Analytics and big data are becoming business imperatives. One big element that separates them from unstructured data pools or BI drill-down reports is that analytics can be used to “tell a story,” typically with visualizations.
The visualization angle of this storytelling is typically reinforced with the same types of charts you’ve seen before (you know, the ones that barely register a response during business meetings) or is danced around in attempts to dazzle you with data, data, data.
When I think of art that expresses a narrative, my mind goes to the Albright-Knox Art Gallery in Buffalo, N.Y., specifically the massive work by Anselm Kiefer that hangs in one stairwell. Kiefer’s “The Milky Way” (seen at left) is a few dozen feet of bleak canvass I’ve always interpreted as a destroyed farm, mostly in greys, rusty reds and barren whites. Your mind can go and go with that type of piece, often with all of the joy and smiles the term “destroyed farm” brings along. Another favorite story-teller piece is “Refrigerator Pies” by Wayne Thiebaud, hanging closer to my home at the Milwaukee Art Museum. The title kind of gives it away – it’s a display of pies, as you’d see in a diner, all thick with oil paint portraying cream, crust and cherries – and its directness of its subject quickly fills in the rest. “It’s just pies,” my internal critic says. (Another critic reminds me how much I like to eat pie.) But there’s a prettiness and charm in the presentation of the pies that speak to the power of a straightforward image.
Not to delve too deeply into my C+ college art history course background, but the fluctuation and purpose of these pieces and others came to mind a couple of times this week in discussions on the direction of visualization in the realm of data. After this week, part of me is left thinking there’s a movement of artists-cum-data practitioners that should have those who want to tell great stories with their business data ready to have a few more people wearing berets around the office.
We had lively input Tuesday from the vocal crowd in the Twitter “#IMChat” on data visualization led by my colleagues Julie Langenkamp-Muenkel and Whitney Eden. (To summarize one important lesson from the chat: simplicity = good, donut charts = very, very bad) That same day, I saw in-person displays of the literal practices of data visualization. At Predictive Analytics World in Chicago, Robert Lancaster’s presentation on work in raking and assessing hotel information for travel site Orbitz featured dispersals of dots that gave gravity to the amount of data they were shifting into Hadoop and MapReduce. IBM, in another nail-polishing show of their big data potential, brought an air of humility to its analytic accuracy hits-and-misses in devising “Jeopardy!” champ computer Watson.
On the more abstract and brilliant front, I’ve also been digging into the slick arrangements of data visualization in Nathan Yau’s book, “Data Points: Visualization That Means Something.” (This new work by the thinker behind flowingdata.com is also being enjoyed by my cat, Friendly, if our recent Facebook “Meme Monday” post is to be taken literally.) In particular, Yau’s section slugged “Data Art” takes the Excel graphs we know and the Gantt charts we (have to) love and throws them in a blender ready for any Guggenheim collection. In a few shining instances, there’s Jason Salavon’s work assembling a compilation of the greatest music videos of all time, each categorized and compressed into a rectangle striped by the average color each video displays on screen. That description might not do it justice (check out his work in the link), but essentially the collection brings together a theme, flow and order of sorts. In another of Yau’s entries, “Cinemetrics” is a work by German film artist Frederic Brodbeck that consists of a measuring rod with Wi-Fi sensors and tiny lights to display network activity like tree rings.
Without giving too much away, Yau accompanies the colorful and telling data visualizations with actual, or at least potential, business uses. Yau summarizes the modern relationship of the two like this: “The definition of visualization changes by who you ask, and as a whole, the breadth of visualization changes every day. As you come across rules and design suggestions for how to present data, be sure to know their context … With visualization, draw from previous work and keep guidelines in mind, but don’t let it keep you from what works best to achieve your goal and to communicate to your audience.”
The communication that Yau writes about isn’t easy – ask any project leader who’s in charge of expressing the importance of data governance, for example – but it’s an inevitable part of the equation. That comes with time, as does the broader understanding of where someone with an MFA might meet in the middle on day-to-day work roles with your typical data manager or visualization tool user. But I’m sure it will, to varying degrees. For artists, they’re already expressing interest in data as a medium, and the information management field might be one of the few where they could find such a quick entry into decent paying and intellectually satisfying work. And, whether we’re clear about it or not, CIOs, data managers and business analysts are reaching out for information “storytellers” through visualizations. It wouldn’t be too broad a stroke to paint a scene where “corporate art” is more about exciting, innovative and engaging data visualizations and less about that wrought iron abstract piece forgotten about in the middle of a bank headquarters courtyard. I’m geeked up to see how the art and data worlds will combine to make the destroyed farms and refrigerator pies that usher in a new wave of business understanding with a touch of heart.
Oh, and as I’ve passed along a few interesting visualization artists here, I’d enjoy checking out suggestions on data artists — or even emerging vendors or tools – from our readers.