I’m currently enrolled in two excellent online courses: Data Journalism by the European Journalism Centre (#ddjmooc) and the TechChange course on Mapping for International Development (#TC141). I’m not a journalist or a crisis mapper but both of these themes, I suspect, will be relevant to global/media development and the UN post-2015 goals (something that I am passionately interested in).
The more I learn of the amazing tools, the fascinating projects, and the impressive visualizations, the more cautious I become about the possible lack of multidimensional perspectives.
I am wondering whether we are sometimes falsely seduced by massive numbers. Given the debates about diminishing resources in journalism, can the big data be an easy — too easy — solution to research? What kind of stories are not being told? Or, given that new data is often created by the means of people’s digital activities, whom does it leave in shadows? And, crucial in all cases but very, very important in development work: How do we detect the (Western?) biases of big data?
I’m, of course, not the only one wondering. As Michael Brodie of MIT comments in a recent issue of The Economist (May 24th, pg., 16), ‘much harm is being done by people asserting causality as a consequence of data analysis. […] Big Data’s pursuit of “what” should be symbiotically linked to empiricism’s pursuit of “why”…’.
The Wired says we should focus on Long Data. I suspect the rise of qualitative approaches, as this wise blog post by an applied anthropologist also claims. Am I right or wrong? Ahead or behind the curve? What are your experiences of using / being informed by Big Data?