Recently, a contact shared the following image with me, along with his thoughts. I found both amusing, so thought I'd share with you here, along with some of my own thoughts and a makeover:
Commentary accompanying the visual: This seems like some seriously simple data to present, but SOOO poorly executed. Looking at it hurts my head and leaves me with nothing but questions:
- How much time does it take others to figure out the color pattern(s)?
- Is there really even a pattern?
- Why are the two legends/color-schemes different? Don't make me work so hard!
- Why use donuts/pies instead of some simple paired bars/columns, or even just a pair of lines (i.e., a simple histogram)?
No matter what your content, this is the sort of reaction we should work to avoid in our data visualizations. In this case, it seems the color and donut form is meant to make the data more visually interesting, but it hinders our ability to understand the data.
There are a number of lessons we can employ here to make this data easier to comprehend:
- If there is an intrinsic order in your categories, leverage it. In this case, the 2011 data has categories in order of increasing days away from home (starting at the lower middle left of graph with the light green segment and working clockwise around), but somehow neither this ordering or the colors of the categories carried over to the 2012 graph; rather, this graph appears to be sorted numerically by category. This makes comparing the segments of the pies even more difficult than it would otherwise be. Speaking of which...
- Don't make people compare segments of two different pies (or donuts, in this case - substitute your fave dessert dataviz). Our eyes have a hard time measuring angles and areas: this difficulty is amplified when we're meant to do it across different pies/donuts, where the pieces are in slightly different places and there is no consistent baseline.
- Put the things you want to compare close together. Physical distance between the things we're meant to compare makes comparing those things more difficult. In this case, a bar graph would allow us to put 2011 and 2012 right next to each other so we can get an easy visual comparison.
- Use color strategically. Don't use color to make something colorful; rather, use color sparingly and strategically to draw your audience's attention to where you want it.
- Tell a story with your data! Don't assume your audience will want to look at the data and make up their own story. If you look at the full article, the point they are trying to make is that consultants are traveling less in 2012 than prior years. I'm not actually sure this data shows that (it could be that the other groups surveyed are traveling less but the consultants are traveling just as much - we don't have that breakdown of the data to see). At any rate, I'd suggest making the point more clearly with the data and actually calling out the takeaway within the data visualization to help your audience know where to look for the evidence of what you're telling them.
Here's an alternative view of the same data, employing the lessons I've outlined above: