Getting Out
This post was originally written for an internal blog at my home department at Infrastructure Canada. It’s an example of the kind of communication the GCEs do within our own departments to share our experiences. It has been edited slightly from the original to adjust for the audience here on Medium. Il est disponible en francais aussi.
As a cohort of GCEs, we’re all being trained on a whole breadth of innovation methods. But here’s the thing: I’m not a big fan of professional training. I love to learn, but I find professional training is too often done in a nondescript classroom with a generic slide deck projected on a dim screen. Yawn. A few weeks ago, however, I had the opportunity to do the most exhilarating training I’ve ever done, and I wanted to share it with all of you.
The day-long training was a “Thick Data Taster” and it was given by InWithForward in Toronto. This organization sets up partnerships with social service organizations to design and deliver social programs in a more ‘human’ way. In practice, this means combining the expertise of sociologists, designers, anthropologists and evaluators to try to make social programs better respond to the needs of their users.
So what does this have to do with me? I’m not a social policymaker. But InWithForward was asking all of us to think about social programs because they’re a tangible way to understand the benefits of ‘thick data.’ When you think about designing a policy or program, you’re likely going to want to rely on insights from large sets of observations, something you might call ‘big data.’ But at the opposite end of big data is thick data: it’s the qualitative, deep insights that comes from understanding a single person’s experience in interacting with a system.
The morning of this “Thick Data Taster” training was learning a long version of what I just wrote in the above paragraph, along with tools and skills to collect thick data. And then as we approached lunch, one of the experts leading the training said something that caught me off guard: we’d be split into groups of two and given a place in the city to do our own thick data collection in real time during the afternoon.
This kind of exercise is what InWithForward calls an “ethnography deep dive,” getting us out of our cushy training/office environment to see and sense challenges individual people face in the very contexts in which those challenges are actually experienced. No calling a formal consultation meeting hosted in a government meeting room; no long, academic surveys; no pre-selected focus groups. Just collecting thick data to get a rich picture of a few individuals’ whole lives. I was placed with my colleague Laura from the Canadian Food Inspection Agency, and we were assigned a hospital in the west end of downtown Toronto. Our task? Listen the sounds of the hospital, watch the way people interact with the building, and talk to people. We were expected to have detailed, full conversations with at least 3 people in order to be able to build anonymous, thick data ‘profiles’ of those people by the end of our 3 hours.
I’m a classic introvert, so talking to strangers on any day is a daunting prospect. But nonetheless, Laura and I hopped on the Queen St streetcar to the hospital. And once we got there, we started to talk to people. It was hard at first, but got easier over time to approach strangers to chat. I can’t tell you about the specific people that Laura and I met for ethical reasons, but I can tell you that it was about as odd an experience as it sounds. Lots of people didn’t want to talk, and almost everyone seemed a bit confused by us. But a few people really opened up, and it was fascinating to ask them about their experiences using the hospital and then broadening the conversation to learn about their whole lives: their children, their jobs, their hobbies, their stresses. This was human experience data that no dataset on hospital wait times or client satisfaction could give us.
When the whole group of GCEs reassembled a few hours later, we compared our experiences. Some people were assigned to coffee shops, other people to social housing complexes, and still others to places like the LCBO. Some pairs had emotionally deep experiences with the people they met, and other pairs had really superficial interactions. But nearly all of us had a strong reaction to the process, whether positively or negatively; talk about getting us out of our comfort zones!
We took our new thick data and made anonymous ‘profiles’ of the people we met, and then discussed what insights we could draw from the small group of strangers we had just met. It was fascinating and completely unlike anything I’d ever done before. If this were a real experience, we’d be able to combine these insights with large data sets or academic literature to design a more resilient, responsive policy or program.
This training was an important and refreshing reminder that ‘regular’ data, and its attendant statistics and trends, isn’t the only kind of data that we should find valuable in making policy or designing programs. Combining big data with thick data can make for a richer experience, as long as you’re willing to get out of your comfort zone to collect it.
