Coming from the academic and computer science side of visualization, I always assumed that it would be self-evident to anybody that visualization is first and foremost useful, and only happens to also produce nice pictures. Alas, this is not actually the case. To most people, visualization means pretty pictures first, and maybe also a fact or two. We have to fight that or risk the trivialization and marginalization of visualization as an analytic tool.
Insight, not Pictures
Manuel Lima’s Information Visualization Manifesto has caused quite a stir, and it’s an important wake-up call. Lima’s points are excellent, and I can only recommend you read both the article and the comments. I couldn’t possibly agree more with absolutely everything he is saying.
The key problem is that people are much more interested in clicking through interesting pictures than learning about actual analysis work done using visualization. When I complained earlier about the state of visualization on the web, I thought that the problem was simply one of representation. Surely, there is a lot of great analytic work out there, we just have to write about it! But it’s not easy to find them. Stephen Few also recently asked his readers for visualization success stories, with an underwhelming response.
I noticed the same thing at the OECD Seminar a few weeks ago: the main goal seemed to be to make something colorful out of the otherwise dry numbers. Whether the visualizations were any good was secondary, and was not even a question most people there had even thought of asking.
Artistic vs. Pragmatic Visualization
Lima makes the distinction between data visualization and data art. The criteria are not perfectly clear-cut, and it’s easy to discuss a particular example ad nauseam. I did write about possible criteria a while ago though, and think that the distinction is pretty clear. What is the main question: Do you want to communicate the data or a concern? Do you care about perceptual effectiveness or beauty? Is the data a given or is showing the existence of the data part of the point?
There is no ‘right’ answer to these questions, they simply lead you in two very different directions. Some people see a clear distinction between data visualization and data art as a threat, but I don’t see why that would be the case. There is nothing wrong with data art, it’s just not pragmatic data visualization. So let’s stop calling it that.
Not Visualization, but Visual Analysis
We need a new term. Visualization has been around for too long, has too many meanings, and has been used by artists to describe many other things. Visual Analysis is perhaps a better term; it does not have that baggage and contains a key aspect of actual data visualization: analysis. It’s also less politically loaded than the closely related visual analytics.
Visual analysis is not primarily about the pictures, but about finding ways to use our powerful visual systems to analyze data. It’s analysis done in a visual way. It’s visual exploration, visual data analysis, and visual presentation of results.
Visual analysis is difficult. While we certainly want to use visual means of communication to make data accessible, that’s not all there is. Visual analysis can involve incredibly complex and difficult data and visual concepts. There is a common assumption that visualization is easy to understand, but that is only the case for some types of data and some uses.
Visual analysis is not trivial. You have seen the bar and pie charts, but do you actually know what they mean? Do you know how to use them to tease the relevant information out of your data? Can you handle more than two dimensions of data and still find meaningful structures? There is so much more to visual analysis than what Excel offers you.
These things are not just important to make us feel better. If we want to be taken seriously as a field, receive funding for research, and expect businesses to pay for what we are doing, we have to communicate this. Visual analysis is not the clipart to cover up your lack of ideas. Visual analysis is the tool that gives you insight, the microscope that lets you find the hidden structures, and the link between your data and your brain.