16 Oct Ethnography plus Big Data, NOT vs. Big Data
EPIC 2013 was held in London this past September 15 – 18, and as has been the case for the previous eight gatherings, it was a combination of seeing old friends, meeting new people, inspirational presentations and irrelevant noise. Tricia Wang’s opening keynote and the first session that immediately followed her both addressed the theme of Big Data, and it certainly set a tone for the rest of the conference. (Download draft proceedings here.) Though most of the presenters themselves in this first session actually provided thoughtful and disparate angles of entry that embraced the theme as it relates to deep qualitative approaches for understanding people, the subtext of the audience’s immediate questions afterward, and a major discourse ongoing throughout the conference, was a kind of hand-wringing anxiety about our role vs Big Data: what will happen to us?
This discourse seemed to circle around two ideas; that data is either going to take my job away, or it’s an unwieldy – and untrustworthy – complication to our investigations. Of course, at a certain level, this reaction is no different than how people in other adjacent functional areas are trying to make sense of Big Data within their current roles. But rather than characterize Big Data as a direct threat to my livelihood or that it’s an overwhelming new technical burden I must instantly master, we should reframe Big Data as a rich new resource that provides us additional context to explore and make connections within.
Of course these observations above have already been well documented and commented on by others– at least from an ethnographic practice point of view. Personally, I don’t frame my identity as an ethnographer; I see ethnography (or more appropriately, the selected goals and approaches we’ve all extracted from true ethnography) as one of many useful tools that guide us towards understanding human problems and generating solutions that will fit people’s expectations and routines. At Claro Partners we see ourselves, not as ethnographers looking for a single pure truth, but as problem-solvers mapping the chaos churning constantly around us into some kind of organized landscape where we can generate opportunities for people and for businesses.
The presentation by Abby Margolis, Researcher Director at Claro, Five Misconceptions about Personal Data: Why We Need a People-centred Approach to “Big” Data, (full paper here) addressed just this question of how to separate the fact from the fiction around Big Data as a lived experience for the people we engage with. Moving away from demonising Big Data as our competitor or a complication in our work, we can clearly see that Big Data reveals new aspects of people’s lives, previously unavailable to us, that we can now address. We need to develop framing that gets us to the right questions about the meaning of all this Big Data landscape to individuals, especially since the average consumer is probably not yet equipped to articulate the answer, accurately, if at all. We need to situate what we understand about their motivations, objectives, obstacles and frustrations within this Big Data context in order to posit reasonable commercial responses for our clients. This means we need tools and methods to find out what, when, where, why and how individuals generate, access, manage, make sense of, exchange, archive and retire data related to whatever arenas of human experience we are investigating, and we also need to map this across people’s social and commercial interactions in order to devise strategies that really enhance people’s lives.
To an extent, having a process is necessary to provide a team with a shared mission and credible theoretical foundations so they can focus on content rather than inventing the form for their work anew for each project. However, I am less concerned about questions of methodology around integrating Big Data, since approaches are blooming and fading in this space so quickly that we must all focus on the problem and the shifting contexts we are trying to make sense of rather than attaining some pure ideological approach. I am more concerned about Big Data as a new landscape that we must – and that we get to – navigate so that we may more quickly develop a deeper understanding of people’s attitudes and behaviours – to reframe the many challenges as opportunities. This attitude manifests itself in what ken anderson, Tony Salvador and Brandon Barnett of Intel refer to as Claro’s “Topics Model” as one possible new way of developing understanding of people in their paper at EPIC 2013, Models in Motion: Ethnography Moves from Complicatedness to Complex Systems. Claro is driven primarily by a particular problem space and we then assemble the specific disciplines, skill sets and experiences necessary to understand it.
My take is that qualitative researchers – or really anyone who is in the interpretation and sense-making game – should stop self-identifying narrowly as ethnographers, stop relying on explanations of the (qualitative) value of your skill set against (quantitative) Big Data as the other. Instead, we must all demonstrate value every day by making sense of Big Data within our overall approach to understanding people and then explaining why our insights matters. Big Data is a part of all of our lives now in one form or another, so as problem-solvers we need to integrate this fact into how we go about understanding human experiences in ways that help our clients navigate this disruptive landscape. In this way, together we can develop solutions relevant for them in this emerging world. I’d love to hear people’s reaction to this post.
Rich Radka is a founding partner at Claro