Beyond Big Data: Methodological Reflections on Data, Context, and Social Knowledge

This session critically examines the expanding use of big data and data engineering in the study of social phenomena under conditions of rapid transformation and global turbulence. Contemporary challenges—including persistent inequalities, poverty, environmental crises, armed conflicts, and the spread of misinformation—have prompted a proliferation of data-driven projects, policy interventions, and technological innovations. With the increasing availability of large-scale datasets and advanced computational tools, data engineering is often positioned as a powerful means to assess social conditions, identify patterns, model causal mechanisms, and evaluate policy impacts.

This session interrogates the epistemological assumptions and methodological logics underpinning this “data-centric turn” in sociology. While recognizing the analytical strengths of big data techniques, it challenges the tendency to treat them as universally applicable or inherently superior modes of inquiry. Instead, it highlights the risks of abstraction, decontextualization, and the marginalization of lived experience in data-driven analyses.

We invite contributions that critically reflect on the selection, construction, and interpretation of data in relation to specific social contexts. In particular, we foreground the continued importance of small data, qualitative evidence, and subjective experiences in producing sociological knowledge. By encouraging dialogue between computational, quantitative, and interpretive approaches, this session advances a reflexive and pluralist methodological agenda for understanding complex social realities.