Purpose of the Study

As Big Data and business analytics continue to grow and draw more attention, there is also an increasing recognition that existing theory and conceptual development in other areas studying intangible assets may have something of value to add. This study explores the potential of that relationship.

About the Background of This Work

The authors continue a research stream exploring the connections between knowledge management, competitive intelligence, and Big Data/business intelligence. This includes theory development, comparing the concepts of the different fields, and looking at where contrasting emphases can add value through cross-fertilization of ideas. The stream also includes comparison of methods and techniques, from Big Data platforms to knowledge management (information technology solutions, communities of practice, etc.) and on to competitive intelligence analysis tools (e.g., environmental scanning, war games).

What Is the Scope of This Work?

While further developing themes from some earlier work, such as the role of business analytics in recognizing the value of basic data and information and the similar contribution of knowledge management to encouraging and capturing insights from intangible assets, this chapter looks more specifically at the potential contribution of competitive intelligence to our understanding of all these fields. Data are available on the industry level concerning Big Data capabilities and knowledge management or intangible asset development. To these are added further data, specifically on competitive intelligence activity and threats in comparable industries.

Focusing on competitive intelligence (CI) can bring new insights to the conversation. CI has always valued the full range of intangible asset inputs (data, information, and knowledge) and actionable intelligence, something knowledge management can neglect (with its strict definitions of purportedly more valuable knowledge vs. mere data or information). CI can also be more directed, looking for additional data, information, or knowledge in a specific area in order to address a specific question. This chapter looks at data on CI activity in specific industries, identifying those with high intelligence commitment as opposed to those without.

These results will be compared and contrasted with data on Big Data potential, also by industry. As a consequence, the authors are able to prescribe directions for the development of all, some, or none of the disciplines in question while also providing recommendations for cross-field combinations for greater impact.

Definition of the Key Concepts

Data: accumulated observations Information: organized data

Knowledge: data and information subjected to reflection and experience Knowledge management: deliberate programs to leverage and grow knowledge assets, including learning, sharing, and discovery Intellectual capital: knowledge assets, usually implying categorization and/or metrics

Intelligence: applied knowledge, often at the strategic level, usually implying action Competitive intelligence: the process of gathering, analyzing, and acting upon data, information, and/or knowledge concerning a competitor, technology, or related subject

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