Information Visualization

Information visualization and data visualization are closely related, but they aren’t the same. Data visualization focuses on representing quantitative data, like numbers and statistics, in charts or graphs. It helps users to compare values, track changes, or identify patterns in numerical datasets.

Information visualization goes broader by presenting abstract information, relationships, and hierarchies, not just raw numbers. For example, a mind map or a network diagram falls under information visualization since it shows how ideas or entities connect.

The main difference is scope: data visualization is usually numeric, while information visualization includes qualitative and abstract data. Both aim to simplify complexity, but information visualization emphasizes insight, exploration, and understanding in contexts beyond just numbers, and often has a purpose to persuade viewers or users.

Discover helpful insights to get further into it in our article How to Design an Information Visualization.

Information visualization includes many types, with each suited to different purposes, such as:

• Bar charts are ideal for comparing categories.

• Line charts reveal changes over time.

• Scatterplots highlight correlations between variables.

• Heatmaps display intensity or frequency with color, making them great for spotting density patterns.

• Treemaps organize hierarchical data into nested rectangles, helping viewers compare proportions.

• Network diagrams show relationships among entities, such as social connections or system flows.

• Gantt charts visualize project timelines and dependencies.

• Bubble charts combine size and position to represent multiple dimensions.

• Flow diagrams track processes or movement.

Each type serves a unique role in how it can make complex data understandable, so when you know their strengths, you can pick the right one for your communication goals and target audience.

Keep a handle on the best way to get your information across to your audience with some additional insights from our article Preattentive Visual Properties and How to Use Them in Information Visualization.

You should use a bar chart when you need to compare different categories side by side. Bar charts make it easy to see differences in size, frequency, or amounts, which is why they’re common in business reports, surveys, and marketing dashboards.

They are most effective when the categories are discrete or distinct, like sales by product line, expenses by department, or votes by candidate. Bar charts work well for ranking items from highest to lowest, too.

However, they’re less effective for showing trends over time, where line charts or area charts perform better. From how it can keep scales consistent and labels clear, a bar chart ensures the audience quickly sees which categories outperform others.

Explore a treasure trove of tips and points to help you make better charts to reach your audience more clearly, in our article Guidelines for Good Visual Information Representations.

A line chart is most effective when you want to show trends or changes over time. It connects data points with lines, which highlights direction, growth, or decline. This makes it ideal for tracking metrics like revenue over months, website traffic over weeks, or temperature over years.

Line charts are good for helping audiences to see patterns such as seasonality, cycles, or long-term growth. They’re also useful for comparing multiple trends at once, provided you use clear labels and avoid clutter. Unlike bar charts, line charts focus on continuity and progression. If you want viewers to understand how a variable evolves rather than how categories compare, a line chart is the clearest and most intuitive choice.

Pick up a wealth of helpful insights for your chart-making and more, in our article Visual Mapping – The Elements of Information Visualization.

A scatterplot helps identify relationships, patterns, and correlations between two variables. When you place data points on a two-axis graph, you can quickly see if a relationship exists, such as positive, negative, or no correlation. For example, scatterplots can reveal how advertising spend relates to sales or how study hours relate to exam performance.

Scatterplots are particularly valuable for spotting clusters, gaps, or outliers that may not be obvious in tabular data. Scatterplots allow you to add regression lines, too, helping highlight predictive trends. When you want to test hypotheses, explore cause-and-effect, or showcase variability across data, scatterplots can be a great way to offer clear, evidence-based insights that guide decision-making.

Explore how to manage and prevent a risk in information visualization presentations, in our article Information Overload, Why it Matters and How to Combat It.

A heatmap is useful for visualizing the intensity or concentration of data across a space or set of categories. It uses color gradients to indicate frequency, density, or performance, which makes it easy to spot areas of interest.

In web analytics, heatmaps show where users click most often on a webpage. In biology, they reveal gene expression levels across samples. In business, they highlight which products sell best in different regions.

Heatmaps are a great way to simplify large datasets by converting numbers into intuitive color patterns. They work best when you want to emphasize magnitude differences and quickly guide the eye to hotspots that deserve attention, analysis, or further action.

Harvest helpful tips on how to use heatmaps from their application in eye tracking studies.

To choose the right visualization type, begin by asking what question you want your data to answer. If you have to compare categories, use a bar chart. To show trends over time, go for a line chart. If your goal is to highlight relationships between variables, scatterplots work well. To show proportions in parts of a whole, consider pie charts or treemaps. For geographic data, maps are the clearest option.

Always match the chart to the story the information tells. Don’t use complex visuals when a simple one communicates better, anyway. Moreover, consider your audience’s familiarity with certain charts, ensuring they can interpret your visualization quickly and accurately without extra explanation. Your information visualization needs to give a good user experience, one that’s neat and seamless and doesn’t make your target audience pause in confusion.

Get a greater understanding of how to accommodate your target audience well, in our article The Properties of Human Memory and Their Importance for Information Visualization.

Gestalt principles, also known as Gestalt laws, explain how people naturally group and interpret visual elements, and they apply directly to effective information visualization. Here are main ones:

• The principle of proximity encourages viewers to see items near each other as related, which helps organize categories.

• The principle of similarity groups elements with the same color or shape, and makes comparisons intuitive.

• The principle of continuity guides the eye smoothly through aligned data points, improving flow in charts like line graphs.

• The principle of closure lets people mentally complete shapes or trends, which helps fill in missing information.

• The principle of figure-ground ensures the main message stands out from the background.

By applying these principles, you can create visualizations that feel natural, reduce cognitive effort, and let your audiences grasp insights more quickly and reliably.

Grab a greater grasp of Gestalt principles to supercharge your information visualizations and designs of many types.

Scale is critical in charts and graphs since it directly influences how audiences interpret data. A poorly chosen scale can exaggerate or minimize differences and lead to audiences misinterpreting what you intended them to understand. For instance, a truncated y-axis might make small changes look dramatic, while an overly stretched axis can hide significant trends.

Consistent scales permit fair comparisons across categories or time periods. Proper scaling supports accessibility, too, ensuring that visuals communicate accurately to diverse viewers. When you’re working with data of very different magnitudes, logarithmic scales can help reveal proportional relationships. Ultimately, scale sets the context for interpretation, and you should apply it carefully to maintain accuracy, fairness, and trust in the visualization’s message.

Investigate further helpful points about how to get information visualization done well for target audiences, in our article Information Visualization – An Introduction to Multivariate Analysis.

The first step in designing an information visualization is to define your purpose and the question you want the data to answer. Without a clear goal, even the most polished chart may confuse rather than clarify. So, ask yourself what insight the audience should walk away with, such as identifying a trend, comparing performance, or understanding relationships.

Once you’ve defined the purpose, you can choose the right dataset, clean it for accuracy, and decide on the best type of visualization. Begin with clear intention and you can help ensure the final design is relevant, focused, and actionable. It prevents common pitfalls, too, like overloading visuals with unnecessary details that distract from the core message.

Discover a wealth of points to watch out for with presenting to audiences, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Audience awareness is an essential part of information visualization because people interpret visuals differently depending on their background and needs. For example, a visualization designed for data scientists might include statistical models, while one for executives should highlight key takeaways and bottom-line business implications.

When you understand your audience, it helps you adjust complexity, vocabulary, and level of detail so the message you cast lands with those it’s intended for, crystal-clear. For instance, avoid technical jargon with general audiences but provide precise labels for specialized viewers. Tailor the design and you can ensure clarity, engagement, and relevance and keep confusion and disengagement at bay. So, consider what your audience values most, and from there make your visualization more effective, persuasive, and impactful in real-world decision-making contexts.

Find out how to own the room you present to through the audience-awareness technique of active listening, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Storytelling connects to information visualization by giving data context, emotion, and direction. A chart or graph alone shows numbers, but a narrative explains why those numbers matter and lifts them off the screen to become more “alive.”

For example, a line chart of rising sales becomes more engaging when you frame it as a story of customer growth that happened because product improvements drove it. Storytelling guides the audience through beginning, middle, and end, using data as evidence. It helps to highlight conflict, resolution, or opportunity, and so makes the message memorable. Good visualization doesn’t just display facts; it frames them in a way that inspires action or deeper understanding. So, when you pair visuals with a clear story, you can transform data into insights that stick.

Find out how to cast yourself well as a trustable presence in more effective presentations where people listen to compelling information, including information empowered with storytelling, in this video with Morgane Peng.     

Color plays a vital role in information visualization by highlighting patterns, emphasizing contrasts, and encoding categories. It draws attention to important data points, such as outliers or high-performing segments, essential especially for business stakeholders who want to know important things quickly.

Warm colors like red or orange can signal urgency or intensity, while cool colors like blue or green often suggest calm or positive outcomes. However, misuse of color can distort interpretation or overwhelm viewers. Designers and presenters alike need to use color intentionally and sparingly, and ensure that meaning stays clear and accessible and that meaning isn’t conveyed through color alone.

Color schemes should consider accessibility for color-blind audiences, too. When you apply it effectively, color enhances clarity, strengthens storytelling, and makes visualizations more engaging and easier to interpret for your audiences.

Explore how to stay on point when you apply color in UX design and presentations.

Designers should use shapes to differentiate categories, highlight relationships, and add meaning beyond color. Shapes work well in scatterplots to distinguish multiple groups, such as circles for one category and squares for another.

In network diagrams, different node shapes can indicate roles or functions. Arrows and directional shapes guide the eye and suggest flow, making them useful in process charts.

Shapes also support accessibility as color alone may not be sufficient, ensuring everyone can interpret the visualization. The key is consistency: each shape must carry the same meaning throughout the design. Use shapes strategically and you can make visualizations clearer, more inclusive, and more effective in communicating complex information.

Discover a treasure trove of fascinating and helpful insights in our article How to Display Complex Network Data with Information Visualization.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.

This seminal book by Edward Tufte emphasizes clarity, precision, and integrity in presenting data. Tufte introduces principles like the data-ink ratio, which guides designers to minimize nonessential elements and maximize the communication of data itself. He critiques misleading practices such as chartjunk and distorted scales while showcasing historical graphics that set enduring standards. For designers, this book is crucial since it combines theory with visual examples, showing how even simple graphics can powerfully convey truth. Its focus on honesty and simplicity continues to influence modern visualization and is especially relevant in fields where credibility is paramount.

Munzner, T. (2014). Visualization Analysis and Design. Boca Raton, FL: CRC Press.

Munzner’s work provides a comprehensive framework for analyzing and designing information visualizations. Central to the book is the Nested Model, which breaks down the design process into layers of tasks, data, design choices, and validation. This approach helps practitioners align design decisions with user needs and evaluation criteria. Unlike purely theoretical texts, the book offers actionable frameworks and examples applicable to diverse fields, from business dashboards to scientific data. For designers, it serves as both a teaching text and a reference for ensuring visualization quality and usability. Its blend of analysis and practice makes it indispensable for serious visualization work.

Evergreen, S. (2019). Evergreen Data Blog: Storytelling with data visualization. Retrieved from https://stephanieevergreen.com/blog/

Stephanie Evergreen’s blog provides accessible, practice-oriented advice for creating clear and engaging data visualizations. Her posts often feature real-world chart makeovers, illustrating how small design tweaks, such as decluttering, using color strategically, or refining typography, dramatically improve communication. She emphasizes storytelling as a central principle, showing how visuals should connect data with human understanding. For designers, evaluators, and educators, this blog is especially useful because it translates theory into everyday application. Its approachable style and frequent updates make it an ongoing resource for keeping skills sharp and applying best practices without jargon or unnecessary complexity.

Datawrapper. (n.d.). Datawrapper Blog. Retrieved from https://blog.datawrapper.de/

The Datawrapper blog is a practical, open resource created by the team behind the Datawrapper charting tool. Posts explore visualization design principles with case studies drawn from journalism, research, and public communication. Topics include choosing the right chart type, improving accessibility, using color responsibly, and avoiding common design pitfalls. Each post provides actionable insights, often with interactive examples that readers can replicate. For designers, the blog serves as both inspiration and a guide for implementing best practices in professional contexts. Its focus on real-world use cases makes it one of the most relevant ongoing resources for visualization practitioners.

Information visualization and data visualization are closely related, but they are not the same. Data visualization focuses on representing quantitative data, like numbers and statistics, in charts or graphs. It helps users to compare values, track changes, or identify patterns in numerical datasets.

Information visualization goes broader by presenting abstract information, relationships, and hierarchies, not just raw numbers. For example, a mind map or a network diagram falls under information visualization since it shows how ideas or entities connect.

The main difference is scope: data visualization is usually numeric, while information visualization includes qualitative and abstract data. Both aim to simplify complexity, but information visualization emphasizes insight, exploration, and understanding in contexts beyond just numbers, and often has a purpose to persuade viewers or users.

Discover helpful insights to get further into it in our article How to Design an Information Visualization.

Information visualization includes many types, with each suited to different purposes, such as:

• Bar charts are ideal for comparing categories.

• Line charts reveal changes over time.

• Scatterplots highlight correlations between variables.

• Heatmaps display intensity or frequency with color, making them great for spotting density patterns.

• Treemaps organize hierarchical data into nested rectangles, helping viewers compare proportions.

• Network diagrams show relationships among entities, such as social connections or system flows.

• Gantt charts visualize project timelines and dependencies.

• Bubble charts combine size and position to represent multiple dimensions.

• Flow diagrams track processes or movement.

Each type serves a unique role in how it can make complex data understandable, so when you know their strengths, you can pick the right one for your communication goals and target audience.

Keep a handle on the best way to get your information across to your audience with some additional insights from our article Preattentive Visual Properties and How to Use Them in Information Visualization.

You should use a bar chart when you need to compare different categories side by side. Bar charts make it easy to see differences in size, frequency, or amounts, which is why they are common in business reports, surveys, and marketing dashboards.

They are most effective when the categories are discrete or distinct, like sales by product line, expenses by department, or votes by candidate. Bar charts work well for ranking items from highest to lowest, too.

However, they are less effective for showing trends over time, where line charts or area charts perform better. From how it can keep scales consistent and labels clear, a bar chart ensures the audience quickly sees which categories outperform others.

Explore a treasure trove of tips and points to help you make better charts to reach your audience more clearly, in our article Guidelines for Good Visual Information Representations.

A line chart is most effective when you want to show trends or changes over time. It connects data points with lines, which highlights direction, growth, or decline. This makes it ideal for tracking metrics like revenue over months, website traffic over weeks, or temperature over years.

Line charts are good for helping audiences to see patterns such as seasonality, cycles, or long-term growth. They are also useful for comparing multiple trends at once, provided you use clear labels and avoid clutter. Unlike bar charts, line charts focus on continuity and progression. If you want viewers to understand how a variable evolves rather than how categories compare, a line chart is the clearest and most intuitive choice.

Pick up a wealth of helpful insights for your chart-making and more, in our article Visual Mapping – The Elements of Information Visualization.

A scatterplot helps identify relationships, patterns, and correlations between two variables. When you place data points on a two-axis graph, you can quickly see if a relationship exists, such as positive, negative, or no correlation. For example, scatterplots can reveal how advertising spend relates to sales or how study hours relate to exam performance.

Scatterplots are particularly valuable for spotting clusters, gaps, or outliers that may not be obvious in tabular data. Scatterplots allow you to add regression lines, too, helping highlight predictive trends. When you want to test hypotheses, explore cause-and-effect, or showcase variability across data, scatterplots can be a great way to offer clear, evidence-based insights that guide decision-making.

Explore how to manage and prevent a risk in information visualization presentations, in our article Information Overload, Why it Matters and How to Combat It.

A heatmap is useful for visualizing the intensity or concentration of data across a space or set of categories. It uses color gradients to indicate frequency, density, or performance, which makes it easy to spot areas of interest.

In web analytics, heatmaps show where users click most often on a webpage. In biology, they reveal gene expression levels across samples. In business, they highlight which products sell best in different regions.

Heatmaps are a great way to simplify large datasets by converting numbers into intuitive color patterns. They work best when you want to emphasize magnitude differences and quickly guide the eye to hotspots that deserve attention, analysis, or further action.

Harvest helpful tips on how to use heatmaps from their application in eye tracking studies.

To choose the right visualization type, begin by asking what question you want your data to answer. If you have to compare categories, use a bar chart. To show trends over time, go for a line chart. If your goal is to highlight relationships between variables, scatterplots work well. To show proportions in parts of a whole, consider pie charts or treemaps. For geographic data, maps are the clearest option.

Always match the chart to the story the information tells. Do not use complex visuals when a simple one communicates better, anyway. Moreover, consider how familiar your audience is with certain charts, ensuring they can interpret your visualization quickly and accurately without extra explanation. Your information visualization needs to give a good user experience, one that is neat and seamless and does not make your target audience pause in confusion.

Get a greater understanding of how to accommodate your target audience well, in our article The Properties of Human Memory and Their Importance for Information Visualization.

Gestalt principles, also known as Gestalt laws, explain how people naturally group and interpret visual elements, and they apply directly to effective information visualization. Here are main ones:

• The principle of proximity encourages viewers to see items near each other as related, which helps organize categories.

• The principle of similarity groups elements with the same color or shape, and makes comparisons intuitive.

• The principle of continuity guides the eye smoothly through aligned data points, improving flow in charts like line graphs.

• The principle of closure lets people mentally complete shapes or trends, which helps fill in missing information.

• The principle of figure-ground ensures the main message stands out from the background.

By applying these principles, you can create visualizations that feel natural, reduce cognitive effort, and let your audiences grasp insights more quickly and reliably.

Grab a greater grasp of Gestalt principles to supercharge your information visualizations and designs of many types.

Scale is critical in charts and graphs since it directly influences how audiences interpret data. A poorly chosen scale can exaggerate or minimize differences and lead to audiences misinterpreting what you intended them to understand. For instance, a truncated y-axis might make small changes look dramatic, while an overly stretched axis can hide significant trends.

Consistent scales permit fair comparisons across categories or time periods. Proper scaling supports accessibility, too, ensuring that visuals communicate accurately to diverse viewers. When you are working with data of very different magnitudes, logarithmic scales can help reveal proportional relationships. Ultimately, scale sets the context for interpretation, and you should apply it carefully to maintain accuracy, fairness, and trust in the message of the visualization.

Investigate further helpful points about how to get information visualization done well for target audiences, in our article Information Visualization – An Introduction to Multivariate Analysis.

The first step in designing an information visualization is to define your purpose and the question you want the data to answer. Without a clear goal, even the most polished chart may confuse rather than clarify. So, ask yourself what insight the audience should walk away with, such as identifying a trend, comparing performance, or understanding relationships.

Once you have defined the purpose, you can choose the right dataset, clean it for accuracy, and decide on the best type of visualization. Begin with clear intention and you can help ensure the final design is relevant, focused, and actionable. It prevents common pitfalls, too, like overloading visuals with unnecessary details that distract from the core message.

Discover a wealth of points to watch out for with presenting to audiences, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Audience awareness is an essential part of information visualization because people interpret visuals differently depending on their background and needs. For example, a visualization designed for data scientists might include statistical models, while one for executives should highlight key takeaways and bottom-line business implications.

When you understand your audience, it helps you adjust complexity, vocabulary, and level of detail so the message you cast lands with those it is intended for, crystal-clear. For instance, avoid technical jargon with general audiences but provide precise labels for specialized viewers. Tailor the design and you can ensure clarity, engagement, and relevance and keep confusion and disengagement at bay. So, consider what your audience values most, and from there make your visualization more effective, persuasive, and impactful in real-world decision-making contexts.

Find out how to own the room you present to through the audience-awareness technique of active listening, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Storytelling connects to information visualization by giving data context, emotion, and direction. A chart or graph alone shows numbers, but a narrative explains why those numbers matter and lifts them off the screen to become more “alive.”

For example, a line chart of rising sales becomes more engaging when you frame it as a story of customer growth that happened because product improvements drove it. Storytelling guides the audience through beginning, middle, and end, using data as evidence. It helps to highlight conflict, resolution, or opportunity, and so makes the message memorable. Good visualization does not just display facts; it frames them in a way that inspires action or deeper understanding. So, when you pair visuals with a clear story, you can transform data into insights that stick.

Find out how to cast yourself well as a trustable presence in more effective presentations where people listen to compelling information, including information empowered with storytelling, in this video with Morgane Peng.     

Color plays a vital role in information visualization by highlighting patterns, emphasizing contrasts, and encoding categories. It draws attention to important data points, such as outliers or high-performing segments, essential especially for business stakeholders who want to know important things quickly.

Warm colors like red or orange can signal urgency or intensity, while cool colors like blue or green often suggest calm or positive outcomes. However, misuse of color can distort interpretation or overwhelm viewers. Designers and presenters alike need to use color intentionally and sparingly, and ensure that meaning stays clear and accessible and that meaning is not conveyed through color alone.

Color schemes should consider accessibility for color-blind audiences, too. When you apply it effectively, color enhances clarity, strengthens storytelling, and makes visualizations more engaging and easier to interpret for your audiences.

Explore how to stay on point when you apply color in UX design and presentations.

Designers should use shapes to differentiate categories, highlight relationships, and add meaning beyond color. Shapes work well in scatterplots to distinguish multiple groups, such as circles for one category and squares for another.

In network diagrams, different node shapes can indicate roles or functions. Arrows and directional shapes guide the eye and suggest flow, making them useful in process charts.

Shapes also support accessibility as color alone may not be sufficient, ensuring everyone can interpret the visualization. The key is consistency: each shape must carry the same meaning throughout the design. Use shapes strategically and you can make visualizations clearer, more inclusive, and more effective in communicating complex information.

Discover a treasure trove of fascinating and helpful insights in our article How to Display Complex Network Data with Information Visualization.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.

This seminal book by Edward Tufte emphasizes clarity, precision, and integrity in presenting data. Tufte introduces principles like the data-ink ratio, which guides designers to minimize nonessential elements and maximize the communication of data itself. He critiques misleading practices such as chartjunk and distorted scales while showcasing historical graphics that set enduring standards. For designers, this book is crucial since it combines theory with visual examples, showing how even simple graphics can powerfully convey truth. Its focus on honesty and simplicity continues to influence modern visualization and is especially relevant in fields where credibility is paramount.

Munzner, T. (2014). Visualization Analysis and Design. Boca Raton, FL: CRC Press.

This work provides a comprehensive framework for analyzing and designing information visualizations. Central to the book is the Nested Model, which breaks down the design process into layers of tasks, data, design choices, and validation. This approach helps practitioners align design decisions with user needs and evaluation criteria. Unlike purely theoretical texts, the book offers actionable frameworks and examples applicable to diverse fields, from business dashboards to scientific data. For designers, it serves as both a teaching text and a reference for ensuring visualization quality and usability. Its blend of analysis and practice makes it indispensable for serious visualization work.

Evergreen, S. (2019). Evergreen Data Blog: Storytelling with data visualization. Retrieved from https://stephanieevergreen.com/blog/

This blog by Stephanie Evergreen provides accessible, practice-oriented advice for creating clear and engaging data visualizations. Her posts often feature real-world chart makeovers, illustrating how small design tweaks, such as decluttering, using color strategically, or refining typography, dramatically improve communication. She emphasizes storytelling as a central principle, showing how visuals should connect data with human understanding. For designers, evaluators, and educators, this blog is especially useful because it translates theory into everyday application. Its approachable style and frequent updates make it an ongoing resource for keeping skills sharp and applying best practices without jargon or unnecessary complexity.

Datawrapper. (n.d.). Datawrapper Blog. Retrieved from https://blog.datawrapper.de/

The Datawrapper blog is a practical, open resource created by the team behind the Datawrapper charting tool. Posts explore visualization design principles with case studies drawn from journalism, research, and public communication. Topics include choosing the right chart type, improving accessibility, using color responsibly, and avoiding common design pitfalls. Each post provides actionable insights, often with interactive examples that readers can replicate. For designers, the blog serves as both inspiration and a guide for implementing best practices in professional contexts. Its focus on real-world use cases makes it one of the most relevant ongoing resources for visualization practitioners.

Information visualization and data visualization are closely related, but they are not the same. Data visualization focuses on representing quantitative data, like numbers and statistics, in charts or graphs. It helps users to compare values, track changes, or identify patterns in numerical datasets.

Information visualization goes broader by presenting abstract information, relationships, and hierarchies, not just raw numbers. For example, a mind map or a network diagram falls under information visualization since it shows how ideas or entities connect.

The main difference is scope: data visualization is usually numeric, while information visualization includes qualitative and abstract data. Both aim to simplify complexity, but information visualization emphasizes insight, exploration, and understanding in contexts beyond just numbers, and often has a purpose to persuade viewers or users.

Discover helpful insights to get further into it in our article How to Design an Information Visualization.

Information visualization includes many types, with each suited to different purposes, such as:

• Bar charts are ideal for comparing categories.

• Line charts reveal changes over time.

• Scatterplots highlight correlations between variables.

• Heatmaps display intensity or frequency with color, making them great for spotting density patterns.

• Treemaps organize hierarchical data into nested rectangles, helping viewers compare proportions.

• Network diagrams show relationships among entities, such as social connections or system flows.

• Gantt charts visualize project timelines and dependencies.

• Bubble charts combine size and position to represent multiple dimensions.

• Flow diagrams track processes or movement.

Each type serves a unique role in how it can make complex data understandable, so when you know their strengths, you can pick the right one for your communication goals and target audience.

Keep a handle on the best way to get your information across to your audience with some additional insights from our article Preattentive Visual Properties and How to Use Them in Information Visualization.

You should use a bar chart when you need to compare different categories side by side. Bar charts make it easy to see differences in size, frequency, or amounts, which is why they are common in business reports, surveys, and marketing dashboards.

They are most effective when the categories are discrete or distinct, like sales by product line, expenses by department, or votes by candidate. Bar charts work well for ranking items from highest to lowest, too.

However, they are less effective for showing trends over time, where line charts or area charts perform better. From how it can keep scales consistent and labels clear, a bar chart ensures the audience quickly sees which categories outperform others.

Explore a treasure trove of tips and points to help you make better charts to reach your audience more clearly, in our article Guidelines for Good Visual Information Representations.

A line chart is most effective when you want to show trends or changes over time. It connects data points with lines, which highlights direction, growth, or decline. This makes it ideal for tracking metrics like revenue over months, website traffic over weeks, or temperature over years.

Line charts are good for helping audiences to see patterns such as seasonality, cycles, or long-term growth. They are also useful for comparing multiple trends at once, provided you use clear labels and avoid clutter. Unlike bar charts, line charts focus on continuity and progression. If you want viewers to understand how a variable evolves rather than how categories compare, a line chart is the clearest and most intuitive choice.

Pick up a wealth of helpful insights for your chart-making and more, in our article Visual Mapping – The Elements of Information Visualization.

A scatterplot helps identify relationships, patterns, and correlations between two variables. When you place data points on a two-axis graph, you can quickly see if a relationship exists, such as positive, negative, or no correlation. For example, scatterplots can reveal how advertising spend relates to sales or how study hours relate to exam performance.

Scatterplots are particularly valuable for spotting clusters, gaps, or outliers that may not be obvious in tabular data. Scatterplots allow you to add regression lines, too, helping highlight predictive trends. When you want to test hypotheses, explore cause-and-effect, or showcase variability across data, scatterplots can be a great way to offer clear, evidence-based insights that guide decision-making.

Explore how to manage and prevent a risk in information visualization presentations, in our article Information Overload, Why it Matters and How to Combat It.

A heatmap is useful for visualizing the intensity or concentration of data across a space or set of categories. It uses color gradients to indicate frequency, density, or performance, which makes it easy to spot areas of interest.

In web analytics, heatmaps show where users click most often on a webpage. In biology, they reveal gene expression levels across samples. In business, they highlight which products sell best in different regions.

Heatmaps are a great way to simplify large datasets by converting numbers into intuitive color patterns. They work best when you want to emphasize magnitude differences and quickly guide the eye to hotspots that deserve attention, analysis, or further action.

Harvest helpful tips on how to use heatmaps from their application in eye tracking studies.

To choose the right visualization type, begin by asking what question you want your data to answer. If you have to compare categories, use a bar chart. To show trends over time, go for a line chart. If your goal is to highlight relationships between variables, scatterplots work well. To show proportions in parts of a whole, consider pie charts or treemaps. For geographic data, maps are the clearest option.

Always match the chart to the story the information tells. Do not use complex visuals when a simple one communicates better, anyway. Moreover, consider how familiar your audience is with certain charts, ensuring they can interpret your visualization quickly and accurately without extra explanation. Your information visualization needs to give a good user experience, one that is neat and seamless and does not make your target audience pause in confusion.

Get a greater understanding of how to accommodate your target audience well, in our article The Properties of Human Memory and Their Importance for Information Visualization.

Gestalt principles, also known as Gestalt laws, explain how people naturally group and interpret visual elements, and they apply directly to effective information visualization. Here are main ones:

• The principle of proximity encourages viewers to see items near each other as related, which helps organize categories.

• The principle of similarity groups elements with the same color or shape, and makes comparisons intuitive.

• The principle of continuity guides the eye smoothly through aligned data points, improving flow in charts like line graphs.

• The principle of closure lets people mentally complete shapes or trends, which helps fill in missing information.

• The principle of figure-ground ensures the main message stands out from the background.

By applying these principles, you can create visualizations that feel natural, reduce cognitive effort, and let your audiences grasp insights more quickly and reliably.

Grab a greater grasp of Gestalt principles to supercharge your information visualizations and designs of many types.

Scale is critical in charts and graphs since it directly influences how audiences interpret data. A poorly chosen scale can exaggerate or minimize differences and lead to audiences misinterpreting what you intended them to understand. For instance, a truncated y-axis might make small changes look dramatic, while an overly stretched axis can hide significant trends.

Consistent scales permit fair comparisons across categories or time periods. Proper scaling supports accessibility, too, ensuring that visuals communicate accurately to diverse viewers. When you are working with data of very different magnitudes, logarithmic scales can help reveal proportional relationships. Ultimately, scale sets the context for interpretation, and you should apply it carefully to maintain accuracy, fairness, and trust in the message of the visualization.

Investigate further helpful points about how to get information visualization done well for target audiences, in our article Information Visualization – An Introduction to Multivariate Analysis.

The first step in designing an information visualization is to define your purpose and the question you want the data to answer. Without a clear goal, even the most polished chart may confuse rather than clarify. So, ask yourself what insight the audience should walk away with, such as identifying a trend, comparing performance, or understanding relationships.

Once you have defined the purpose, you can choose the right dataset, clean it for accuracy, and decide on the best type of visualization. Begin with clear intention and you can help ensure the final design is relevant, focused, and actionable. It prevents common pitfalls, too, like overloading visuals with unnecessary details that distract from the core message.

Discover a wealth of points to watch out for with presenting to audiences, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Audience awareness is an essential part of information visualization because people interpret visuals differently depending on their background and needs. For example, a visualization designed for data scientists might include statistical models, while one for executives should highlight key takeaways and bottom-line business implications.

When you understand your audience, it helps you adjust complexity, vocabulary, and level of detail so the message you cast lands with those it is intended for, crystal-clear. For instance, avoid technical jargon with general audiences but provide precise labels for specialized viewers. Tailor the design and you can ensure clarity, engagement, and relevance and keep confusion and disengagement at bay. So, consider what your audience values most, and from there make your visualization more effective, persuasive, and impactful in real-world decision-making contexts.

Find out how to own the room you present to through the audience-awareness technique of active listening, in this video with Morgane Peng, Managing Director, Global Head of Product Design and AI Transformation.

Storytelling connects to information visualization by giving data context, emotion, and direction. A chart or graph alone shows numbers, but a narrative explains why those numbers matter and lifts them off the screen to become more “alive.”

For example, a line chart of rising sales becomes more engaging when you frame it as a story of customer growth that happened because product improvements drove it. Storytelling guides the audience through beginning, middle, and end, using data as evidence. It helps to highlight conflict, resolution, or opportunity, and so makes the message memorable. Good visualization does not just display facts; it frames them in a way that inspires action or deeper understanding. So, when you pair visuals with a clear story, you can transform data into insights that stick.

Find out how to cast yourself well as a trustable presence in more effective presentations where people listen to compelling information, including information empowered with storytelling, in this video with Morgane Peng.     

Color plays a vital role in information visualization by highlighting patterns, emphasizing contrasts, and encoding categories. It draws attention to important data points, such as outliers or high-performing segments, essential especially for business stakeholders who want to know important things quickly.

Warm colors like red or orange can signal urgency or intensity, while cool colors like blue or green often suggest calm or positive outcomes. However, misuse of color can distort interpretation or overwhelm viewers. Designers and presenters alike need to use color intentionally and sparingly, and ensure that meaning stays clear and accessible and that meaning is not conveyed through color alone.

Color schemes should consider accessibility for color-blind audiences, too. When you apply it effectively, color enhances clarity, strengthens storytelling, and makes visualizations more engaging and easier to interpret for your audiences.

Explore how to stay on point when you apply color in UX design and presentations.

Designers should use shapes to differentiate categories, highlight relationships, and add meaning beyond color. Shapes work well in scatterplots to distinguish multiple groups, such as circles for one category and squares for another.

In network diagrams, different node shapes can indicate roles or functions. Arrows and directional shapes guide the eye and suggest flow, making them useful in process charts.

Shapes also support accessibility as color alone may not be sufficient, ensuring everyone can interpret the visualization. The key is consistency: each shape must carry the same meaning throughout the design. Use shapes strategically and you can make visualizations clearer, more inclusive, and more effective in communicating complex information.

Discover a treasure trove of fascinating and helpful insights in our article How to Display Complex Network Data with Information Visualization.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.

This seminal book by Edward Tufte emphasizes clarity, precision, and integrity in presenting data. Tufte introduces principles like the data-ink ratio, which guides designers to minimize nonessential elements and maximize the communication of data itself. He critiques misleading practices such as chartjunk and distorted scales while showcasing historical graphics that set enduring standards. For designers, this book is crucial since it combines theory with visual examples, showing how even simple graphics can powerfully convey truth. Its focus on honesty and simplicity continues to influence modern visualization and is especially relevant in fields where credibility is paramount.

Munzner, T. (2014). Visualization Analysis and Design. Boca Raton, FL: CRC Press.

This work provides a comprehensive framework for analyzing and designing information visualizations. Central to the book is the Nested Model, which breaks down the design process into layers of tasks, data, design choices, and validation. This approach helps practitioners align design decisions with user needs and evaluation criteria. Unlike purely theoretical texts, the book offers actionable frameworks and examples applicable to diverse fields, from business dashboards to scientific data. For designers, it serves as both a teaching text and a reference for ensuring visualization quality and usability. Its blend of analysis and practice makes it indispensable for serious visualization work.

Evergreen, S. (2019). Evergreen Data Blog: Storytelling with data visualization. Retrieved from https://stephanieevergreen.com/blog/

This blog by Stephanie Evergreen provides accessible, practice-oriented advice for creating clear and engaging data visualizations. Her posts often feature real-world chart makeovers, illustrating how small design tweaks, such as decluttering, using color strategically, or refining typography, dramatically improve communication. She emphasizes storytelling as a central principle, showing how visuals should connect data with human understanding. For designers, evaluators, and educators, this blog is especially useful because it translates theory into everyday application. Its approachable style and frequent updates make it an ongoing resource for keeping skills sharp and applying best practices without jargon or unnecessary complexity.

Datawrapper. (n.d.). Datawrapper Blog. Retrieved from https://blog.datawrapper.de/

The Datawrapper blog is a practical, open resource created by the team behind the Datawrapper charting tool. Posts explore visualization design principles with case studies drawn from journalism, research, and public communication. Topics include choosing the right chart type, improving accessibility, using color responsibly, and avoiding common design pitfalls. Each post provides actionable insights, often with interactive examples that readers can replicate. For designers, the blog serves as both inspiration and a guide for implementing best practices in professional contexts. Its focus on real-world use cases makes it one of the most relevant ongoing resources for visualization practitioners.