Big Data and Audience Research

I agree with Chris Anderson's oversimplified argument that big data has ushered the end of the scientific method. Though I imagine in the 7 years following his blog post his position has possibly evolved, his article assumes that big data is simply available at our disposal. However, "big data," especially related to to audiences is not easily at the hands of researchers. It sits behind the gated server walls of Facebook, it needs to be munged and reorganized, and there is a relatively high level of human capital that goes in to analyzing this big data. The other thing to note about audience data is that natural language processing of peoples' conversations, social media postings, etc., is still relatively crude. Machine language and NLP have come a long way but in order to get high level results isn't a cake walk.

I think having access to large datasets is excellent, but as Jen Schradie of UC Berkeley wrote in this blog post , "I am not suggesting fishing expeditions in lieu of hypothesis testing nor any Anderson-esque junking of the scientific method. The numbers do not speak for themselves. Instead, it is our job as social scientists to understand the difference between the data, whatever its size, and the method, whatever that may be." I agree with Schradie. We can't simply starting poking at the data. Understanding a group of people, a place, a set of ideas and having some type of expertise is essential otherwise we lack the cultural capital to really engage the "big data" we've collected.