View from my hotel room in Singapore. That was just before the heavy haze blanketed the city, forcing school closures, keeping tourists out of the swimming pools, and generating a surge in the wearing of breathing masks. Who knew, but the haze sometimes blows in from neighbouring forests in Indonesia. The cab driver who took me into the city from the airport railed against Indonesia and the multinational paper mill robbers, he called them, who were deliberately clear cutting the forests to serve their greedy capitalists ends. He wanted them charged with endangering the atmosphere and harming the lives of people living in neighboring nations. After several days here I give him my full sympathy.
I am in Singapore for an annual global leaders summit sponsored by the Washington-based Council of Graduate Studies. Thirty five of us have been invited from all over the world to discuss a topic of current interest. This year the National University of Singapore is hosting us, and doing a fine job of it, unhealthy air notwithstanding. Our topic this year is Big Data: what is it, what are we doing with it, how is it helping or changing university research and teaching practices? I thought it would be a dreadfully tedious topic but the opposite has proven true. We have been talking pretty well non stop for days about Big Data, defining it, qualifying it, critiquing it, advocating for it, and recommending that our universities train our students in data literacy much more than we already are. I know, like any 21st century consumer, that every time I purchase a book from Amazon I become an object of a dense network of computational determinants, a target for a vast marketing enterprise aimed at urging me to like and purchase another book just like the one I already paid for. I know that if I order a product on one site very similar products will be flashed on screen to tempt me to buy them on an entirely different site. I know that every time I browse online I am submitting my tastes and tendencies to an automated patterning function, one that knows me like a book—a predictable read, so to speak. But here in Singapore I have learned a lot more than that.
Some of my colleagues are urging universities to ensure all our students are trained, across all disciplines, in data literacy. That would include learning computer code, another language of a kind. One colleague here argues that by 2018 there will be a huge Big Data knowledge deficit. Few of us have the capacity to do that kind of basic training right now, but I am hearing a vision of the future, and expertise in analytics is definitely part of it. Harnessing Big Data requires big computers and, in many cases, big research teams. Size, in this case, obviously matters. Collaboration is at the heart of much of this practice, arguably a beneficial effect of this revolutionary turn in research practice. But very few participants here are fully loaded to deliver Big Data programs, although a number have boasted about recent developments of professional masters programs with a focus on the subject.
Humanities scholar that I am, I tend to raise the challenge of where Big Data sits in our arts faculties, especially in our humanities departments. The turn to Big Data approaches to problem- solving shifts us towards machines and the behavioral patterns they describe (and predict), and away from the meaning-making activity humans perform. My colleagues here in Singapore agree with me but they keep insisting on the necessity of embedding Big Data skills in our curricula. Another colleague says that it’s all a mess and we don’t know what we are doing. Yet another laments we are shifting from a curiosity-driven to a data-driven approach to research, that is, going from raising a question and seeking the data to help answer it to aggregating data and then finding the question. Ultimately, we all still want our students to reflect on what they are doing, not merely to absorb the mountains of data they can harvest without thinking.
But it’s not an either/or option. We all agree that because Big Data practices are here, shaping our world in ways we often cannot see, it is our responsibility to understand those practices, not merely surrender to systems being done to us. It’s the human intervention in research, after all, that will or should make the best use of Big Data—for good, not for evil (or merely my shopping urges).