How can you ensure your data analysis is accessible to those with disabilities?

2 Choose appropriate formats and layouts

In order to make your data analysis accessible, the second step is to choose appropriate formats and layouts. Depending on the data type, purpose, and audience, you may opt for various formats and layouts, such as graphs, tables, charts, maps, or dashboards. However, not all formats and layouts are equally accessible for everyone. For instance, people with visual impairments may have difficulty reading small fonts or low contrast information. Additionally, those with cognitive impairments may have difficulty interpreting abstract symbols or following complex or cluttered layouts. To select suitable formats and layouts, consider using high contrast and large fonts for text and labels. Additionally, use simple shapes, colors, and symbols for data points and categories. Furthermore, use descriptive titles, captions, and legends for your data analysis elements. Moreover, utilize white space and alignment to create a clear and logical structure for your data analysis. Finally, make sure that your data analysis is accessible on different devices and screen sizes through using responsive design principles.

3 Provide alternative and supplementary information

The third step to make your data analysis accessible is to provide alternative and supplementary information. This can be in the form of text, audio, video, summaries, explanations, or annotations. These extra elements can help your audience access, understand, and interact with your data analysis in different ways. To provide alternative and supplementary information, you can use alt text, captions, transcripts, or audio descriptions for visual or auditory elements. Summaries and explanations can be used to highlight the main points of your data analysis. Interactive features such as filters, sliders, or tooltips can allow for exploration or customization. You can also use feedback mechanisms like comments, ratings, or surveys to get opinions or questions from your audience.

4 Test and evaluate your data analysis

Testing and evaluating your data analysis is the fourth step to making it accessible. This can help identify and fix any issues that may affect your audience's experience, as well as gather feedback for improvement. To do so, consider using accessibility tools such as validators or checkers to check for any violations or problems. You could also use user testing methods like interviews or surveys to observe and ask your audience how they interact with your data analysis. Analytics tools like metrics or dashboards can measure and monitor performance and impact, while iterative processes such as prototyping or reviewing can refine and optimize your data analysis based on findings and feedback.

5 Follow accessibility guidelines and standards

Following accessibility guidelines and standards is the fifth step to make your data analysis accessible. This includes sets of principles, criteria, and techniques that can help you create and maintain accessible data analysis. It can also ensure your data analysis complies with best practices and legal requirements for accessibility, as well as demonstrate your commitment to accessibility. When considering accessibility guidelines and standards, use the Web Content Accessibility Guidelines (WCAG) as a reference for creating and evaluating web-based data analysis. Additionally, use the Accessible Rich Internet Applications (ARIA) specification for creating interactive data analysis, the ISO/IEC 40500:2012 standard for implementing web-based data analysis, and the Section 508 standards or EN 301 549 standard for complying with federal or European accessibility requirements.