If viewers can’t read your graph, why bother making it? Accessibility is at the top of my priority list. A lot of things go into a data visualization’s readability. In this post I’ll explain how to objectively measure text readability (and lower your graph’s reading level).
There’s a misconception that accessibility takes all day, that’s it’s costly, or that it’s complicated. Those are all false. In this blog post, you’ll learn about lowering the numeracy level. Then, you’ll see a case study that combines several accessibility quick wins.
How do we make our graphs more accessible? There’s a misconception that accessibility takes all day, that’s it’s costly, or that it’s complicated. Those are all false. We need to lower the reading level. Not because our readers dumb, but because they’re busy. They need to be able to understand what you wrote the first time—not the second, third, or fourth read-through.
You want to display a lot of historical data–great! But sometimes we have so many points in time that our graph’s labels get smooshy. In this post, I’ll show you a before/after data visualization makeover in which we selectively labeled a few key milestones in order to tell our story (and make the graph more legible).
Whether you’re visiting a web site or a listening to a presentation, you’ve probably thought about data visualization as a helpful way to digest complex information. At its best, “data viz” communicates data simply and efficiently using a combination of graphs, graphics, and other forms of design. So what makes a good graph great?