Big Data and Economic Science: Redefining Evidence, Models, and Policy Design

This paper argues that the rise of big data is not merely an empirical extension for economic research, but a catalyst for fundamental methodological and conceptual rethinking across the discipline. By enabling granular, real-time observation of economic behaviour, big data integrates computational techniques, behavioural insights, and network analysis into mainstream economics. The paper explores how these developments intersect with and challenge classical, Keynesian, institutionalist, and Marxian frameworks, highlighting the shifting dynamics of market power, inequality, and agency in the data-driven economy. Methodological challenges—data veracity, access asymmetries, computational constraints, and ethical risks—are analysed in depth, alongside opportunities for more context-sensitive and policy-relevant analysis. The paper concludes by outlining future research directions that emphasise causal inference in high-dimensional settings, interdisciplinary collaboration, and the design of ethical, inclusive data infrastructures. These developments, it argues, signal not only a technical evolution but a foundational transformation in how economic knowledge is produced and applied.