So I’m going to make a statement that I’m sure some people are going to disagree with: good data visualization should be boring.
Well, at least kind of boring.
I’ve had a lot of conversations with a lot of people over the last few years or so about data visualization: why it’s important, what constitutes good and bad, and examples of its application in both problematic and very effective ways.
A salient point someone made to me once is that part of the problem with the practice of data visualization is that it isn’t viewed as a standalone discipline; it’s simply done, in high school math classes, university courses, or even in the workplace by professionals, and usually assumed that people will just pick it up without discussion around it and its proper application.
I think this is gradually starting to change, as with all the talk (or hype, depending on your point of view) around “Big Data”, analytics is becoming more mainstream, and data visualization is as well as a part of it. I also think dataviz is beginning to – gradually, very gradually – become viewed as a standalone discipline, with courses now being offered in it, and the “data visualization evangelism” of academics such as Edward Tufte and Alberto Cairo and work of practitioners like Stephen Few and Mike Bostock helping to raise awareness of what’s doing it wrong and what’s doing it right. This, along with others creating visualizations which go viral or delivering inspirational TED talks, are doing a lot for visualization as a practice.
The thing I found when I first started to get into dataviz is that even if you’re good with data that doesn’t necessarily mean you’re good at visualizing it. This is because, in addition to working with data, doing proper visualization involves questions of design and also the psychology of perception.
Less is More
I’m a minimalist, and therefore take what I call a functionalist perspective of data visualization. That is to say, the purpose of visualization is to most effectively represent that data so that it can be understood by the audience both most quickly and easily.
As such, I feel that good data visualization should be somewhat dull, or at least somewhat dry; in terms of depicting information and people perceiving it, it is usually the case that simpler is better. This is illustrated in principles like Tufte’s data-ink ratio.
So, look at the charts below. Which is more visually appealing to you? Which is simpler? Which one depicts the quantities such that you are able to interpret them the most quickly, accurately and with the most clarity?
If you’re like me, you’ll say the one on the right, which is a better visualization, even though it may not be as visually appealing to some. Most often you’re better served by a simpler, cleaner visualization (or perhaps several of them) than a lot of complexity and visual noise that doesn’t add to the reader’s understanding.
Never say always
That being said, as I mentioned, choices around data visualization are ultimately ones of design. I do believe that there are some hard and fast rules that should never be broken (e.g. always start the y-axis at 0 for bar charts of strictly positive values, don’t represent data with the same units on a secondary y-axis, never use a line chart for categorical data), however I also believe there are some that are more flexible, depending on what you want to accomplish, and your audience. Should you never, ever, use a pie chart? No. Some people are more comfortable with pie charts just from their familiarity with them. Is a bar chart a better choice in terms of representing the data? Yes. But that doesn’t mean there aren’t exceptions (just don’t make a 3D one).
The same individual that made the observation about dataviz not being taught also pointed out to me another factors that can influence design choices: what she called chart fatigue. Is the bar chart the best way to plot a single metric across a categorical variable? Almost always, yes. But show a room full of businesspeople bar chart after bar chart after bar chart and anyone can tell you that they’re all going to start to look the same, and interpretation of them is going to suffer as a result. Plus you’re probably going to lose the interest of your audience.
Practice makes perfect
In conclusion, I think that awareness of data visualization is only going to get better as companies (and the average consumer) become more “data savvy”. It is my sincere hope that people will give more and more emphasis, not only to the importance of visualization as a tool, but also to the design choices around it, and what constitutes good and bad depictions of data.
For now, just remember that data visualization is ultimately all about communicating and having your reader understand, not necessarily wowing them (though both together are not impossible). And sometimes, that means boring is better.