Big Data in the House
While reading Golumbia, the main thing that caught my interest was his cynicism towards the idea of the democratizing ability of computation. On page four, he says, “…computationalism often serves the ends of entrenched power despite being framed in terms of distributed power and democratic participation.” This immediately made me think of Fandom Studies. For those not familiar with the field, it is pretty commonplace for scholars, such as Henry Jenkins, to view fandom as a way for audiences to interact with texts on their own terms and create their own transformative works. I have long had a problem with this utopian point of view, mostly because it is based on consumerism and presents a false choice. Sure, fandom is a way for audiences to voice their support for a particular television show/film/artist/etc. However, this is limited to the options that the culture industries provide us. How does this trace back to computationalism specifically? As Golumbia says on page eleven,
To submit a phenomenon to computation is to striate otherwise-smooth details, analog details, to push them upwards toward the sovereign, to make only high-level control available to the user, and then only those aspects of control that are deemed appropriate by the sovereign… Computation can then be used, at sovereign discretion, as part of instruction, as a way of conditioning subjects to respond well to the computational model.
This is essentially what certain industries have already been doing, and what they continue to do now on a larger scale thanks to Big Data. Let’s focus on one specific example that I feel exemplifies this, Netflix’s House of Cards. As explained in this New York Times article, Netflix essentially mined its massive amount of viewer watching data and created a show fine tuned to the tastes of Netflix subscribers. (Apparently, we all really like David Fincher, Kevin Spacey, and the original British House of Cards.) It dehumanized the way we watch movies and television, as Netflix is wont to do, and in doing so, created a show that was a hit with both critics and viewers.
Despite my cynicism towards the production of culture (or perhaps because of it), I have learned to accept my role as producer of data for the culture industries to use. I really am OK with this. Especially if it means Season 2 of House of Cards is finally around the corner.
1. Before it even offered streaming services, and certainly long before it was considering creating its own television shows, Netflix was already trying to figure out how to best use its data of what people were renting. The logical step was to use it to recommend something new for subscribers to watch based on their previous viewing habits. They knew this would be an important feature if done correctly, but that if it didn’t work, people would lose interest over time in the service’s offerings. Thus they created the Netflix Prize, which offered $1 million to the team that created the best recommendation algorithm. I would argue that the ultimate success of this feature helped build a trusting relationship between user and sovereign, to borrow Golumbia’s terms, which also helped in the success of Netflix’s original programming. If viewers didn’t feel as if Netflix knew what they liked, then why would they bother watching?