Using color strategically and sparingly is often the quickest and easiest change to improve your data communications. Today’s quick post is a cautionary tale about not using color strategically—both in quantity and color choice.

I recently encountered the following graph. At first glance, how does it make you feel?

If you’re like me, I feel alarmed. I feel even worse after examining the chart title and legend—warehouse accuracy rates, encoded as red. This doesn’t seem very positive!

The reason I feel on alert is because of cultural associations with the color red. Western audiences often interpret red as a signal of danger, anger or alarm. It can also be associated with love, excitement, or passion, as we explored in a past SWD challenge. In this example, my brain didn’t immediately associate “accuracy rate” with “passionate love,” so I assumed that this chart was delivering some bad news.

As it turns out, I was mistaken. This data actually represents positive performance of warehouse fulfillment. These warehouses are filling orders accurately about ~90% of the time. (A caveat here: without knowing more about the underlying context, we can’t be certain that 90% accuracy should be considered a good score, but for illustrative purposes, let’s assume that it is.)

To avoid the knee-jerk reaction of alarm (and improve the visual’s overall effectiveness by bringing the data to life), I’m going to make a few simple changes to this graph:

Utilize a different color palette. There is a positive/negative connotation to this data, so I’ll elect to use blue to signal the positive (accurate) and its complement on the color wheel—orange—to accentuate the negative (errors).
Eliminate clutter. The original graph has many elements (gridlines, harsh bolding, rotated x-axis labels, legend placement) that make it appear more complicated to process than it really is. I’ll strip away these non-essential elements, leaving only those that add enough value to make up for their presence.
Use words effectively. If I want my audience to understand that this data is positive, I shouldn’t assume that they will come to that conclusion on their own. I’ll not only state it in words, but tie the words to the data it describes using similarity of color.

Check out the difference! Does the revised graph still evoke feelings of alert? Likely not.

The “after” graph still has room for improvement. The data could be sorted differently and there's an opportunity to add additional context and a call to action. You may see other things you’d approach differently as well. Join me Thursday, March 24 at 11AM Eastern time for a live chat on our YouTube channel, where I’ll continue to transform this visual into a data-driven story. See you there—click the link above to set a reminder and access the event!