Not ready to throw in the towel yet: Thoughts on Big Data, digital social research, and academia


(Not the future, merely the Gladstone Link)

Full disclosure: This was written for a ‘position paper’ assignment in my Digital Social Research course at the OII.

The article by Savage and Burrows (2007) plays on an insecurity that haunts me every time I pay my tuition: what ensures that social researchers have valuable, employable skills? I agree with them that survey research is becoming overdone, tired, and obsolete in areas where we can use digital methods to obtain and analyse entire populations. I have also heard former journalists in my class echo their sentiment about qualitative research, wondering what exactly separates it from in-depth journalism. They argue that the solution to this is a “politics of method” (p. 895) that includes methodological innovation but, in true sociologist form, they cut the article short before specifying exactly what this might entail. As intended by their prompt, I began to imagine it but was confronted by a dilemma that seems to plague many employment sectors in the post-industrial era: whether to specialize or to become a generalist.

When I worked for the public sector, there was a constant debate over what type of employee training was most useful. Would it help the client better to interact with an employee who had general knowledge of all programs and services or should clients immediately be filtered to specialists in certain programs depending on their needs? The debate was never settled because sometimes people have a variety of needs and sometimes they just want to get one thing done. Such is the case for research: sometimes there are the descriptive projects as mentioned by Savage and Burrows, which can be tackled through through the application of a very specific method for the purpose of answering a tightly bounded question. For example, if I want to know who are the most influential people in my particular Facebook network, I can throw social network analysis at this question to identify the friends with the highest eigenvector centrality (or however I choose to measure influence) and come up with an answer pretty quickly. However, if we want to know the what, how, why and greater implications of social movements involving the use of Web 2.0 (e.g. the Iranian protests), then clearly an analysis of Facebook or Twitter networks is only one type of building block required to reach an overall understanding of such a complex social phenomenon.

This brings me to boyd and Crawford’s (2011) article, which I feel instils some hope for the value of academic training. Their cautions about jumping on the Big Data bandwagon are rooted in judgement and discretion that I feel is generated from the knowledge and skills included in a social research degree. Consideration of epistemology, reflexivity about research decisions, viewing data within a larger social context, understanding the limitations of conclusions, and following ethical principles are all recurring themes of my methods courses. Tying into ideas mentioned in a talk the other week by Diego Beas, this gives me optimism that there is an enduring role for social scientists even as data are more frequently digital, bigger, and not generated through traditional methods. With industry, popular media, and governments claiming a stake in digital research, academically informed research will remain distinct because it is founded upon the principles of robust, knowledge-generating research practices.

For these reasons, I am not ready to buy into Savage and Burrows’ crisis and hide in the bunker of market research. As an interdisciplinary scholar, I am aiming to generalize enough so that I can be adaptable as methodological tools and data types continue to evolve but I also believe in specializing to the point of knowing what I am doing when producing and analysing social research (hence my choice to take courses in Digital Social Research and Online Social Networks). I believe that this agility, in combination with the principles of the academic tradition, will allow me to produce meaningful research into the future, no matter the tools or types of data it may hold.


boyd, d., & Crawford, K. (2011). Six provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, 1–17. Retrieved from

Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. doi:10.1177/0038038507080443

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