Jake Porway : Hyperakt Lunch Talk
Close your eyes and picture “data.” What do you see?
Jake Porway begins his lunch talk at Hyperakt, hosted by the studio on the first spring-like Friday in April, by posing this question to the audience. The responses paint a predictably dry portrait: spreadsheets, rooms of servers, and the anonymous, matrix-like flickerings of 0’s and 1’s. These commonly held assumptions, he laments, can obscure all the very intimate ways we produce and are produced by our data.
Realising the personal and meaningful potential of data is something he’s built quite the career out of. By now, hackathons are a familiar and celebrated staple of the urban landscape — a flexing of creative and technical muscle under hectic conditions. But at a session Porway once attended, he felt that the output was out of sync with big picture issues. The brightest minds were churning out apps to find parking and restaurant ratings — tools to make the lives of the comfortable just ever so slightly more convenient.
To help foster more meaningful collaborations, he co-founded DataKind, which funnels that surplus energy and talent towards nonprofits, NGO’s, and government agencies that need it the most. Their DataDives adopt the hackathon model, where data scientists spend a marathon weekend with groups that have well-defined problems to tackle. DataCorps is a longer venture, pairing volunteers with the organizations for 1-6 months for a more in-depth exploration.
Transforming intimidating fields of numbers into an appropriate visual expression is work that can easily be taken for granted. But the results vouch for themselves, providing the organizations the “ah-ha” moment of new insight into their own content. For most of DataKind’s partnerships, the effort goes into cleaning and visualizing the data to build exploratory tools, though this also sets the groundwork to make insight available to users at many literacy levels. Although a line is usually drawn between visualization for internal, academic purposes and those designed for public communication, the conversation between Porway and audience members turns to how even experts benefit from the use of intuitive, visual metaphors. As the saying goes, “if you can’t explain it simply, you don’t understand it well enough.”
With the growing popularity of data visualization have come a fair share of guidelines, tutorials, and manifestos. One of the often-repeated commandments tells us that "form follows data,” which seems to overshadow the nature of data as a cultural artifact. Porway shows a few examples of how deeper interrogation unravels these assumptions. Among them was an argument between a New York Times report unfavorably reviewing the new Tesla Model S, and the motor company disputing the review based on information collected by the car itself. Of course, this is only one of many examples of how the forensic process of finding the narrative in a spreadsheet can yield many, differing readings.
Porway finds his work indeed has more of the organic quirks of scientific processes, as initial expectations about a dataset fall apart when deeper investigation reveals missing information, noise, and biases. One of the benefits of DataKind collaborations to the data scientists is the exposure to policy experts, who have the qualitative expertise to help navigate the quantitative. It’s perhaps what influences his next open question to the audience: What responsibilities do we have as data scientists and visualizers? And to whom? No simple answers can be offered, though the audience — a mix from academia, statistics, and design — share concerns from their various perspectives.
The last few slides give a glimpse at the recently released NYPD stop and frisk data. Instead of launching into the “raw” information first, Porway rounds out the talk with another question: What questions can we ask before we even look at the data? This time the room fires back with suggestions: whether or not there are quotas, how strictly stop and frisks are recorded, and how racial categories might be defined, to name a few. Even this short exercise seems to bring a more invigorated, critical perspective to discovering the problem at large, or, at the very least, bringing a heightened awareness to the problem at hand.
Catch Jake Porway on the latest Data Stories Podcast and follow @jakeporway and @DataKind
Hyperakt is a design and data visualization studio in Brooklyn, NY. Follow them @Hyperakt for information about their regular lunch talks. All event photos courtesy of Hyperakt.
