Data for Days: An Artist Learns to Love Numbers

NOTE: this post originally appeared on the Convergence Academies blog on July 31, 2015.

I have a confession to make: lately I’ve been getting unusually excited about data.

I’ve started calculating percentages, designing color-coded charts, and watching webinars on data visualization…for fun. I’ve been attending workshops with organizations like Ingenuity to try to understand how data functions in the world of the arts and learning.

A chart I created to show how our DMMs (Digital Media Mentors) are using their time at our two school sites.

A chart I created to show how our DMMs (Digital Media Mentors) are using their time at our two school sites.

I guess I’ve always been the type of person who likes maps and diagrams. I find comfort in seeing information laid out visually. I like the way that a good chart can help you know where to look, and what to examine more closely. Playing around with data mapping this year has helped me appreciate the huge impact this kind of visual framing can have on the way we understand complex information. As Kim wrote in her post last December, we at Convergence have been using a technique this year called “Data Therapy” (based on an approach from MIT’s Civic Media Center) to look at, analyze, and understand the impact of our work in the schools.

The idea behind data therapy is to make information relevant & accessible: to find and extract stories from the data that help our team draw their own conclusions, and in doing so make strategic, data-informed decisions.

Although this practice makes a great deal of sense to me, it’s not something I’ve ever encountered in other arts organizations. When I talk with other artists about data I am met with groans and impatient eye-rolling, or at best an apathetic shrug. In these situations data is either seen as boring and irrelevant, or simply illegible: something to be decoded only by those who speak the secret language of numbers (read: not “artists”).

I found a document that shed some light on why that might be.

A recent study by the Cultural Data Project surveyed approximately 185 cultural organizations in major cities across the U.S. about the challenges they face collecting and utilizing data within their organizations. Interestingly —and not surprisingly—their major finding wasn’t that organizations lacked access to data, it was that they lacked capacity for understanding the data; and more, they even cited the very culture of the arts as diametrically opposed to the use of data to inform decision-making:

The challenge that resonated most strongly with participants was the underdeveloped capacity for data collection and interpretation within their organizations. Many also cited ways that organizational culture and field-wide values in the arts can undermine the effective use of data, as well as the lack of a clear organizational vision for how to use data in planning and decision-making [emphasis added]

The fact that arts organizations often lack the capacity for working with data should not be surprising. Resources are always limited, and I’ll be the first to admit that data analysis is not always the most enticing topic to a room full of creative folks. It's the organizational equivalent of broccoli: sure, you know it’s good for you and rich in nutrients and all that — but it’s not necessarily going to be the first bite you take of the meal. I get it.


What is particularly interesting to me about these findings, though, is this idea that “field-wide values in the arts” actually inhibit the prioritization of data as a decision-making tool. In other words, the very nature of the arts is culturally opposed to the concept of using data. Why is that?

In part, it has to do with the way that many artists are taught to create: we learn to throw ourselves into unknown territory, take big risks, and pursue unusual or divergent ideas despite logical objections. All this is well and good. The problem emerges when we forget that creativity is not forged solely on this process of intuitive exploration; creation also involves hard work, discipline, and yes — planning (renowned choreographer Twyla Tharp’s book The Creative Habit is good evidence of this duality). This is where data can be useful: it can help us stay on course, remind us where and how to focus our creative energies.

According to the Cultural Data Project’s report, among their surveyed organizations they found a "deeply embedded tendency for their artistic colleagues to assume that decision-making can either be informed by data or can be informed by an artistic or curatorial vision—but not both.” There is real fear here that the very creative process they are trying to support will be undermined by the perceived objectivity of concrete data.

The other difficulty, they say, comes from what data measures: "Practitioners on the ‘business' side (marketing, development, etc.) said they’ve gotten the message from some of their colleagues on the artistic side, that 'what I do can’t be counted,' which they said leads to an over-reliance on anecdote and opinion rather than objective data, whether qualitative or quantitative."

Again, there is truth in this statement. So much of what we do can't be easily counted. There are also limitations to the depth of data collection, and ethnical implications that keep us from reporting on some of the qualities that matter most. And yes — data-based decision-making taken to the extreme can limit our ability to freely explore new  & unconventional paths in our work. But it doesn’t have to. Part of knowing how to use data is also knowing how not to use it — when to put the charts down and listen to what people actually have to say, in real time and space.

Artists are also expert at post-its.

Artists are also expert at post-its.

So what does all this mean for those of us working in the arts? Well, for starters it means a shift in culture. And thankfully, that is something that artists are expert at.

As artists who understand and affirm the power of data in strategic design, it is important for us to advocate for its seat at the table, while remaining sympathetic to the power of the arts to address things in ways that are unmeasurable. This is the point behind data therapy: in collectively mining data for story and personal relevance, we are shifting power back to the interpreter, and creating a space where artistry and objectivity can go hand-in-hand. After all, raw data on its own is not particularly useful; it’s the way we organize and interpret the data that brings relevance & meaning.

As Robert Morrison put it in his keynote address at yesterday’s Ingenuity Data Institute, “in order to get where we want to go, we have to know where we are.” That is what data affords us.

There are a good deal of artists out there who understand this & are starting to appreciate the appeal of data: the way that a bit of known information can fill in some gaps while simultaneously opening up new avenues of curiosity and inquiry. In fact, data visualization is incredibly popular right now (see Information is Beautiful), and many artists and designers are working with data to create stunning infographics and interactive displays (see Ann K. Emery’s work for excellent examples & tutorials on this topic). That is a huge step forward for the use of data in an artistic context.

But as most artists know, aesthetics are only one part of the puzzle. Although data is more beautiful than ever, there are still many roadblocks to its application in practical contexts. Questions remain around legibility, capacity, and the deeply-held values of organizations that run contrary to its use.

So, how do we learn to admire the certainty of numbers without relinquishing our love affair with the ethereal? There’s no right way, of course, but I think a good place to start is with awareness. What assumptions do you make about data? How does your organization tend to make big decisions? Considering questions like these will start you on a path to a more thoughtful relationship with data, and perhaps even lead to new insights around your own creative practice.

Data can be lovable, I promise.