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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 data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

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

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

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

Real-Life Application of Bar Charts

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

Step 1: Selecting Data

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

The sales manager has highlighted these products for the presentation.

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

Step 2: Choosing Orientation

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

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

Step 3: Colorful Insights

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

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

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

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

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

Real-life Application of Line Graphs

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

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

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

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

Step 3: Connecting Trends

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

Step 4: Adding Clarity with Color

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

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

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

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

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

Real-Life Application of a Dashboard

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

Step 1: Defining Key Metrics

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

Step 2: Choosing Visualization Widgets

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

Step 3: Dashboard Layout

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

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

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

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

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

Real-Life Application of a Treemap Chart

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

Step 1: Define Your Data Hierarchy

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

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

Step 2: Choose a Suitable Tool

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

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

Step 3: Make a Treemap Chart with PowerPoint

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

Step 5: Input Your Data

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

Step 6: Customize the Treemap

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

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

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

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

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

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

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

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

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

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

Real-Life Application of Pie Charts

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

Step 1: Define Your Data Structure

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

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

Step 2: Insert a Pie Chart

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

For instance:

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

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

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

Step 03: Results Interpretation

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

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

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

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

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

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

Real-Life Application of a Histogram

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

Step 1: Gather Data

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

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

Step 2: Define Bins

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

Step 3: Count Frequency

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

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

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

Step 4: Create the Histogram

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

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

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

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

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

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

Real-Life Application of Scatter Plot

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

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

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

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

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

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

1. Fact Sheet Dashboard for Data Presentation

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

Use This Template

2. 3D Column Chart Infographic PPT Template

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

3. Data Circles Infographic PowerPoint Template

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

4. Colorful Metrics Dashboard for Data Presentation

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

5. Animated Data Presentation Tools for PowerPoint & Google Slides

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

6. Statistics Waffle Charts PPT Template for Data Presentations

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

7. Data Presentation Dashboard Template for Google Slides

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

8. Weather Dashboard for Data Presentation

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

9. Social Media Marketing Dashboard Data Presentation Template

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

10. Project Management Summary Dashboard Template

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

11. Profit & Loss Dashboard for PowerPoint and Google Slides

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

Overwhelming visuals

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

Inappropriate chart types

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

Lack of context

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

Inconsistency in design

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

Failure to provide details

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

Lack of focus

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

Visual accessibility issues

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Data Analysis, Data Science, Data Visualization Filed under Design

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

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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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10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation with AI nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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10 Data Presentation Tips

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

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

1. Choose a Communication Style

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

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

2. Break Down Complicated Information

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

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

3. Choose the Right Data Visualizations

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

• Line graphs
• Scatter plots

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

4. Choose the Right Visualization Tools

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

• Microsoft Power BI
• Google Charts

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

5. Get Your Audience Involved

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

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

6. Be Authoritative

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

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

7. Label Your Data Clearly

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

8. Practice Your Data Presentation With Other Team Members

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

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

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

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

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

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

Leah Nguyen • 05 April, 2024 • 17 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

#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.

Tips to Engage with Polls & Trivia

More from AhaSlides

Take Advantage Of The Best Interactive & Effective Data Visualization Examples

Table of Contents

1) The Benefits Of Data Visualization

2) Our Top 27 Best Data Visualizations

3) Interactive Data Visualization: What’s In It For Me?

4) Static vs. Animated Data Visualization

Data is the new oil? No, data is the new soil." – David McCandless

Humans are visual creatures. A visual is processed 60,000 times faster than any text, and studies show that 65% of the population comprises visual learners. Moreover, 90% of the information transferred to the brain is visual.

Marrying digestible text with striking visuals provides the greatest results regarding the effective presentation of data, making it easy for audiences to understand and retain data. This very notion is the core of visualization.

Recently, data visualization specialists have married information to high aesthetics, taking advantage of humans’ natural affinity for beauty. When choosing the right data visualization type, the most important element to consider is if you’re offering people the opportunity to see insights they haven’t seen or experienced before and wouldn’t otherwise be able to decipher in written text alone.

Creators of effective visuals understand our human predisposition for the visual, taking it a step further by adding interactive functionalities that capture the imagination while presenting critical insights in a way that is as inspiring as it is understandable. By scrolling, clicking, and moving the cursor over interesting data points, designers engage users on a deeper level and enable them to be a participant rather than a viewer alone, adding more meaning to the data discovery learning process as a result.

Studies suggest that those who follow directions with illustrations perform 323% more efficiently than those who follow text-only directions. To demonstrate, here we spotlight 27 of the best data visualization examples from around the globe. Whether static or interactive dashboards , these creative data visualization examples will serve as an inspiration for any data enthusiast. Once we’ve showcased them, we’ll go back with a retrospective glance at the genesis of graphics and explore why it matters today more than ever.

The Benefits Of Data Visualization

Before we delve any deeper into our inspiring interactive data visualization examples, we’re going to look at the primary benefits of using visuals in a business context:

• Quicker action: As mentioned, the human brain processes visual information faster than text-based one, which means that your stakeholders – internally – or your prospects – externally – will be able to digest fresh insights and take swift, positive action on them.
• Finding connections: By displaying data in an inspiring visual format, it will be far easier to spot correlations and find connections between your operations and your overall commercial performance. As a result, you can develop a management report that will enable you to gain the insights you need to make changes that have a positive impact on the business.
• Emerging trends: By curating your data dashboards and presenting them visually, you stand a greater chance of spotting the kind of market trends that you can use to evolve your efforts while boosting profitability and gaining an edge over your competitors.
• Fresh discussions: Among the most beneficial elements of using visuals is the fact that it allows you to tell a story with your insights and, as a result, drill down deeper into specific segments of data. By utilizing your own dashboard storytelling efforts, you will be able to spot fresh insights and spark new discussions based on the growth, development, and direction of your business. Moreover, with the use of inspirational, informational graphics, you’ll engage your target audience on a deeper level, encouraging the kind of online discussions that will boost brand awareness, expand your reach, and help you position yourself as a thought leader in your field.

Our Top 27 Best Data Visualizations

Now that we’ve outlined the clear-cut benefits, let's explore our 27 best data visualization examples from experts worldwide.

1) Transparency International

**click image for source**

The first of our animated data visualization examples will be this clean, simple interactive diagram that displays the results of Transparency International’s Corruption Survey data. Bold red makes the results pop out immediately. This visual uses icons instead of words to communicate the different sectors included in the survey, making the presentation very visually appealing. Transparency International shows the viewer the relationships between the geographic location of the country and the type of corruption, leaving the audience to draw their conclusions.

2) Largest Vocabulary in Hip Hop

Rounding out our list was not an easy task. Instead of going for the super useful, we picked the most interesting: Matt Daniel’s Largest Vocabulary in Hip Hop. Matt explains, “Literary elites love to rep Shakespeare’s vocabulary: across his entire corpus, he uses 28,829 words, suggesting he knew over 100,000 words and arguably had the largest vocabulary ever. I decided to compare this data point against the most famous artists in hip-hop. I used each artist’s first 35,000 lyrics. That way, prolific artists, such as Jay-Z, could be compared to newer artists, such as Drake.” While the result may surprise you, it is a reminder that good online data visualization tools do more than present information – they tell a story.

3) Selfiecity – The Science of Selfies

This project is studying how people take self-portraits in different parts of the planet – over 120,000 selfies from Berlin, New York, Bangkok, Sao Paolo, and Moscow have been analyzed. Are women more likely to take selfies than men? In which proportion? Do people smile, tilt their heads, or crop their pictures? All these questions have been answered, and the results are displayed in very well-made interactive visualization examples. The complexity of the study and the insights found made it a real challenge to expose the results, which they addressed very well, creating cool data visualizations like this one.

4) Pinellas County’s Public School Inequalities For Black Pupils

Another example pictures the link between the increasing segregation of schools in Pinellas County in Florida and the poor performance of students. The visualization displays the results of an investigation carried out by the Tampa Bay Times based on data gathered by the Florida Department of Education and Pinellas County School District. The results are strikingly accurate and clear. They reveal five out of 150 elementary schools in Pinellas County where students perform exceptionally poorly. The underperforming schools are located in black community areas with the highest levels of social segregation. These insights can move local decision-makers and social activists to take better care of troubled institutions and introduce improvements.

Users scroll, not click, to see charts change and follow the investigation step by step. The scroll action is the new users’ favorite, which is particularly fit for the mobile user. On Facebook, Instagram, or Twitter, we scroll through streams and streams of content. Scrolling engages users for longer and makes it difficult to look away. The view transitions easily from chart to chart, making the story easy to follow and making the data itself central to the view on the screen. The data and results collected are complex and prolific, which made this data viz a real challenge, but they managed it so well and easy to understand that we deem it to be amongst the best interactive data visualizations that we came across.

5) The First Data Visualization to Solve a Deadly Threat

Cholera is a bacterial infection of the small intestine that causes its victims to suffer from such severe diarrhea and vomiting that they can die in two hours. It’s fatal in half of the untreated cases. When the disease arrived in London in 1832, it was thought to be spread by a “miasma” or bad smell in the atmosphere. There were four major cholera outbreaks in London over the next twenty-two years.

It was during the 4th epidemic that Doctor John Snow began to think that the air contamination argument seemed flimsy. Londoners were drinking water from the disgustingly polluted Thames, which also served as the city’s sewage line. Dr. Snow hypothesized that cholera was spread through the ingestion of polluted water. On August 31st, 1854, “what Dr. Snow later called ‘the most terrible outbreak of cholera which ever occurred in the kingdom’ broke out. It was as violent as it was sudden. During the next three days, 127 people living in or around Broad Street died.”

Dr. Snow set out to track where people died and the nearby water sources. His map not only solved the source of cholera but is amongst the first (and most well-known) visualizations. Dr. Snow simply put a dot on a map to indicate where people died of cholera. He found that almost all deaths had taken place a short distance from the Broad Street water pump. It was discovered that in “houses much nearer another pump, there had only been ten deaths — and of those, five victims had always drunk the water from the Broad Street pump, and three were schoolchildren who had probably drunk from the pump on their way to school.”

Several other anomalies helped prove his theory.  There was a pump nearby where only five out of 530 residents came down with cholera – it turned out they had their own well. The seventy employees at a nearby brewery made it through the epidemic unscathed; they were provided free beer and didn’t bother drinking water. The final piece of the puzzle arrived when Dr. Snow was alerted to the death of a woman who had not been in the SoHo area. When the deceased woman’s son informed him that his mother liked the taste of the Broad Street well water so much that she had a servant fetch it daily, he knew his hypothesis was proven. It would take years for the rest of London to believe him.

6) Where the Population of Europe is Growing And Declining

Among the various interactive data visualization examples that we picked, this is interesting when it comes to the huge amount of data it deals with. The Berliner Morgenpost’s EuropaKarte is a detailed map that provides viewers with detailed insights into the population growth and decline in Europe. Countries are divided into fields corresponding to geographical locations like cities or villages with less than 5.000 inhabitants. The fields vary in colors and intensity, with vibrant orange symbolizing the biggest growth and dark blue the biggest decline. White means no change. When you move the cursor over a selected geographical field, you will see a pop-up with data, including the unit's name, the number of inhabitants, and the growth/decline rate.

You can also type the location's name that interests you into the search box and find it easily. Moreover, you can use filters to see which municipalities are growing/declining at the fastest pace in Europe or Germany or view the list of countries with the fastest growing/declining populations in total.  The last filter will display the birth rates of every country listed in descending order. This interactive infographic fits squarely into the current trend of geographical map visualizations offered by datapine and other visual analytics software providers.

Are you a news junkie? This application organizes Google News’s top stories into color-coded category blocks. The size of each story depends on the number of related articles that exist inside each news cluster, so users can quickly identify the stories that receive the most coverage. Users can also filter their news by country and category, making it easy to find the stories that are important to them.

8) Interactive Real-Time Map of Berlin Traffic

Another of the good real-time data visualization examples out there shows the location and movement of trains, trams, buses, and even ferries in real time. If you want to look at the whole city from a longer distance, the graphic won’t be useful. You will just see dozens of signs swarming around like angry ants. To fully appreciate this map’s usefulness, you should use one of the filters – you can drill down to a street address, means of communication, lines, stops, or stations to get a clearer picture.

Moreover, if you zoom in, you can also see taxis, car sharing, bike sharing, and parking lots. If you are still not impressed, click on the icons. If you click P on the parking lot, the pop-up will appear with more detailed insights like the number of vacant places or the price. On the other hand, if you click, say, a taxi, you will see the car make, and accordingly, a bus – the whole timetable, same for a bus stop – the timetable of every single bus line that stops there.

According to VBB (Verkehrsverbund Berlin-Brandenburg), the public transport authority covering the federal states of Berlin and Brandenburg, the graphic doesn’t reflect the bus location with 100% precision because the data doesn’t come from GPS but is calculated according to the timetables. So it can happen that a user will wait, freezing at a train station, expecting the train to arrive, but it won’t appear. However, if the train is canceled or it has a serious delay, then it will be taken out of the system. The added value of this kind of timetable is that it can show multiple pieces of information at a glance so that you can decide which transportation will suit you most. The only drawback is that the mobile application is available only for Android systems.

9) The Dawn Wall

The Dawn Wall is The New York Times’ best interactive data visualization on the astonishing 19-day free climb in Yosemite National Park in California. Tommy Caldwell and Kevin Jorgeson were the first daredevils ever who completed a breathtaking climb up the mostly smooth granite face of El Capitan, widely considered the most difficult free-climb on the planet. A free-climb means that ropes are used only to catch a climber’s fall — not to aid the ascent. Visuals complement the story, weaving organically into the narrative and helping the user to picture the scale of this venture.

10) General Electric

While at first glance, this health infoscape seems overwhelming, a second look will show that it is worth the bounty of information it presents, making it one of the most effective data visualization examples we’ve seen to date. By gathering data from over 7.2 million electronic medical records, General Electric created an entertaining presentation about the prevalence of health symptoms and the symptoms commonly associated with each other. With pleasing colors and multiple ways to view the relationships, this makes looking at unpleasant symptoms quite enjoyable.

11) The Big Mac Index

Big Mac Index, popularized by The Economist, compares the prices of a Big Mac burger in McDonald’s restaurants in different countries. Why this particular product? Big Mac burger represents a standardized product that includes input costs from various areas, such as agricultural commodities (beef, bread, lettuce, cheese), labor (blue and white collar workers), advertising, rent, and real estate costs, transportation, etc., and therefore is representative of the general state of the local economy.

The Big Mac Index is a real-time data visualization example that shows whether currencies are at their “correct” level. It is based on the theory of purchasing-power parity (PPP), the notion that in the long run, exchange rates should move towards the rate that would equalize the prices of an identical basket of goods and services (in this case, a burger) in any two countries. For example, the average price of a Big Mac in the Euro area in July 2015 was $4.05 at the market exchange rate, while in the United States it was $4.79. It means that Big Mac in Europe was undervalued by 4.4%.

The Scatter chart at the bottom displays the local price of a Big Mac (expressed in the current base currency) against the GDP per person in that country. When you move the cursor over individual data points for details, you will see insights like GDP per person and Big Mac price in a chosen currency. When you click on the particular country, the scatter chart will be replaced by a line chart displaying the selected country’s under- or over-valuation against the current base currency over time.

12) Daily Rituals Showcases the Daily Routines of Iconic Famous People

It turns out that all great minds don't quite think alike, and this is masterfully showcased in this example that uses key insights from the book "Daily Rituals" by Mason Currey. Offering audiences a detailed reflection of the daily habits, rituals, pursuits, and priorities of some of history's most prolific creative minds, including the likes of Benjamin Franklin, Maya Angelou, and Franz Kafka, Daily Routines is one of the most entertaining data visualization examples on our list.

By using a simple color-coded bar chart format, Daily Routines’ creator makes it easy for viewers to compare the daily lives, habits, and rituals of these iconic creative geniuses and drill down into the very pursuits that help these individuals thrive during their lifetimes while comparing one to another.

Simple, striking, effective, and informational, this is certainly among the prime data visual examples in existence, and its message is as engaging as it is informational, offering key insights into optimizing our daily routines for enhanced success.

13) A Visualization of Thanksgiving Flights on Google Trends

Thanksgiving is, without a doubt, one of the most widely observed annual holidays in the US, with 54.3 million people traveling across the country to spend time with their loved ones last year alone.

Back in 2015, when around 46.3 million people traveled far and wide for Thanksgiving, tech colossus Google used its internal talent to create one of the best interactive data visualization examples we’ve seen to date. Powered by Google Trends, this aesthetically stunning piece of interactive content offers a glimpse into the flights moving to, from, and across the USA the day before Thanksgiving Day. Not only is each flight color-coded by the airline, but this short movie-style visualization has transformed flight-based metrics into a piece of art that shows the path of each flight in action.

Without displaying any numbers or flight metrics other than the time, viewers can see which segments of the day are the most popular for different types of flights – a testament to the value of effective data visualization.

14) An Insight into ‘Why Buses Bunch’

When considering the best interactive data visualization, it would be impossible to exclude this creation from Setosa. In a bid to show travelers why buses 'bunch,' or in other words – why services experience delays or disruptions that cause several buses to arrive at a bus stop simultaneously, the company developed this almost game-like piece of interactive data visualization comprised of complex data sets.

Upon the realization that telling the story by numbers would be messy and far from engaging, Setosa put the wheels in motion to produce a visual that used the data to give users a chance to cause delays and watch scenarios unfold while gaining an insight into how many passengers are affected by disruptions.

A glorious example of numerical metrics transformed into something accessible, engaging, and informational for everyone.

15) The Internet In Real-Time

Have you ever taken a second to think about how many people are using the internet simultaneously as you are? The answer is 5.03 billion , spread across 1.14 billion websites globally. These huge numbers are very hard to grasp by just reading them. With that in mind, Betfy created an interactive visualization that gives you a graphical idea of everything that is going on on the internet by the second. Covering different social media platforms and popular websites. 

While the visual aims to show an idea of how the internet works in real-time, the data presented in it is not actually happening live. In reality, what you see is a compilation of massive amounts of sources of data that have been broken down by the second to get a grasp of what is going on on the internet daily. Getting a sense of the magnitude of online activity worldwide is easy and accessible with this tool, which can also be adapted to hours, days, or months, depending on what the user wants to see.

16) Interactive Visualization Of The Exponential Spread Of COVID-19

The COVID-19 pandemic paralyzed the entire world with fear and uncertainty, probably more than any other event we’ve experienced in the past few decades. As a result of the unknown nature of this disease, various organizations created data systems to keep the public informed regarding infections around the world. However, many of these systems were images that were harder to understand and explore. With that premise in mind, Computer Science professor Wade Fagen-Ulmschneider created DIVOC-91, a project that shows the global spread of COVID-19 in a way that is interactive and easy to explore. 

Using data from the Johns Hopkins Center for Systems Science and Engineering, this interactive data visualization provides all the information regarding COVID-19 spread. The chart can be filtered by multiple factors, which include country, time, and scale, as well as new cases, vaccinations, total deaths, and more. Among its many interactive features, this visual allows users to hover over any data point to see what is behind it, animate the whole chart to see the information unfold automatically, and generate a report with the selected information. Definitely a great example of how massive amounts of data can be presented in a way that is fun, smart, and attractive. 

17) How The US Generates Electricity

With climate change at the center of public debate, more and more people are becoming curious about how basic resources are produced. One of them being electricity. In that regard, the USA’s electricity system is often called the “world's largest machine,” and our next example explains why in a very interactive map. 

The climate change website Carbon Brief published a graphic that shows the different electricity sources in the USA and how each of them is used and produced depending on the state. Paired with this, the map can also be broken down into high-carbon sources such as coal, oil, and gas and low-carbon sources such as wind or solar. 

This is one of our interactive data visualization examples that helps to inform the public about the true state of electricity production and the advancements made in terms of more conscious choices. If you want to take a look at the live version of the map as well as an article with insightful findings, click on the image above. 

18) A Map Of Lighthouses Around The World

As you’ve seen throughout this list, there are a bunch of interactive data examples that provide us with all kinds of useful and interesting information. Our next visual is a very cool map showing lighthouses around the globe. Now, you might think, what is so interesting about lighthouses? In fact, the 23,000 lighthouses positioned around the world safeguard maritime traffic with their strong lights, different color combinations, and frequencies. 

Geodienst, the spatial expertise center of the University of Groningen in the Netherlands, created this particularly interesting visual. By taking nautical data from an open-source database called OpenSeaMap, these experts created a map replicating the colors and frequencies of the thousands of lighthouses that guide ships daily on their journeys through the open sea.  If you go to the live version of the map by clicking on the image above, you can see the different colors and lights changing across territories.

19) An Interactive Map Tracking Ocean Cleanup

Cleaning the oceans is a task that many individuals, as well as big corporations, have taken very seriously in the past years. One of these initiatives includes the Ocean Cleanup, a project that aims to remove 90% of floating plastic from the oceans by 2040 and become the “largest clean-up in history.” 

With thousands of people watching this initiative and bringing more awareness to the topic, the Ocean Cleanup team resorted to interactive data visualization tools to generate a dashboard in the shape of a map that tracks the mission's progress. When accessing the dashboard, users just need to click on the highlighter spots to see the work done on that specific area, pictures of the process, information about the technologies used, as well as the economic impact of it. This is a great way to be informed and participate in this global issue.  

20) Where You Fit on the Political Spectrum

A noteworthy data visualization example from the Pew Research Center, this brilliantly presented static graphic showcases the current political spectrum and helps the reader to decide where their personal ideologies fit in comparison to the thoughts, opinions, and preferences of different media outlets, programs, and publications.

Not only is the table-style visual neat, professional, and easy to digest, but it also takes a specific set of metrics and offers a great deal of personal value by allowing the user to determine where their beliefs lie compared to others.

21) Data Breaches By Data Sensitivity 

According to a report by IBM, the global average cost of a data breach in 2023 was 4.45 million USD, a shocking 15% increase over three years. These rising numbers have led businesses and consumers to be more aware and cautious about how and where they share their data as well as how they protect it. From a business perspective, it can bring fatal financial and reputational consequences, and from a consumer perspective, it can lead to sensitive information, such as credit card numbers, addresses, phone numbers, and more, ending up in the hands of cybercriminals and be the victim of an attack. Being aware of the true reality of this issue is of utmost importance to ensure people and companies take the correct steps to protect themselves, which is why we included our next example. 

British data journalist, writer, and information designer David McCandless and his team from Information Is Beautiful gathered records about the world’s biggest data breaches, including technology and social media companies like Facebook, Yahoo, and Twitter, hotel businesses such as the Marriot Hotel, and even one from the Shanghai police. The results are displayed in a bubble chart where the size of each bubble represents the magnitude of the breach. Users can hover over each bubble to see a small extract of what the breach entailed, such as if it was a hack or just uncautious behavior by the company or institution. It is an excellent and scary resource to inform people of the dangers of sharing sensitive information online. 

22) A Journey Through Every Billboard Top 5 Hit 

Our next dynamic data visualization example is more fun and less scary than the previous one. The debate about the greatest musical era in history is a hot topic at many family meals and friend gatherings. Now, we know the answer to this debate is subject to the personal taste of each person. However, data doesn't lie, and the digital publication The Pudding aims to help people determine the answer to this popular debate.

The Pudding is a digital publication that explains the most debated ideas in culture through powerful visual essays that show users “stories they didn’t know they need it”. In this case, they generated the visual “A Journey Through Every Billboard Top 5 Hit To Find Music’s Greatest Era” that allows users to go through the top 5 Billboard Hit songs from the 1960s to the 2010s. What makes this graphic so cool is the fact that users can hear a small extract of each song playing as the years go by. It is an amazing and interactive way to honor music’s greatest hits.  

23) A Closer Look Into The Stars 

Have you ever wondered how many stars exist out there? How far is each star from the other? How do constellations look? Well, look no further. The ESA Star Mapper is an animated map that shows 59.921 stars with data gathered from the European Space Agency’s (ESA) Hipparcos catalog. The ESA’s Hipparcos was the first space astrometry mission, which operated from 1898 to 1993. It measured the positions, motions, and distances of over 100.000 stars, and it had a major impact on much of the astronomy research that has been carried out to this date. 

The map was generated using two images, one from the northern hemisphere of the Canary Islands and the other from the southern hemisphere in Chile. Users can scroll through the stars with five different filters that are explained during the experience, as well as see constellations and star names. A great way to learn more about the exciting topic of astronomy! 

24) Tualatin River Watershed Navigator 

Understanding the value of water and what it does for our world is of utmost importance, especially for communities that live near rivers or watersheds that significantly enhance their ecosystems. With this premise in mind, the organizations Tualatin Soil, Clean Water Services, and Water Conservation District developed the "Tualatin River Watershed Navigator". An interactive map that helps residents of Washington County learn everything about the Tualatin River Watershed and the big effect it has on the community's natural resources.

When navigating the visual, users can learn about water safety and soil conservation, plants and local wildlife, and answer some common questions, such as where the water they drink comes from or if they can fish in the lake. Paired with that, users can also find other valuable resources, such as homeless relief, local garbage, recycling, and closest farmers markets and parks, among other things. A great animated data visualization example to keep the community informed.

25) Density Designs’ World Languages Graphic

The next of our best data visualizations, this sensational graphic from Density Design offers a quick, easy, and engaging way to understand the number of languages that exist on the planet, where they're spoken, and who speaks them in different pockets of the world.

Produced for non-linguists, this data visualization example is easy to navigate and simple to break down. It serves up a level of insight that inspires, educates, and acts as an incredibly useful resource for those researching languages in a number of fields and industries. It is an impressive and creative feat of visualization.

26) Charles Joseph Minard, Napoleon's Invasion of Russia

Next, in our rundown of the best data visualizations, we’re going back a few hundred years to glance at Napoleon's Invasion of Russia. Drafted by civil engineer Charles Joseph Minard in 1892, this is perhaps the earliest recorded data visualization example and still one of the greatest visualizations to date.

Not only does this infographic-style visualization offer vital insights in a digestible format, but this age-old and effective data visualization example proves that we, as humans, are genuinely visual creatures. The pioneering visual is regarded by many as the “greatest statistical graphic ever drawn,” and while such a statement is subjective, it’s nothing short of inspirational.

Combining six data sets – geography, strategic movement, direction, the number of remaining soldiers, temperature, and time – this iconic visualization offers a tangible insight into Napoleon’s push into Russia, immortalizing an important historical moment for future generations to explore.

27) Climate Impact Map

Last but not least, in our list of examples of interactive data visualizations, we have another map that tracks the climate change impact globally. As you saw with some of the other related visuals on this list, climate change is a topic of global concern that is sparking conversations across many industries. Given that information is key when it comes to making a change, a map like this proves to be one of the most valuable interactive data visualization solutions out there.  

The visual was created by the Climate Impact Lab on a mission to show the damage that climate change has caused and is expected to keep making in the coming years. The graphic can be filtered by the average temperatures during the year, as well as a historical and future scenario for the world and the US. 

Data Visualization Today: Why It Matters

As you’ve learned from this insightful list of use cases, good visualizations are valuable in several areas and industries. That said, they are particularly important in business, where large volumes of data must be analyzed swiftly or presented in a clear and actionable format. The rows of numbers alone won’t create a compelling story to catch the audience’s attention. The goal is always to make the data behind your arguments look attractive enough to persuade decision-makers or enlighten your team members. With easy-to-use interactive online data visualization software, more and more companies can create eye-catching animated data visualizations on their own. Interactive interfaces make it possible even for non-technical users to create actionable charts.

The type of visualization you select is guided by the kind of information you are seeking to convey. A fixed image is ideal when alternate views are neither needed nor desired and when publishing to a static medium, such as print. Dynamic or interactive visualizations are better for empowering people to explore the data for themselves. Both have their advantages. When creating one, the purpose should always be to generate a certain level of excitement and engagement with the audience for it to be the best visualization. 

Static vs. Animated Data Visualizations

Due to the multitude of use cases we see in our daily lives, it is fairly clear that powerful visuals can change the game when it comes to explaining or introducing a specific topic to an audience. The way you decide to use visuals will solely depend on your audience and goals. Some prefer a more traditional static view of information, while others prefer the power of interactivity. But how can you know which kind is best for you? To help you put the value of these two types of data visualizations into perspective, below we will discuss their main differences, pitfalls, and attributes. 

From a user perspective, interactive data visualizations have changed the game. They enhance engagement and data discovery thanks to multiple features that bring information to life and help convey relevant stories. That said, from a technical perspective, interactive visuals are also complex, time-consuming, and more expensive to design and develop. Plus, they can be less portable or reproducible than static ones as they require specific software to be reproduced and an internet connection, in most cases. 

On the other hand, static visuals display the data in a fixed format. They help highlight certain trends or conclusions clearly and, most importantly, reliably. They often show a specific conclusion, argument, or trend through a presentation or report. However, compared to more innovative approaches, static visualizations are more restrictive and give users less control over the data. They are also less engaging and, at times, not enough to keep up with the fast-paced world we live in today. 

As mentioned, choosing between the two can be hard. You need to consider the size of your data, its complexity, the audience, and overall analysis goals. If you are working with large datasets, then interactivity is probably the best choice. If you are dealing with small or simple datasets, then a static chart might be enough. That being said, we believe that animated data visualizations are changing the world of analytics for the better, and with the help of professional BI software , you should be able to bring your data to life in ways you never thought possible. Some of the biggest benefits include: 

Combine Time and Motion to Aid the Audience Understanding

A layer of interactivity enables your audience to connect directly with your data and offers a second axis to track information changing over time. Hans Rosling, a famous data scientist and visualization Ted Talker, is a huge proponent of showing the time as a graphic movement. Check out this video – The Joy of Stats – a perfect example of how combining time with your visualization lets the viewer see trends.

Drilling Down to Extract Meaning From Numbers

A trend we are seeing in all content marketing, not just visualization, is personal attention. Content is no longer a one-size-fits-all solution, from customized white papers to interactive ebooks. Interactive visualization enables you to reach your audience on different levels by allowing you to drill down into the data. Newcomers to the topic can still spot trends and learn the basics, while experts in the field can drill down deeper into the data for more insight.

Engage, Engage, Engage

Content marketing is competitive. There is a lot of noise to push through before you can be heard. The best interactive data visualization is a ticket into the spotlight. Let’s take, for example, USA Today’s interactive story “ Behind The Bloodshed .” Mass killings dominate the American media. How can you compete with major prime channels as a newspaper? The USA upped the ante with this piece, particularly the interactive data visualization that enables the viewer to drill down on the mass killing timeline for details. The piece is informative, powerful, and emotional.

Interactive Data Visualizations: Key Takeaways 

Our selection of the best dynamic visualization examples above demonstrates creative, innovative leaps that illustrate the changing way we see and interpret data.

Everything is becoming personalized. People want to see how they fit into the big picture and where they stand on the shifting terrain. A real-time visualization example or some of the best interactive visualizations answer that need in two ways: giving the viewers control over what they see and letting them narrow the data down to their personal situation, whether it is age, location, income, or other factors.

This is the beginning of a new phase of data personalization. Instead of abstract headlines and generic pronouncements, readers can project themselves into the dataset. With the right tools, you can prepare the best interactive data visualizations for your business on your own, within a few clicks, and with no advanced IT skills needed.

“Visualization gives you answers to questions you didn’t know you had.” – Ben Shneiderman

To summarize, here are the best data visualization examples:

• Transparency International
• Largest Vocabulary in Hip Hop
• Selfiecity – The Science of Selfies
• Pinellas County’s Public School Inequalities For Black Pupils
• The First Visualization to Solve a Deadly Threat
• Where the Population of Europe is Growing And Declining
• Interactive Real-Time Map of Berlin Traffic
• The Dawn Wall
• General Electric
• The Big Mac Index
• Daily Rituals Showcases the Daily Routines of Iconic Famous People
• A Visualization of Thanksgiving Flights on Google Trends
• An Insight into ‘Why Buses Bunch’
• The Internet In Real-Time
• Interactive Visualization Of The Exponential Spread Of COVID-19
• How The US Generates Electricity
• A Map Of Lighthouses Around The World
• An Interactive Map Tracking Ocean Cleanup
• Where You Fit on the Political Spectrum
• Data Breaches By Data Sensitivity
• A Journey Through Every Billboard Top 5 Hit
• A Closer Look Into The Stars
• Tualatin River Watershed Navigator
• Density Designs’ World Languages Graphic
• Charles Joseph Minard, Napoleon’s Invasion of Russia
• Climate Impact Map

You can start visualizing your own data by exploring our software for a 14-day trial - completely free! 

20 Great Examples of PowerPoint Presentation Design [+ Templates]

Published: January 17, 2024

When it comes to PowerPoint presentation design, there's no shortage of avenues you can take.

While all that choice — colors, formats, visuals, fonts — can feel liberating, it‘s important that you’re careful in your selection as not all design combinations add up to success.

In this blog post, I’m sharing some of my favorite PowerPoint tips and templates to help you nail your next presentation.

Table of Contents

What makes a good PowerPoint presentation?

Powerpoint design ideas, best powerpoint presentation slides, good examples of powerpoint presentation design.

In my opinion, a great PowerPoint presentation gets the point across succinctly while using a design that doesn't detract from it.

Here are some of the elements I like to keep in mind when I’m building my own.

1. Minimal Animations and Transitions

Believe it or not, animations and transitions can take away from your PowerPoint presentation. Why? Well, they distract from the content you worked so hard on.

A good PowerPoint presentation keeps the focus on your argument by keeping animations and transitions to a minimum. I suggest using them tastefully and sparingly to emphasize a point or bring attention to a certain part of an image.

2. Cohesive Color Palette

I like to refresh my memory on color theory when creating a new PowerPoint presentation.

A cohesive color palette uses complementary and analogous colors to draw the audience’s attention and help emphasize certain aspects at the right time.

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It‘s impossible for me to tell you the specific design ideas you should go after in your next PowerPoint, because, well, I don’t know what the goal of your presentation is.

Luckily, new versions of PowerPoint actually suggest ideas for you based on the content you're presenting. This can help you keep up with the latest trends in presentation design .

PowerPoint is filled with interesting boilerplate designs you can start with. To find these suggestions, open PowerPoint and click the “Design” tab in your top navigation bar. Then, on the far right side, you'll see the following choices:

This simplistic presentation example employs several different colors and font weights, but instead of coming off as disconnected, the varied colors work with one another to create contrast and call out specific concepts.

What I like: The big, bold numbers help set the reader's expectations, as they clearly signify how far along the viewer is in the list of tips.

10. “Pixar's 22 Rules to Phenomenal Storytelling,” Gavin McMahon

This presentation by Gavin McMahon features color in all the right places. While each of the background images boasts a bright, spotlight-like design, all the characters are intentionally blacked out.

What I like: This helps keep the focus on the tips, while still incorporating visuals. Not to mention, it's still easy for me to identify each character without the details. (I found you on slide eight, Nemo.)

11. “Facebook Engagement and Activity Report,” We Are Social

Here's another great example of data visualization in the wild.

What I like: Rather than displaying numbers and statistics straight up, this presentation calls upon interesting, colorful graphs, and charts to present the information in a way that just makes sense.

12. “The GaryVee Content Model,” Gary Vaynerchuk

This wouldn‘t be a true Gary Vaynerchuk presentation if it wasn’t a little loud, am I right?

What I like: Aside from the fact that I love the eye-catching, bright yellow background, Vaynerchuk does a great job of incorporating screenshots on each slide to create a visual tutorial that coincides with the tips. He also does a great job including a visual table of contents that shows your progress as you go .

13. “20 Tweetable Quotes to Inspire Marketing & Design Creative Genius,” IMPACT Branding & Design

We‘ve all seen our fair share of quote-chronicling presentations but that isn’t to say they were all done well. Often the background images are poor quality, the text is too small, or there isn't enough contrast.

Well, this professional presentation from IMPACT Branding & Design suffers from none of said challenges.

What I like: The colorful filters over each background image create just enough contrast for the quotes to stand out.

14. “The Great State of Design,” Stacy Kvernmo

This presentation offers up a lot of information in a way that doesn't feel overwhelming.

What I like: The contrasting colors create visual interest and “pop,” and the comic images (slides 6 through 12) are used to make the information seem less buttoned-up and overwhelming.

15. “Clickbait: A Guide To Writing Un-Ignorable Headlines,” Ethos3

Not going to lie, it was the title that convinced me to click through to this presentation but the awesome design kept me there once I arrived.

What I like: This simple design adheres to a consistent color pattern and leverages bullet points and varied fonts to break up the text nicely.

16. “Digital Transformation in 50 Soundbites,” Julie Dodd

This design highlights a great alternative to the “text-over-image” display we've grown used to seeing.

What I like: By leveraging a split-screen approach to each presentation slide, Julie Dodd was able to serve up a clean, legible quote without sacrificing the power of a strong visual.

17. “Fix Your Really Bad PowerPoint,” Slide Comet

When you‘re creating a PowerPoint about how everyone’s PowerPoints stink, yours had better be terrific. The one above, based on the ebook by Seth Godin, keeps it simple without boring its audience.

What I like: Its clever combinations of fonts, together with consistent color across each slide, ensure you're neither overwhelmed nor unengaged.

18. “How Google Works,” Eric Schmidt

Simple, clever doodles tell the story of Google in a fun and creative way. This presentation reads almost like a storybook, making it easy to move from one slide to the next.

What I like: This uncluttered approach provides viewers with an easy-to-understand explanation of a complicated topic.

19. “What Really Differentiates the Best Content Marketers From The Rest,” Ross Simmonds

Let‘s be honest: These graphics are hard not to love. I especially appreciate the author’s cartoonified self-portrait that closes out the presentation. Well played, Ross Simmonds.

What I like: Rather than employing the same old stock photos, this unique design serves as a refreshing way to present information that's both valuable and fun.

20. “Be A Great Product Leader,” Adam Nash

This presentation by Adam Nash immediately draws attention by putting the company's logo first — a great move if your company is well known.

What I like: He uses popular images, such as ones of Megatron and Pinocchio, to drive his points home. In the same way, you can take advantage of popular images and media to keep your audience engaged.

PowerPoint Presentation Examples for the Best Slide Presentation

Mastering a PowerPoint presentation begins with the design itself.

Get inspired by my ideas above to create a presentation that engages your audience, builds upon your point, and helps you generate leads for your brand.

Editor's note: This post was originally published in March 2013 and has been updated for comprehensiveness. This article was written by a human, but our team uses AI in our editorial process. Check out our full disclosure to learn more about how we use AI.

Don't forget to share this post!

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The world is getting “smarter” every day, and to keep up with consumer expectations, companies are increasingly using machine learning algorithms to make things easier. You can see them in use in end-user devices (through face recognition for unlocking smartphones) or for detecting credit card fraud (like triggering alerts for unusual purchases).

Within  artificial intelligence  (AI) and  machine learning , there are two basic approaches: supervised learning and unsupervised learning. The main difference is that one uses labeled data to help predict outcomes, while the other does not. However, there are some nuances between the two approaches, and key areas in which one outperforms the other. This post clarifies the differences so you can choose the best approach for your situation.

Supervised learning  is a machine learning approach that’s defined by its use of labeled data sets. These data sets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.

Supervised learning can be separated into two types of problems when  data mining : classification and regression:

• Classification  problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees and  random forest  are all common types of classification algorithms.
• Regression  is another type of supervised learning method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful for predicting numerical values based on different data points, such as sales revenue projections for a given business. Some popular regression algorithms are linear regression, logistic regression, and polynomial regression.

Unsupervised learning  uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsupervised”).

Unsupervised learning models are used for three main tasks: clustering, association and dimensionality reduction:

• Clustering  is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, and so on.
• Association  is another type of unsupervised learning method that uses different rules to find relationships between variables in a given data set. These methods are frequently used for market basket analysis and recommendation engines, along the lines of “Customers Who Bought This Item Also Bought” recommendations.
• Dimensionality reduction  is a learning technique that is used when the number of features (or dimensions) in a given data set is too high. It reduces the number of data inputs to a manageable size while also preserving the data integrity. Often, this technique is used in the preprocessing data stage, such as when autoencoders remove noise from visual data to improve picture quality.

The main distinction between the two approaches is the use of labeled data sets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.

In supervised learning, the algorithm “learns” from the training data set by iteratively making predictions on the data and adjusting for the correct answer. While supervised learning models tend to be more accurate than unsupervised learning models, they require upfront human intervention to label the data appropriately. For example, a supervised learning model can predict how long your commute will be based on the time of day, weather conditions and so on. But first, you must train it to know that rainy weather extends the driving time.

Unsupervised learning models, in contrast, work on their own to discover the inherent structure of unlabeled data. Note that they still require some human intervention for validating output variables. For example, an unsupervised learning model can identify that online shoppers often purchase groups of products at the same time. However, a data analyst would need to validate that it makes sense for a recommendation engine to group baby clothes with an order of diapers, applesauce, and sippy cups.

• Goals:  In supervised learning, the goal is to predict outcomes for new data. You know up front the type of results to expect. With an unsupervised learning algorithm, the goal is to get insights from large volumes of new data. The machine learning itself determines what is different or interesting from the data set.
• Applications: Supervised learning models are ideal for spam detection, sentiment analysis, weather forecasting and pricing predictions, among other things. In contrast, unsupervised learning is a great fit for anomaly detection, recommendation engines, customer personas and medical imaging.
• Complexity:  Supervised learning is a simple method for machine learning, typically calculated by using programs like R or Python. In unsupervised learning, you need powerful tools for working with large amounts of unclassified data. Unsupervised learning models are computationally complex because they need a large training set to produce intended outcomes.
• Drawbacks: Supervised learning models can be time-consuming to train, and the labels for input and output variables require expertise. Meanwhile, unsupervised learning methods can have wildly inaccurate results unless you have human intervention to validate the output variables.

Choosing the right approach for your situation depends on how your data scientists assess the structure and volume of your data, as well as the use case. To make your decision, be sure to do the following:

• Evaluate your input data:  Is it labeled or unlabeled data? Do you have experts that can support extra labeling?
• Define your goals:  Do you have a recurring, well-defined problem to solve? Or will the algorithm need to predict new problems?
• Review your options for algorithms:  Are there algorithms with the same dimensionality that you need (number of features, attributes, or characteristics)? Can they support your data volume and structure?

Classifying big data can be a real challenge in supervised learning, but the results are highly accurate and trustworthy. In contrast, unsupervised learning can handle large volumes of data in real time. But, there’s a lack of transparency into how data is clustered and a higher risk of inaccurate results. This is where semi-supervised learning comes in.

Can’t decide on whether to use supervised or unsupervised learning?  Semi-supervised learning  is a happy medium, where you use a training data set with both labeled and unlabeled data. It’s particularly useful when it’s difficult to extract relevant features from data—and when you have a high volume of data.

Semi-supervised learning is ideal for medical images, where a small amount of training data can lead to a significant improvement in accuracy. For example, a radiologist can label a small subset of CT scans for tumors or diseases so the machine can more accurately predict which patients might require more medical attention.

Machine learning models are a powerful way to gain the data insights that improve our world. To learn more about the specific algorithms that are used with supervised and unsupervised learning, we encourage you to delve into the Learn Hub articles on these techniques. We also recommend checking out the blog post that goes a step further, with a detailed look at deep learning and neural networks.

• What is Supervised Learning?
• What is Unsupervised Learning?
• AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

To learn more about how to build machine learning models, explore the free tutorials on the  IBM® Developer Hub .

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AI + Machine Learning , Announcements , Azure AI Content Safety , Azure AI Studio , Azure OpenAI Service , Partners

Introducing GPT-4o: OpenAI’s new flagship multimodal model now in preview on Azure

By Eric Boyd Corporate Vice President, Azure AI Platform, Microsoft

Posted on May 13, 2024 2 min read

• Tag: Copilot
• Tag: Generative AI

Microsoft is thrilled to announce the launch of GPT-4o, OpenAI’s new flagship model on Azure AI. This groundbreaking multimodal model integrates text, vision, and audio capabilities, setting a new standard for generative and conversational AI experiences. GPT-4o is available now in Azure OpenAI Service, to try in preview , with support for text and image.

Azure OpenAI Service

A step forward in generative AI for Azure OpenAI Service

GPT-4o offers a shift in how AI models interact with multimodal inputs. By seamlessly combining text, images, and audio, GPT-4o provides a richer, more engaging user experience.

Launch highlights: Immediate access and what you can expect

Azure OpenAI Service customers can explore GPT-4o’s extensive capabilities through a preview playground in Azure OpenAI Studio starting today in two regions in the US. This initial release focuses on text and vision inputs to provide a glimpse into the model’s potential, paving the way for further capabilities like audio and video.

Efficiency and cost-effectiveness

GPT-4o is engineered for speed and efficiency. Its advanced ability to handle complex queries with minimal resources can translate into cost savings and performance.

Potential use cases to explore with GPT-4o

The introduction of GPT-4o opens numerous possibilities for businesses in various sectors: 

• Enhanced customer service : By integrating diverse data inputs, GPT-4o enables more dynamic and comprehensive customer support interactions.
• Advanced analytics : Leverage GPT-4o’s capability to process and analyze different types of data to enhance decision-making and uncover deeper insights.
• Content innovation : Use GPT-4o’s generative capabilities to create engaging and diverse content formats, catering to a broad range of consumer preferences.

Exciting future developments: GPT-4o at Microsoft Build 2024 

We are eager to share more about GPT-4o and other Azure AI updates at Microsoft Build 2024 , to help developers further unlock the power of generative AI.

Get started with Azure OpenAI Service

Begin your journey with GPT-4o and Azure OpenAI Service by taking the following steps:

• Try out GPT-4o in Azure OpenAI Service Chat Playground (in preview).
• If you are not a current Azure OpenAI Service customer, apply for access by completing this form .
• Learn more about  Azure OpenAI Service  and the  latest enhancements.  
• Understand responsible AI tooling available in Azure with Azure AI Content Safety .
• Review the OpenAI blog on GPT-4o.

Let us know what you think of Azure and what you would like to see in the future.

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