Not long ago, BusinessWeek published a story titled “Data Visualization: Stories for the Information Age” by Maria Popova (self-described as a “digital anthropologist, cultural curator and semi-secret geek aggregating the world’s eclectic interestingness”). The article featured the work of Aaron Koblin of Google’s Creative Labs (self-described as an “artist/designer/programmer”). Popova and Koblin bring fresh perspectives to data visualization, but they are newcomers to the field and have both made statements about it that demonstrate their lack of experience.
Popova describes the field as follows:
Data visualization has nothing to do with pie charts and bar graphs. And it’s only marginally related to “infographics,” information design that tends to be about objectivity and clarification. Such representations simply offer another iteration of the data—restating it visually and making it easier to digest. Data visualization, on the other hand, is an interpretation, a different way to look at and think about data that often exposes complex patterns or correlations.
“Has nothing to do with pie charts and bar graphs”? I would gladly support any effort to dismiss pie charts (with a few exceptions), but the notion that bar graphs and other traditional displays of quantitative data have nothing to do with data visualization is just plain silly. No one who understands data visualization and has done any work in the field would make such a statement, nor would they go on to say that unlike quantitative graphs, “data visualization…is an interpretation, a different way to look at and think about data that often exposes complex pattern or correlations.” In truth, all visual representations of data are interpretations. The very act of selecting information and presenting it in a particular way is an act of interpretation. All forms of data visualization—whether traditional bar graphs or some of the newer animated displays—should be embraced if they bring data alive in clear, simple, and accurate ways to help us understand the stories that live therein. Let’s not be tempted to dismiss the tried and true in our excitement over the novel—or what gives the impression of being novel.
Moving on to Koblin, much of data visualization that is novel technologically works on the same principles as bar graphs, scatterplots, and line graphs. For example, Koblin created an animated display of SMS messages throughout a single day in Amsterdam. The degree to which it provides insight into this information relies on the same basic mechanism that causes bar graphs to work: using objects of varying heights to display differences in quantitative values.
As an artist and technologist, Koblin has done some interesting work. I especially like his animated visualization of worldwide air traffic throughout the course of a day. It tells the high-level story of daily air traffic and works as an effective starting point for further exploration and analysis, which would then require more conventional visualizations. At Google, Koblin has an enviable opportunity to play with data. He has produced several visualizations that are fun and useful for artistic purposes, but fewer that actually present information in meaningful and useful ways. Of the three terms that he uses to describe himself—artist/designer/programmer—self-expression as an artist and programmer appears to come through more frequently and strongly in Koblin’s work than the solutions of a designer. I certainly can’t fault him for the wonderful examples of self-expression that he’s created, but I feel that I must take issue with something he’s said about data visualization: “It’s not about clarifying data…it’s about contextualizing it.” Actually, it’s about both. Without clarity, which is sometimes lacking in Koblin’s visualizations, context can only take us so far.
In spite of its problems, I love the way that Popova concludes her article, with one minor exception:
Ultimately, data visualization is more than complex software or the prettying up of spreadsheets. It’s not innovation for the sake of innovation. It’s about the most ancient of social rituals: storytelling. It’s about telling the story locked in the data differently, more engagingly, in a way that draws us in, makes our eyes open a little wider and our jaw drop ever so slightly. And as we process it, it can sometimes change our perspective altogether.
I balk only at her statement that “it’s about telling the story locked in the data differently.” Differently than other forms of visual display that have worked in the past—like bar graphs, perhaps? To stress that the story must be told differently is to shift our assessment of what works and what’s needed to “innovation for the sake of innovation” to a degree that Popova herself warns against. As artists, programmers, and people from other disciplines and perspectives venture into the realm of data visualization, they will help us expand our horizons. Some will also be too quick to dismiss what they don’t understand as they approach data visualization, in Popova’s words, as “a new frontier of self-expression.” Those of us who have worked in the field for awhile must be open to new ideas; those who are newcomers must respect the work of those who have worked hard to make data visualization useful and effective. I welcome this collaboration.
People sometimes adopt existing terms and give them new meanings to suit their interests, leading to confusion—sometimes usefully, when things need to be shaken up, and sometimes not. Perhaps Koblin and others like him have done this with the term “data visualization” and are using it to describe something related to but in many ways different from data visualization as I know and practice it. I suspect that another term—perhaps “information art”—would better describe what Koblin and others who combine their artistic and technological interests are introducing. Manuel Lima, of the site Visual Complexity, recently made this point in a blog post titled “Information Visualization Manifesto.” Lima proceeded from there to thoughtfully and eloquently describe the characteristics that define data visualization—or more specifically “information visualization”—as distinct from other types of information display. His words are brilliant and timely. I heartily recommend that you read them.