We are so busy creating sexy charts to illustrate some random data that we often forget to check if our chart really answers the question. Heck, most of the times we don’t even have one. Chart first, ask questions later.
One of the major differences between tables and charts is this: a tables says “here is your data, now go find the answers (they must be here, somewhere)”, while a good chart says “here is your answer”.
The more precise and clear our question is, the easier is to select the right data and the right chart. Let me give you a recent example. Robert Kosara, at EagerEyes, discusses the “swing states”. Several readers contributed with great alternative displays but, as I commented, there is a fundamental issue: if we want to see “the swing” that’s what should be displayed, not the election outcomes. This:
is different from this:
In the first chart, we can see that the Republican candidate won in Alaska in 1968. In the second chart, we know that, in 1968, in Alaska, there was a different outcome – a “swing”.
Sure we can infer the swing from the first chart, and our answer about the “swing states” is there somewhere, but only the second chart can provide a clear and concise answer.
It is prudent to keep all the data (who knows what the future will bring, right?), but we should always be aware of our loss aversion tendency, and make sure that our chart is displaying what it was designed for. Edit your chart without mercy, and let redundant or plainly useless data go. That’s the only way to highlight the patterns we are looking for.
Now, you may want to reevaluate your question to allow for a broader answer. That’s ok, but do it carefully. Add detail without breaking the pattern. For instance, we may want to know more about the direction of the swing:
Bottom line, make sure that what your chart says is aligned with what you asked. If you can use your question in the chart title that’s a good sign that you are on the right track.
Can a picture of a nude person improve your decision-making processes? (Please don’t say “yeaaaaah”.) Probably not, but if you need a good attention grabber a picture of a naked body is your best bet. Make sure you’ll add one to your next sales report.
Because, if you are using those glossy 3D pie charts from Crystal Xcelsius (or Dundas, or…), you are applying the same principle, safe-for-work version. Your underlying message to your audience is “you are so dumb that you don’t even understand a simple chart with a clear message. I have to use charts that obfuscate the message, but they grab your attention and that’s all that matters. Let me take my shirt off too.”
I’m a business analyst I usually try to create charts that can support the decision-making process. I am not a graphic designer, trying to illustrate a story and get reader’s attention.
When you are in a corporate environment you can enjoy the attention of your audience (the organization is paying for it…). Also, information is shared among people with similar professional profile that at least know what the basic concepts are.
On the contrary, in a magazine, your readers don’t know or may not care about your subject. How do you grab their attention? Your best option is to add a photo of a naked male/female. Can you justifiably use it to illustrate the story? Do it. You don’t? OK, try other attention-grabber devices, like a nice, glossy pie chart (not as satisfying, though).
These are different needs, but we, the so called “visualization experts” often fail to aknowledge that.
Eye-Catching Charts vs. Decision-Support Charts
Eye-catching charts are used to get the reader’s attention by providing some sort for light entertainment. Their primary focus is on the format. They use many colours and and large textured surfaces. Because of that, their data density is low and context is almost absent. A 3D pie chart is the typical eye-catching chart.
Decision-support charts focus on the data and should be “invisible” (the audience sees the patterns, not the chart). There are no textured surfaces and colors are used to highlight specific details. The display real estate can be filed with context data, maximizing data density. The typical decision-support chart is, obviously, the scatterplot.
Charts for Analysis and Charts for Communication – Not Anymore?
This is the traditional split. After the analysis stage, the analyst should prepare his/her findings for the communication stage. But vendors like Microsoft and Business Objects have been short-circuiting this process, selling the idea that all you need is form, not content, and whatever stage you are in, you must have a nicely textured 3D chart.
These charts are sold as “professional-looking” and let’s accept that for a moment. They are professional-looking from a graphic designer perspective, but they are completely useless in a corporate environement where you have masssive amounts of data to deal with. I’m sorry to say, but the more textured charts you have the dumber you look.
Pin-Up Charts Don’t Belong Here
I don’t really care about pin-up charts (charts that the media pin up on their pages…). Sometimes they are amusing (not sexy, unfortunately) but they just don’t belong in a corporate environment. If you need attention, make better use of your data to find its inner beauty or use a photo of a proper pin-up.
