We spent the last couple of days working with a client on displaying data for real-time dashboards. It got me to thinking: Are there an implicit assumptions and mental habits that people bring to data interpretation? And if so—are there some basic practices to consider for visualizing data?
Which isn’t to say this is a right and perfect way to display any particular data; there is room both for creativity and structure. (Check out Information Aesthetics for examples of creative data visualization.) But in the world of management communication, it can’t hurt to be aware of your audiences’ ingrained assumptions. You want the smoothest path to your important points. The risk is in missing your tiny window to focus a frazzed executive’s mind on your point--and finding your carefully constructed analysis get sidetracked.
Here’s a starter list of these embedded assumptions:
1. Axes are often the last thing people look at in a chart. They expect time to progress from right to left and linear scales that start at zero. If two charts are adjacent, they will probably assume the axes and scales are the same. When it comes to the famous two-by-two consulting matrix, good things happen in the upper-right; bad things are in the lower-left. That said, I’m mystified that the famous BCG growth/share matrix’s insists on rejecting my new rule.
2. Fluff. Dressing up your display implies you aren’t comfortable with the data’s ability to stand on its own or you don’t have much to say. This can include clip art, data incorporated into pictures, and animation. USA Today is particularly good at this. Check out a couple of examples from their Snapshots section. They have less than three numbers to communicate, but fill it up with eye-catching graphics.
3. Point of focus. Most data displays have a clear point of focus for the viewer, whether the presenter intends it or not. It could be the peak in a line chart, values crossing over zero, or a sudden change in values. In a chart like this (below), your intention may be to highlight the general growth trend -- but you can’t avoid the inevitable questions about the drop after 2000. You can short-circuit these off-the-topic questions with an explanatory footnote or annotation. Ask yourself: what is the main point I want the reader to get, and what else will my data presentation imply?
4. Proximity and size. Placing information close together suggests a connection. Sometimes accidental proximity can cause confusion. You might present two unrelated phenomena next to each other and the audience will automatically try to draw a connection (e.g. dogs have big teeth; teeth are good for crunchiing carrots. Audience thinks: dogs must like to crunch carrots). I just ran across Live Plasma, a great site that lets you enter a musical artist (or band, movie, director, or actor) then shows you related artists. The designers of this data visualization do a great job of building on our data display expectations by using size and proximity to show related artists.
