Big data and knowledge management: a case of déjà vu or back to the future?

Abstract

Purpose Big data clearly represent an important advance in information systems theory, but to describe it as “revolutionary” is premature. Similar technological breakthroughs, from online databases to ERP, were clearly modulated by advances in the organizational domain, including matters of structure, strategy and culture and arguably big data will be similar. The purpose of this paper is to encourage discussion of the wider implications of big data for the theory and practice of knowledge management. Design/methodology/approach This is a conceptual study based on critical analysis of the relevant literatures including those of organizational studies and management, big data and knowledge management. Findings The literature of big data emphasizes the application of algorithms to pattern analysis and prediction, resulting in data-driven decision-making, with data being the creator of value in organizations and societies. This would appear to render obsolete previous depictions of the “data-information-knowledge” relationship and, in effect, spell the end of knowledge management. However, big data literature largely ignores the organizational dimension and, significantly, the importance of frameworks, strategies and cultures for big data. As all of these are present in the literature of knowledge management, it would seem that big data have a long way to go to catch up and qualify even as a sub-discipline. Indeed, on the evidence, big data may well have a future as a contributor to and/or an element of knowledge management. Even for this to happen, however, major advances are required across the spectrum of big data technologies. Research limitations/implications This is a position paper written as the precursor for an empirical study. Originality/value The paper offers a critical literature-based and knowledge management perspective on big data while pointing out the common thread that runs through decades of advances in information systems technologies.