London, Silwood Park, Ascot, UK

Joseph A. Tobias

Milner Centre for Evolution, University of Bath, Bath, UK

Tamás Székely

ELKH-DE Reproductive Strategies Research Group, University of Debrecen, Debrecen, Hungary

Center for Macroecology, Evolution, and Climate, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

Jonathan David Kennedy, Carsten Rahbek & Gary R. Graves

HUN-REN-PE Evolutionary Ecology Research Group, University of Pannonia, Veszprém, Hungary

Andras Liker

Behavioural Ecology Research Group, Center for Natural Sciences, University of Pannonia, Veszprém, Hungary

Bird Group, Natural History Museum, Akeman St, Tring, Hertfordshire, United Kingdom

Martin Stervander

CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal

Agostinho Antunes

Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal

NABU, Berlin, Germany

Dieter Thomas Tietze

Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark

Mads Bertelsen

Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

College of Life Science, University of Chinese Academy of Sciences, Beijing, China

Institute of Ecology, Peking University, Beijing, China

Carsten Rahbek

Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark

Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA

Gary R. Graves

University of Illinois Urbana-Champaign, Champaign, IL, USA

Tandy Warnow

Department of Biology, University of Florida, Gainesville, FL, USA

Edward L. Braun

University Museum, NTNU, Trondheim, Norway

M. Thomas P. Gilbert

Vertebrate Genome Lab, The Rockefeller University, New York, NY, USA

Erich D. Jarvis

Howard Hughes Medical Institute, Durham, NC, USA

University of California, San Diego, San Diego, CA, USA

Siavash Mirarab

Villum Center for Biodiversity Genomics, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Guojie Zhang

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Corresponding authors

Correspondence to Josefin Stiller , Siavash Mirarab or Guojie Zhang .

Supplementary information

Supplementary information.

This file contains Supplementary Methods and Supplementary Results.

Reporting Summary

Peer review file, supplementary data.

Table of all sequenced species with taxonomic grouping according to Howard & Moore. 4th Edition and accession numbers of the used genome assemblies. Given as a separate tab-delimited text file.

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Stiller, J., Feng, S., Chowdhury, AA. et al. Complexity of avian evolution revealed by family-level genomes. Nature (2024). https://doi.org/10.1038/s41586-024-07323-1

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DOI : https://doi.org/10.1038/s41586-024-07323-1

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