Big Data, Knowledge, and Business Intelligence

Big Data, Knowledge, and Business Intelligence

DOI: 10.4018/978-1-5225-2255-3.ch081
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Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.
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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).

Key Terms in this Chapter

Data: Observations.

Knowledge Management: Methods to identify, organize, and leverage knowledge assets through further distribution and sharing.

Information: Data in context.

Big Data: Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications.

Competitive Intelligence: Gathering and analyzing data, information, and knowledge relating to a competitor or related topic, resulting in actionable intelligence.

Intelligence: Actionable insights.

Intellectual Capital: Knowledge assets of the organization, commonly thought to be made up of human capital (job-related knowledge), structural capital (persisting knowledge assets of the organization such as systems or culture), and relational capital (knowledge relating to external entities).

Business Analytics/Intelligence: Analysis of big data for strategic, tactical, and/or operational insights.

Knowledge: Know-how or expertise.

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