How Many Colors Are Too Many In Your Chart?

Nine.  Well, so says Scott Berinato, in his book Good Charts. He bases this number on a conversation he had with Tamara Munzner, a data visualization expert and professor of computer science at the University of British Columbia.  Here is an example Gantt chart with more than 8 colors.

Munzer is quoted (in the footnotes) as saying “There are fewer distinguishable categorical colors than you’d like. You don’t get more than eight.”

This is an opinion — a subjective view from someone who has studied data visualization for over a decade, but an opinion nevertheless.

Humans are all different, and are going to have a different threshold for where that too-many-colors-line is, but the number eight feels right…maybe even a little on the high-side for me (my brain is more feeble than most).

Have you ever heard the expression “can’t see the forest through the trees?”  This is perfect for explaining what we experience when we’re presented with a chart that has too much complexity. We have a hard time visually and mentally extracting what the most important information is. Instead our brain wants to look at everything at once.

Based on Munzer’s thoughts, and his own research, Berinato adds an accentuating comment that, “The threshold at which individual data points melts into aggregate trends is surprisingly low.”

This makes sense, right? For that reason, we, as the ones creating these plan communications, need to be very conscious of over-using color to illustrate attributes in our data.

The principle can be applied beyond color, as well. “Simplicity is courageous,” is something also written, very smartly, by Scott Berinato in Good Charts. Any sort of complexity will take away from the important information — the “story” — that we’re trying to get across, and we should do everything we can to simplify for the sake of clarity.

Is Munzer right? Again, it’s subjective. You have to ask your audience.

When you are building your plan communications, use your judgment. Don’t accept what is being asked of you as the right way if it doesn’t make sense. Ask questions to find out what it is your audience wants, and needs, to see. Go through a process of iteration and drafts. From that point you can work to find better solutions within the designs of your data visualizations.

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