Big Data, Knowledge, and Business Intelligence

Background

The field of knowledge management (KM) and its related discipline, intellectual capital (IC) have both contributed considerably to our understanding of the value of intangible assets of the firm. The general concept that intangibles are something worthwhile goes back at least to Schumpeter’s (1934) work on innovation and has since included contributions from other high-profile writers such as Drucker, with his knowledge workers (1991). The idea that proper management of such intangibles might lead to competitive advantage was explored by scholars such as Nelson and Winter (1982) in their evolutionary theory of growth. Such competitive advantage fits well with the resource-based theory of the firm (Wernerfelt, 1984), specifically identifying knowledge as a potential key resource. As a result, we have the knowledge-based theory of the firm (Teece, 1998; Grant, 1996) and its suggestion that knowledge is not only a potentially important differentiator but perhaps the only differentiator for firms looking for sustainable competitive advantage.

In pushing the field forward, the KM and IC disciplines have always carefully defined the nature of their study, perhaps because of the obvious need to clarify and distinguish knowledge assets or intellectual capital from the more widely known but explicitly formal intellectual property terminology. Patents, copyrights, and other intellectual property are valuable intangible assets, but intellectual capital extends our recognition of value to additional, less well-defined intangibles such as know-how and expertise (knowledge). As a result, a clear distinction exists in the field between data, information, and knowledge. Formally, data are observations, information is data in context, and knowledge is information subjected to experience, reflection, or some similar analysis (Zack, 1999b). Within the field, knowledge is often referred to as know-how, effectively a sort of learning based on experience, learning, or insight. Such a perspective flows naturally out of the more general DIKW (data, information, knowledge, wisdom) hierarchy suggested by Ackoff (1989).

Growth in knowledge of this sort can come about in different ways. Nonaka and Takeuchi (1995) borrowed the concept of tacit knowledge from the sociology literature (Polanyi, 1967), identifying and explaining tacit and explicit knowledge in a business context. Tacit knowledge is more personal, harder to express, and more difficult to codify within organizational information technology (IT) systems. Explicit knowledge, on the other hand, is easier to express, easier to share, and easier to store in IT structures. Nonaka & Takeuchi also developed the SECI or ba framework categorizing how knowledge grows, by tacit to explicit, tacit to tacit, explicit to tacit, or explicit to explicit transfer. The explicit to tacit process is of particular interest as it concerns the conversion of more structured intangible assets into personal tacit insights. From there, it is only a short step to the idea of creating new knowledge from data and information, foreshadowing how non-knowledge intangible assets can also create value. The overall objective of KM is to better understand how knowledge can be more effectively developed and employed by means of combination, sharing, learning, or similar means (Zack, 1999a; Grant, 1996).