Yet their expansive popularity doesn’t ensure that data visualizations are well done or even useful.
You can find entire pages dedicated to poorly crafted data visualizations. Our personal favorite is WTF Visualizations.
How do you stop this from happening to your own data visualizations?
Don’t be a data viz dummy. Read through these tips to build and better understand how data visualizations should look.
Don’t make it hard on the user
The user is number one. It doesn’t matter if your data visualization make sense to you and your team; if it isn’t clear to the user, it’s useless.
Make sure you data viz is comparable and consistent. If you have many data visualizations on a page, make sure that they work together so your user doesn’t need to re-adjust the margins for each new image. The best way to do this is to create a wireframe of your data visualization so you can review and prevent f!&k ups without investing a lot of time or resources.
Organize your data sequentially, alphabetically, by value.
Draw trends or provide context when needed. Users shouldn’t have to wonder why their was a sharp decrease in a visualization. Explain it to them.
Keep your data readable. Don’t overcrowd with plot points. Make sure that you are only using the pertinent data and not including excessive information that can draw attention away from your main point. This is more important for static visualizations, since you aren’t able to manipulate the variables yourself.
Follow normal conventions. Up and right is positive, down and left is negative. We all understand the desire to be creative, but breaking conventions makes it hard on the user and can hinder your data communication.
Sequence your data based on color. The color density should match your data. The darker, more vivid colors representing a higher value and lighter, less vivid representing a lesser value.
Don’t forget the background color and label colors. You can change a data visualization drastically with your background. Make sure to keep the data understandable.
Check your color connotation. This might not be important for all data sets, but for some, you’ll need to think about color connotations. For example, if you are visualizing a car’s capability to stop from varying speeds, it might be better to use red versus green.