Data, Knowledge, and Intelligence

Data, Knowledge, and Intelligence

G. Scott Erickson (Ithaca College, USA) and Helen N. Rothberg (Marist College, USA)
DOI: 10.4018/978-1-4666-5888-2.ch378
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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).

Key Terms in this Chapter

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

Intelligence: Actionable insights.

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

Information: Data in context.

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

Data: Observations.

Knowledge: Know-how or expertise.

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

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