The book titled, Strategic intelligence: Business intelligence, competitive intelligence, and knowledge management was edited by Jay Liebowitz. The book, entailing 244 pages, was published in 2006 by Auerbach Publications of Taylor & Francis Group in the United States of America. As an edited book chapter, it has seventeen (17) contributors examining related several issues within the context of this book.
According to editor of the book, Jay Liebowitz, strategic intelligence (SI) has mostly been used in military and defense settings, but its worth goes well beyond that limited role. His view of Strategic Intelligence is that every organization should be doing it, as it applies towards improving the strategic decision making process of an entity as it is thrust of this book. Furthermore, the editor’s experience in the knowledge management (KM), business intelligence (BI), and competitive intelligence (CI) fields has indicated that there are tremendous synergies between these areas. The ultimate goal is how to best use internal and external intelligence for making better decisions. In the same way that knowledge management is being used to break down the silos, so does this need to happen as a whole with the KM, BI, and CI disciplines. Each of these communities seems to be working in isolation; the walls should be demolished to maximize the collective value of what these disciplines offer to the organization. To help better understand the role of SI in the organization as the KM, BI, and CI fields merge, this book aids the decision maker in applying its underlying concepts.
Going further, the first part of the book discusses the convergence of KM, BI, and CI into SI. The second part of the book describes case studies written by respected individuals/contributors from leading organizations in the various fields of KM, BI, and CI. The cases reinforce some of the concepts presented in the first part of the book. For clarity of purpose, this review or critique would be done in two parts; part 1 contains conceptual explanations to key variables in the book while Part 2 contains case study reports.
PART 1 (Chapters 1 to 6)
In the first chapter of this book in Part 1, the editor explores the title, from individual transformation to organizational intelligence, which sees change as an omnipresent entity. According to the editor, there are certainly many events in life that may change a person. However, some believe that the genetic composition of an individual enforces a certain stability and pattern such that the environment may change, but the person never really changes. The same argument is made for knowledge management. It was noted in this chapter that, knowledge management involves best leveraging knowledge internally and externally in an organization and creating a process for valuing the organization’s intangible assets. Some people say that knowledge cannot be managed, i.e., the environment in which knowledge is housed, transferred, and used can change, but knowledge itself cannot be controlled. In this chapter, the editor ascribes to the philosophy that knowledge, as well as the environment itself, can be managed. Furthermore, it is discussed that culture, change management, and individual transformation are, among others, important components leading to organizational transformation and a heightened organizational intelligence.
Chapter 2 of this book focuses on the intelligentsia galore. This chapter holds the view that, there could be ways to consolidate and synthesize the various types of intelligentsia such as artificial intelligence (AI), business intelligence (BI), competitive intelligence (CI), into a meaningful framework. The narration in chapter upholds that many of the AI techniques could be useful in these other intelligentsia (such as knowledge representation techniques for developing knowledge ontologies or case-based reasoning for help desk applications or business rule engines); however, most of these other intelligentsia do not necessarily use AI in practice. While examining the state in chapter 2, AI deals with how we think, it seems only natural that KM should embrace some of these concepts to help people capture, organize, and share knowledge within the organization and externally with the stakeholders. From the conclusion of this chapter, it was noted that BI forms the next layer of the intelligentsia onion after KM, and then CI becomes the next layer by using both internal and external information and knowledge to develop a systematic and ethical program to manage, analyze, and apply this information and know-how for improving organizational decision making. Finally, the aggregation of all these various intelligentsia becomes SI for the organization to best make strategic decisions.
Chapter 3 explores the title, organizational intelligence through strategic intelligence. This chapter submits that, a synergy of business intelligence (BI), competitive intelligence (CI), and knowledge management (KM) has contributed to the formation and development of strategic intelligence (SI). Organizations need to apply these catalysts to foster strategic intelligence for improved decision making. It was noted from this chapter that, when looking at an organization’s intelligence, there are four general types of capital that can be harnessed and nurtured. The first is human capital, which is the brainpower of your employees (essentially, the knowledge that your employees possess). Structural capital is the second type, which refers to the knowledge gained from things you cannot easily take home with you from the office, like intellectual property rights. The third type of capital is customer capital (sometimes referred to as social or relationship capital). This is the knowledge gained from your customers and stakeholders, and you incorporate this knowledge into your own organization’s knowledge base. The last main type of capital is called competitive capital, which is the knowledge gained from your competitors. These four main types of knowledge will help an organization determine its IQ or intellectual capital.
Chapter 4 discusses the lessons learning in the intelligentsia melting pot. In understanding the basis of the analysis drawn from this chapter, it is noted that business intelligence cannot exist without knowledge management. According to the editor, whether talking about business intelligence (BI) or competitive intelligence (CI), a key ingredient must exist — is, knowledge management (KM). It was stated that, KM is determining how to best leverage knowledge internally and externally in an organization and how to create value out of the organization’s intangible assets. Specifically, KM is the process of identifying, capturing, sharing, applying, disseminating, and creating knowledge in the organization’s context. Besides knowledge dissemination techniques, KM can enhance the BI process through its emphasis on knowledge elicitation and sharing techniques. Most people in the KM community classify knowledge as tacit and explicit, or fluid and sticky. As BI and CI evolve, an understanding of the various links of entities and knowledge sources becomes important. The KM field has been applying social network analysis techniques to map the knowledge flows and detect knowledge gaps in the organization. In so doing, several case study research was done to validate the submissions in this chapter.
Chapter 5 is on competitive intelligence. In this chapter, it is believed that competitive intelligence is really involved with developing a systematic program for capturing, analyzing, and managing external (and internal) information and knowledge to improve the organization’s decision-making capabilities. As explained in this chapter, knowledge management (KM) and business intelligence (BI) are closely linked with CI. KM deals with how best to leverage knowledge internally within the organization and externally to the organization’s customers and stakeholders. Certainly, some cultures are more permissive and receptive to knowledge sharing. For example, in Jonathan Calof’s study at the University of Ottawa (performed for the Society of Competitive Intelligence Professionals), Canada was more open toward knowledge sharing versus the United Kingdom and France. This influences how willing people are toward sharing their information and knowledge. Similarly, BI deals with how best to capture and share internal information to make it widely available throughout the organization. KM and BI are close cousins to CI. Summarily, CI is as simple as that — collect, analyze, develop, and manage; collect the appropriate information and knowledge, analyze the information and knowledge, develop an approach based on the synthesis of the results, and manage your expectations and strategy, and adjust accordingly.
Chapter 6 discusses how strategic intelligence is the core of executive decision making. This chapter explains that, the essence of SI applies to all organizations — that is, how organizations can improve their strategic decision-making process. From the foregoing, it could be deduced that convergence and application of knowledge management (KM), business intelligence (BI), and competitive intelligence (CI) can lead to the development and implementation of strategic intelligence (SI). It is also explained in the chapter that, to help reduce this risk, executives can use structured decision-making approaches such as multicriteria decision making. Executives are typically faced with multicriteria decision making when addressing strategic decisions. The alternatives used in multicriteria decision making are often competing, and there are numerous criteria to be factored into the decision- making process. To provide some SI to senior leader decision making, human insight and gut feeling can be augmented by applying techniques to help structure one’s decision-making process. We have already highlighted the use of SWOT analysis, the balanced scorecard, scenario planning, etc., to help senior leaders better structure their decision process for making informed decisions. More importantly, one multicriteria decision-making-based approach that can be very useful to help quantify subjective judgments in decision making is the Analytic Hierarchy Process (AHP), which was developed by Thomas Saaty (University of Pittsburgh). The goal of the decision is first determined, and then criteria and subcriteria are developed, as well as various alternative solutions. A tree hierarchy is created with the goal at the top, the criteria and subcriteria in the middle, and the alternatives at the lower level. Through pairwise comparisons, the criteria are compared and weighted against each other with respect to the goal. This creates weights on the criteria that sum to one. Then, the alternatives are compared with respect to each criterion to get the weighting of the alter natives per criterion. Finally, a synthesis is made where the weights of the criteria and the weights of the alternatives per criterion are combined to get an overall weighting and ranking of the alternatives. This approach has been used by hundreds of organizations throughout the world to help them better structure their decision-making process, especially for strategic type of decisions.
PART 2 (9 Case Studies were presented)
Thomas E. Kern examined knowledge management and organizational learning at the Annie E. Casey Foundation: A case study. The contributor revealed that, the study began by identifying the obstacles to knowledge sharing that were impeding staff from breaking out of their program silos. An analysis and detailed staff survey brought several issues to the fore. First, accessing the most current and relevant knowledge across all programs was hindered by the lack of a centralized system for finding information. Second, staff members’ heavy workloads meant that they had limited time to spend publicizing new knowledge, especially when there was no structured way for doing this. Third, Casey’s organizational culture made over-reliance on informal (and, therefore, potentially less comprehensive) networks for exchanging knowledge the norm.
Maritza Morales did a case study in strategic scenario development. This case study is about the use of scenarios to define the strategic focus of Motorola, a leader in telecommunications industry. The key strategic question raised by the management team was: should Motorola continue to invest heavily in Asia in the expectation that increasing shares of its revenues will come from the Far East? Or does the long-term outlook favor the United States or Europe? In going through this process, it was very important to keep in mind that scenario planning does not predict the future. No matter how likely one scenario appears to be, the organization must take advantage of the insights achieved to prepare for a future that would largely be a combination of two or more of the scenarios envisioned. Also, the strategic insights resulting from the meritocracy scenario did provide the right focus to capitalize on the growth of the Asian region, especially the opportunities presented by China and Southeast Asia and the chief challenge that resulted from the 2010 scenario exercise was the need to drive momentum for a stronger China/Asia business throughout the company while keeping ahead of opportunities in Europe.
Peter Mckenney examined CI at a major telecommunications company. This case study will explore a collaborative relationship between a CI consulting firm and a Fortune 500 telecommunications corporation. This partnership has evolved over time and illustrates a unique, cost-effective method of integrating CI within a corporation. This innovative approach delivers a solid return on investment by optimizing the use of scarce resources.
Shereem Remez examined strategic in AARP. As it is stated, today’s organizations, whether in the profit or nonprofit sectors, are finding that technology and data-driven analysis can really provide a tremendous competitive edge. The journey, however, is a difficult one. It requires leadership, vision, discipline, teamwork, execution, and perseverance. At AARP, many steps have been taken toward building this SI infrastructure that is hoped allow members to serve the organization and society at large.
Stephan Berwick examines Northrop Grumman Information Technology: Business Intelligence Case Study on “Information Assurance” Competitive Analysis. While Todd Drake, Bill McGilvery and Liza Puterman looked at transforming data into actionable intelligence: Case studies using analyst’s Notebook and other i2 products. Going further, Keith B. Johnson and Cint Gauvin examined surviving and thriving despite the loss of a major customer at the analysis corporation. From their study, it was revealed that, the Analysis Corporation (TAC) was faced with the unexpected and nearterm loss of their largest contract. As this contract represented over 80 percent of TAC’s revenue, loss of this contract threatened its ability to continue as a viable business. Although a short-term solution to this problem was found, it was necessary to get more business to replace this contract. Through the use of business and competitive intelligence and leveraging of specialized knowledge, TAC was able to turn this threat into an advantage and expand its customer base.
Furthermore, Francisco J. Cantu, Silvia P. Mora, Aldo Díaz, Héctor Ceballos, Sergio O. Martínez, and Daniel R. Jiménez examined a methodology for strategic intelligence: A Roadmap Model, a Knowledge-Based Tool, and a Bio-MEMS Case Study. This study believes that strategic intelligence (SI) has become an indispensable task for competitiveness and enterprise development in the modern economy. Adding that, the synergy among business intelligence (BI), competitive intelligence (CI), and knowledge management (KM) for improving the organization’s strategic decision-making ability, SI offers decision makers a repertoire of methods, tools, and best practices for these three areas for accomplishing the company’s objectives.
Finally, Arik Johnson examined semiconductor CI — from current awareness to predictive decision making: Building a best-of-breed ci program at a top-tier global IC manufacturer. The study reveals that even among this rich, though myopic, universe of current-awareness-driven CI practices, there is very rarely concern directed toward more predictive capabilities of “strategic” CI or its ability to assist executive decision making, despite its increasing importance in an era in which regulators and shareholders alike demand greater reliability of forecasted earnings.
Cite this article as (APA format):
Bolaji David Oladokun (2024). Book Review: Strategic intelligence: Business intelligence, competitive intelligence, and knowledge management. Retrieved from https://www.africanresearchers.org/book-review-strategic-intelligence-business-intelligence-competitive-intelligence-and-knowledge-management/
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