As big data and business analytics continue to grow and draw 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. 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). 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 paper will look 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/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 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 paper will look at data on competitive intelligence 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 and significant development of intangible assets, 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.
