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We want to ensure that all users can learn and interact with data. Often one visual may be great for some users, but inaccessible for others.
The best case scenario here is to have a text-based equivalent of every data visual. This text summarizes and explains the visual and key findings. Learn more about the importance of defining key findings.
In general, beware of overly complicated graphs. The increase in cognitive load for users trying to decipher a new type of visual may not be worth it.
Test your visuals to ensure everything is large enough to view and click. Tiny shapes or widgets are difficult to click.
Aim to use vetted, widely-used visuals. Smaller, homegrown, custom visuals may not be reliable enough for public reporting (or may require additional purchase).
For more guidance, check out these tools:
Maps are particularly challenging and should be tested for accessibility and color contrast.