Data Visualization: Honest, Powerful Interpretative Design

I have seen the future of interpretative design, and that future is data visualization. I'm talking tables of figures. Huge swaths of words. Volumes of dry-as-dirt content.

On the face of it, data visualization is just about the least sexy thing imaginable. Entertain the idea of an exhibit based on Gantt charts and spreadsheets, and your head might just explode. And yet, over the last few years, as the web has unlocked piles of information, a quiet group of math-minded designers are figuring out how to interpret the vast impersonalness of data and make it both beautiful and meaningful.

I met one of these data artists last year while visiting a friend/journalist at the New York Times. His name is Mark Hansen, a UCLA statistician, and he was working on the finishing touches of the installation of Moveable Type in the lobby of the new Times building (shown above).

Moveable Type, like its predecessor, Listening Post (now touring international art and science museums), is an exercise in harnessing and repackaging data as art. And while the installation is digital (560 fluorescent displays backed by individual tiny speakers), the effect, when multiplied across a large space, is intensely physical. Talking to Mark, I was amazed by he and his partner Ben Rubin's dogmatic insistence on capturing the energy and life inside the millions of words cranked out by reporters in the building, echoing the energy and life of the outside world about which they write.

Sure, a lot of artists can express those kinds of intentions. But in Hansen and Rubin's case, it actually gets across. Moveable Type is one of the most accessible pieces of art I've ever experienced, and I think its honesty and power come from the fact that it is a distillation, not an interpretation, of the New York Times. It doesn't launch from a news story and then go gestural. Every element, from the obituaries that blow across the screens like wind through grass to the wedding announcements, which tick by interchangeable as train schedules, tries to get at the core meaning of the data involved. And that leads ultimately to a presentation of content which is both evocative and deeply connected to the core information.

And herein lies the power of data visualization: no matter how artistic it gets, it remains truthful to the core content. It has to, because that content is the basis for the work itself. Whether you are modeling the brain, tracking the incidence of emotional statements on the Web, or conveying a chair as a sound wave, the resultant art is a deep reflection, not just an interpretation, of the data involved.

And thus data visualization tackles one of the core problems with interpretative design. Traditionally, there's a battle between veracity and interpretation--the more you interpret, the more the purists cry foul. There's an ongoing debate in the museum field about whether interpretation enhances or distorts visitors' understanding of content, and what kind of interpretation distorts in what ways.

We have well-developed design skills for interpreting and presenting stories and objects. But when it comes to presenting data, most museum folks believe that over-interpretation is necessary. It would be deadly dull, they reason, to show the meat of what scientists produce--endless tables of numbers--so we have to find another way to interpret and translate their work. We throw a rug over it and call it a story. But data visualizers, instead of looking for another way beyond or outside the data, pore into the numbers and try to create an interpretation centered, and endlessly circling back on, the data itself.

This is not to say there aren't bad incidences of data visualization, pieces that distort or confound data in ways that may be particularly harmful (since they retain the semblance of being based on hard numbers). And there are plenty of gestural data pieces that go a little too far off the interpretative end to be meaningful (origami representation of web use, anyone?). But when it works, the result is deeply intoxicating, rich with content, and the meaning seems to emerge artistically from the data itself. You feel that you are closer to the true experience of conducting science, the tedious rigor of collection matched with the rush of putting it all together. Data visualization helps us be intelligent interpreters on our own, instead of asking someone else to design an interpreted experience for us.

And that makes you feel like a tiny god, to stand in a lobby and feel that you have the pulse of a newspaper, a corporation, a world, in your grasp.