Big Data and Knowledge Management: Establishing a Conceptual Foundation

Abstract

Abstract: The fields of knowledge management and intellectual capital have always distinguished between data, information, and knowledge. One of the basic concepts of the field is that knowledge goes beyond a mere collection of data or information, including know‑how based on some degree of reflection. Another core idea is that intellectual capital, as a field, deals with valuable organizational assets which, while not formal enough to rate a designation as intellectual property, still deserve the attention of managers. Intellectual capital is valuable enough to be identified, managed, and protected, perhaps granting competitive advantage in the marketplace. So what do we make of current trends related to big data, business intelligence, business analytics, cloud computing, and related topics? Organizations are finding value in basic data and information as well. How does this trend square with the way we conceptualize intellectual capital and value it? This paper will work through the accepted literature concerning knowledge management (KM) and intellectual capital (IC) to develop a view of big data that fits with existing theory. As noted, knowledge management and intellectual capital have both recognized data and information though generally as non‑value precursors of valuable knowledge assets. In establishing the conceptual foundation of big data as an additional valuable knowledge asset (or at least a valuable asset closely related to knowledge), we can begin to make a case for applying intellectual capital metrics and knowledge management tools to data assets. We can, so to speak, bring big data and business analytics into the KM/IC fold. In developing this theoretical foundation, familiar concepts such as tacit and explicit knowledge, learning, and others can be deployed to increase our understanding. As a result, we believe we can help the field better understand the idea of big data and how it relates to knowledge assets as well as provide a justification for bringing proven knowledge management strategies and tools to bear on bi