Big Data and Doing Research in the Management Discipline

We argue that big data should be understood as an indispensable element in a wider context of big data science that also includes machine learning and results interpretations. By addressing this wider context, we examine the differences between big data science and modern sciences in general and management discipline in particular. While the former adopts data-driven approach to enhance predictive accuracy, the latter adopts theory-driven approach to produce causal explanation. Data-driven approach in conjunction with machine learning strives to enhance the predictive accuracy by allowing big data to choose a set of parameters on its own under rather loose assumptions and learning processes. In contrast, management discipline emphasizes the role of theories in deriving testable hypotheses and encourages scholars to present compelling arguments without explicitly referring to data to be used for estimation at a later stage. This implies that management discipline may not benefit much from big data science in doing academic research. But we believe that big data may prove helpful for the management discipline if we carefully identify small but meaningful patterns that are not easily detected in small data. We also argue that sampling is still an important issue in using big data for academic research.