Data visualizations come in all shapes and sizes, and range from the simple to the complex. Some are destined for the public web, others for internal eyes only. Some require sophisticated visual analytics software to produce, and others… Excel. In any case, some will work and others won't.
Some, in fact, will be so successful they'll go viral. Sadly, some will be so bad they'll go viral as well. You don't want this latter situation to happen to you -- unless you think it's OK to turn your company into a public laughingstock.
The fact is, you may not ever have had to think about presenting your data visually before, but you do now. As businesses turn to data-driven decision making, you carry the onus for delivering insight and intelligence in a way the business can absorb easily -- meaning, through data visualizations.
A visual message, done well, delivers far more punch than text -- or numbers -- alone. Study after study shows this, and experts scream it from on high. Visualizations give people something around which they can wrap their brains.
The good ones compare multiple values and put information into context, said Randy Krum, president of InfoNewt and Cool Infographics blogger, during his "Effective Infographics" session at yesterday's INFORMS Analytics Conference in San Antonio. The bad ones misrepresent, misinform, or otherwise totally miss the mark.
Krum, who makes a living at "helping companies see their data," shared eight design tips for making sure your data visualizations -- self-created or outsourced -- don't implode on you. Click on the image below to start a slideshow with his tips, the first being accuracy.
Tip 1: Accuracy
"If you mess up your data visualization, you lose your credibility" -- and yet data visualizations are littered with mistakes, Krum said. For example, using circle size to show that one category is three times the size of another is correctly done by multiplying the area times three. Yet designers often enlarge the circle by tripling the diameter, and thus misrepresenting the data.
Do you have advice on how to make data visualizations shine? Share below.