Data visualization is becoming a catch-all concept with little analytic usefulness. The infographic plague we have to endure is not helping.
It doesn’t have to be that way. Stephen Few wrote recently about the distinction between data visualization (“the goal that data be visualized in a way that leads to understanding”) and data art (“visualizations of data that seek primarily to entertain or produce an aesthetic experience”). Robert Kosara gives data art a different meaning.
While I strongly agree with this, I am unable to see where to draw the line: there is no data visualization without graphic design and no data art without data. For the sake of the argument, let’s think of data visualization as 10% of graphic design and 90% of data and data art as 90% of graphic design and 10% of data. It’s a continuum, where the nature and the purpose change when the percentages change.
It’s fun to play with these meaningless percentages, so let’s carry on. Suppose that a typical Excel user with no graphic design skills or training becomes aware of what data visualization is all about. She understands that some basic rules can improve effectiveness and make her charts aesthetically pleasing. She’s still using traditional charts, but she’s moving the split to the 75% data / 25% design mark. At the 80%/20% mark we can start talking about “functional aesthetics”. We haven’t left data visualization yet.
Around 60% data / 40% design, things start to change (I would place a typical New York Times visualization around here). Design can no longer improve data visualization and it slowly becomes data illustration (at 49% data / 51% design). The designer is making a unique piece and he uses a lot more textures and non-traditional charts, but he still believes (or wants us to believe) that the main goal is to inform and entertain. I would place David McCandless around the 30% data / 70% design mark.
Finally, at the 25%/75% mark data illustration becomes data art and the graphic designer becomes an artist. He’s now free of all constraints. The data can be the starting point, but he uses it to create a unique work of art.
Again, don’t take these percentages too seriously. I do believe that aesthetics play a major role in data visualization and it’s always there, no matter what. But if, at some point, we find ourselves trying to make out charts more beautiful and memorable than more effective, we are changing their nature and their usefulness in a corporate environment. As Stephen Few puts it, it’s “frivolous, costly, and harmful”.
Leave data art to art galleries and please, please, don’t try to be an artist with Excel and canned effects.
So, where are you in the data visualization – data art continuum?