By Michela Tjan
To learn more about the Open Visualization Academy (OVA), visit https://openvisualizationacademy.org
Data visualization is often celebrated for its power to clarify complexity. A well-designed chart can reveal patterns, change assumptions, and support better decisions. But clarity for whom? Too often, visualizations are created with a narrow audience in mind; one that assumes perfect vision, fine motor control, access to the latest devices, and familiarity with visual conventions. When accessibility is treated as an afterthought, data that should inform instead excludes.
Accessibility in data visualization is not just a technical consideration or a box to check for compliance. It is a design philosophy: one that asks us to broaden our understanding of our audience and rethink what it means for a visualization to be truly effective.
The topic is explored further in one of OVA’s upcoming courses, Accessibility in Visualization by Frank Elavsky, a PhD candidate and researcher at the Human-Computer Interaction Institute at Carnegie Mellon University. If you want to get an idea of what to expect from Frank’s course, watch this brief presentation of his at The North American Cartographic Information Society (NACIS):
What Accessibility in Data Visualization Really Means
Accessibility refers to the practice of designing visualizations that can be perceived, understood, and interacted with by people of diverse abilities. This includes, but is not limited to:
People with visual impairments, including color vision deficiency and low vision
People who rely on screen readers or keyboard navigation
People with cognitive or learning differences
Users accessing visualizations on small screens, low-bandwidth connections, or assistive technologies
An accessible visualization does not diminish aesthetic quality or analytical depth. Instead, it prioritizes communication, ensuring that insights are not locked behind visual barriers.
Common Barriers in Visualization Design
Many accessibility issues arise not from malice or neglect, but from default design habits:
1. Color-Dependent Encoding
Relying solely on color to encode information is one of the most common pitfalls. Approximately 8% of men and 0.5% of women worldwide experience some form of color vision deficiency. When categories or values are distinguishable only by color, entire segments of the audience may miss critical information.
Better approach: Combine color with other channels such as shape, texture, labels, or annotations.
2. Low Contrast and Small Text
Stylish but low-contrast palettes and tiny labels may look elegant, yet they significantly reduce readability, especially for users with low vision or those viewing on mobile devices.
Better approach: Follow contrast guidelines (such as WCAG recommendations) and prioritize legibility over minimalism.
3. Interaction-Only Insights
Interactive visualizations often hide insights behind hover states, tooltips, or gestures that are inaccessible to keyboard-only users or screen readers.
Better approach: Ensure that key insights are available in static form or through accessible interaction patterns.
4. Missing Alternative Text and Data Access
Visualizations shared online frequently lack alt text, captions, or access to the underlying data which render them invisible to screen readers.
Better approach: Provide meaningful descriptions and offer data tables or downloadable datasets alongside visuals.
Accessibility as a Design Advantage
Designing for accessibility does more than serve users with disabilities, it improves usability for everyone.
Clear labels and annotations reduce cognitive load
Redundant encodings increase interpretability
Thoughtful hierarchy improves storytelling
Accessible design encourages intentional, audience-centered choices
In practice, many accessibility best practices overlap with good visualization principles. When designers are forced to articulate what a chart is saying in words, they often discover ambiguities or weaknesses in the visual encoding itself.
Practical Guidelines for More Accessible Visualizations
Accessibility does not require specialized tools or radical workflows. Small, intentional changes can make a significant difference:
Test color palettes using color blindness simulators
Avoid conveying meaning through color alone
Use descriptive titles that communicate the main insight
Add annotations to highlight key takeaways
Provide alt text that explains what the chart shows, not just what it looks like
Ensure charts are navigable via keyboard where interaction is involved
Share data sources and tables whenever possible
Most importantly, designers should ask early and often: Who might be excluded by this design?
Toward a More Inclusive Visualization Practice
Accessibility is not about lowering standards or simplifying complexity. It is about respect—for data, for audiences, and for the diverse ways people engage with information. As visualization designers, researchers, and educators, we have the opportunity (and the obligation) to ensure that insight is not a privilege reserved for a few.
When data is truly accessible, it fulfills its promise: not just to inform, but to empower.
If you want to keep learning about accessibility in visualization before the launch of the Open Visualization Academy on January 31st, you can consult great online resources such as Chartability and Do No Harm Guide: Centering Accessibility in Data Visualization.
