Big Data

Big data refers to extremely large, fast-growing, and varied digital datasets, generated through online activity, mobile devices, sensors, transactions, and social media, which are analyzed computationally to detect patterns, trends, and associations. It is often described by the features of volume, velocity, and variety. For sociologists, big data offers new opportunities to study behavior at scale, including networks, mobility, communication, and public opinion, but it also raises serious conceptual and ethical questions. danah boyd and Kate Crawford questioned the assumptions that larger datasets are inherently more objective, pointing to problems of bias, representativeness, and privacy. Rob Kitchin examined how data-driven science changes knowledge production, while Shoshana Zuboff theorized surveillance capitalism, in which companies extract behavioral data for profit and prediction. Virginia Eubanks and Cathy O’Neil have shown how algorithmic systems built on large datasets can reinforce inequality in welfare, policing, and employment. Michel Foucault’s analysis of surveillance and governance is often used to interpret these developments, and Roger Burrows and Mike Savage have discussed the challenge big data poses to the authority of academic sociology. Big data therefore remains central to debates about power, privacy, inequality, and methods.