This study offers a cursory account of the so-called Big Data revolution in social science, in particular in sociology. Borrowing the definition of Big Data from Kitchin [2013], we set out to examine the challenges sociology is facing with the advent of rich data sets comprising the universe of observations. First, we posit that sociology can and should benefit from this opportunity and not relegate the analysis of big data sets to computer science. Second, we ask how sociology should transform to embrace big data? We argue that, apart from the obvious methodological challenges, scholars of big data should understand that most frequently this information is a by-product of bureaucratic organizations that create it as a means of internal control. Therefore, this data may suffer from problems at birth. Finally, we look at the adjacent field of economics that faced similar challenges in the 1990s with the rise and demise of growth regressions in empirical economics. We show how the field had to restructure itself, adopting empirical rigour to tackle the crisis it underwent with the introduction of rich country-level data. We conclude by suggesting that big data may be used to extend existing empirical studies with richer background information.