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How do you design data visualizations for different types of disabilities and impairments?

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Data visualization is a powerful way to communicate insights, trends, and patterns from complex data. But not everyone can access or understand the same visual formats, especially if they have disabilities or impairments that affect their vision, hearing, cognition, or motor skills. How do you design data visualizations that are inclusive, accessible, and effective for different types of audiences? In this article, you will learn some basic principles and best practices for data visualization accessibility.

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1 Know your audience

Before you create any data visualization, you need to know who will use it and what their needs are. Different types of disabilities and impairments may require different adaptations, such as alternative text, captions, color contrast, keyboard navigation, zooming, or audio feedback. You can use tools like personas, user stories, or surveys to understand your audience's goals, preferences, and challenges. You can also test your data visualization with real users or accessibility experts to get feedback and improve your design.

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Bill Shander

Author of "Stakeholder Whispering: Uncover What People Need Before Doing What They Ask", keynote speaker, workshop leader, LinkedIn Learning Instructor. Information design, data storytelling & visualization, creativity.
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One of the most important things to be aware of is that at least 5-6% of the population has visual impairment. And this doesn't include the massive population of people above a certain age who struggle with low contrast colors and small fonts. Really, you can count on up to 30% of your audience having at least a low level of visual impairment, if you include that broader group.

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2 Choose the right format

The format of your data visualization should match the type and complexity of your data, as well as the message you want to convey. Some formats are more accessible than others, depending on the audience and the context. For example, bar charts, line charts, and pie charts are simple and easy to understand, but they may not work well for people with low vision, color blindness, or dyslexia. You may need to use alternative formats, such as tables, text summaries, or audio descriptions, to supplement or replace your visualizations. You can also use interactive features, such as filters, tooltips, or sliders, to allow users to customize and explore your data.

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Bill Shander

Author of "Stakeholder Whispering: Uncover What People Need Before Doing What They Ask", keynote speaker, workshop leader, LinkedIn Learning Instructor. Information design, data storytelling & visualization, creativity.
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If you're going to use visuals, which you should do, since 90% of your audience will gain great value from that visual display of data, at least include thorough and descriptive explainers of the visuals in the alt-text or other screen reader html tags to empower the visually impaired to understand the insights. The full table may be helpful, but a descriptive sentence or three is even better to share the meaning behind the numbers.

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George Fullegar

Growing the next generation of data professionals
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I tend to suggest people make use of a colour blindness simulator such as Coblis (https://www.color-blindness.com/coblis-color-blindness-simulator/) in the initial design phase to ensure colour choices are appropriate for all audiences.

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3 Follow accessibility standards

There are several standards and guidelines that can help you make your data visualizations more accessible, such as the Web Content Accessibility Guidelines (WCAG), the Accessible Rich Internet Applications (ARIA), and the Data Visualization Accessibility Checklist. These standards provide recommendations and best practices for various aspects of data visualization accessibility, such as color, text, layout, structure, navigation, and interaction. You can use tools like validators, checkers, or simulators to check your data visualizations against these standards and identify any issues or errors.

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4 Use clear and consistent language

The language you use to present and explain your data visualizations is also important for accessibility. You should use clear, concise, and consistent language that is appropriate for your audience and your purpose. You should avoid jargon, acronyms, abbreviations, or technical terms that may confuse or exclude your users. You should also use labels, titles, legends, captions, and annotations to provide context and meaning for your data visualizations. You should also use alternative text or audio descriptions to describe your data visualizations for people who cannot see them.

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5 Consider cultural and ethical aspects

Accessibility is not only about technical or functional aspects, but also about cultural and ethical aspects. You should consider how your data visualizations may affect or influence your audience's emotions, values, beliefs, or behaviors. You should respect your audience's diversity and avoid stereotypes, biases, or discrimination. You should also be transparent and honest about your data sources, methods, assumptions, and limitations. You should also provide options and opportunities for your audience to give feedback, ask questions, or share their opinions.

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George Fullegar

Growing the next generation of data professionals
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I find this colour wheel by information is beautiful helpful when considering colour choice based on cultural audience: https://infobeautiful4.s3.amazonaws.com/2010/04/2552_ColoursInCulture-3.png

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6 Keep learning and improving

Data visualization accessibility is not a one-time task, but a continuous process of learning and improving. You should always monitor and evaluate the performance and impact of your data visualizations, and make adjustments or updates as needed. You should also keep up with the latest trends, technologies, and research on data visualization accessibility, and learn from other examples and best practices. You should also seek feedback and collaboration from your audience, your peers, and your stakeholders, and incorporate their suggestions and insights into your design.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Kelsi Johnson

Technical Program Manager | Certified Professional Coach | Data Geek | Mystery Solver | Senior Dog Adopter
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Designing accessible data visualizations requires more than just using shades of gray. While grayscale can improve contrast, people with color blindness may struggle to distinguish between visual elements. Designers should use additional design elements like text labels, patterns, or textures to convey information and create contrast. Tools like Color Contrast Analyzer can help check accessibility. However, solving for color blindness can make visualizations harder to interpret for people with other conditions, like autism spectrum disorder or dyslexia. To strike a balance between accessibility and usability, designers should use multiple design elements, clear labels, test with a diverse group of users, and always be seeking feedback.

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Carla Intal

Data Insights at LinkedIn | Oxford Data Science MSc
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A very specific use case for building data visualization for color-blinded people is to use different shades. Example: if using a stop-light (green/yellow/red) color palette, using a medium shade of red and medium shade of green will appear the same shade of gray to some color-blinded folk. So a trick would be to use a dark-green and a light-red to show contrast, which is also distinguishable to those who are color-blind. More tips here from the Tableau team. https://www.tableau.com/blog/examining-data-viz-rules-dont-use-red-green-together

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How do you design data visualizations for different types of disabilities and impairments?

1

2

3

4

5

6

7

1 Know your audience

2 Choose the right format

3 Follow accessibility standards

4 Use clear and consistent language

5 Consider cultural and ethical aspects

6 Keep learning and improving

7 Here’s what else to consider

Data Visualization

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How do you design data visualizations for different types of disabilities and impairments?

1

2

3

4

5

6

7

1 Know your audience

2 Choose the right format

3 Follow accessibility standards

4 Use clear and consistent language

5 Consider cultural and ethical aspects

6 Keep learning and improving

7 Here’s what else to consider

Data Visualization

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