Knowledge Management Processes
3.8 Summary
intelligence cycle. The intelligence BI function should collect and analyze real- time workflow data to provide answers to questions such as:
• What are the relative volumes of requests (for intelligence) by type?
• What is the “cost” of each category of intelligence product?
• What are the relative transaction costs of each stage in the supply chain?
• What are the trends in usage (by consumers) of all forms of intelligence over the past 12 months? Over the past 6 months? Over the past week?
• Which single sources of incoming intelligence (e.g., SIGINT, IMINT, and MASINT) have greatest utility in all-source products, by product category?
Like their commercial counterparts, the intelligence BI function should not only track the operational flows, they should also track the history of opera- tional decisions—and their effects. Both operational and decision-making data should be able to be conveniently navigated and analyzed to provide timely operational insight to senior leadership who often ask the question, “What is the cost of a pound of intelligence?”
sustainable knowledge creation and dissipation throughout the organization are emphasized in this phase. The attention in this generation has moved from understanding knowledge systems to understanding knowledge workers. The third generation to come may be that of KM innovation, in which the knowl- edge process is viewed as a complete life cycle within the organization, and the emphasis will turn to revolutionizing the organization and reducing the knowl- edge cycle time to adapt to an ever-changing world environment [36].
In this chapter, we introduced a taxonomy that distinguished the processes of KM by the modes of transactions between explicit and tacit knowledge; the subsequent five chapters are organized by this distinction in perspective and mode of transacting knowledge (Table 3.14):
• People and organizations. Chapter 4 introduces the characteristics and virtues of the knowledge-based organization. This includes networks of people who share vision and values to collaboratively solve problems, learn, and adapt to the changing threat or business environment. The emphasis is on the socialization of tacit knowledge exchange.
• Processes and systems. Chapters 5–7 describe the internalization and externalization transaction processes that exchange tacit and explicit knowledge. Chapters 5 and 6 detail the principles and practice of the core KM competency of intelligence—analysis and synthesis—where
Table 3.14
Structure of KM Presentation in This Text
Perspective of KM
People
Operational View
Processes HCI View
Technology Technical View Focus of the
Enterprise
Operations, processes, training
Tools, thinking aids, and visualization
Infrastructure, knowledge protocols
Knowledge Transactions
Socialization tacit-to- tacit transactions
Internalization and externalization transactions between tacit and explicit
Combination explicit- to-explicit transactions
Subsequent Chapters in This Book
Chapter 4: The knowledge-based intelligence organization
Chapter 5: Intelligence analysis and synthesis Chapter 6: Implement- ing analysis-synthesis Chapter 7: Knowledge transfer and transaction
Chapter 8: Knowledge combination Chapter 9: Enterprise architecture
analysts network with other analysts and machines to create intelligence products.
• Technology.Chapters 8 and 9 then describe the information technolo- gies (computing processes, processing nodes, and interconnecting net- work technologies) that constitute the implementation of the architecture of the intelligence enterprise.
Endnotes
[1] O’Dell, C. and Grayson, C. J., Jr.,If Only We Knew What We Know,New York: Free Press, 1998.
[2] KM definitions are quoted from the DoD Web site: http://center.dau.mil/Topical_
Sessions_templates/Knowledge_Management/Definitions_of_Knowledge_Management.htm.
[3] Watson, S., “Getting to AHA!”ComputerWorld, January 26, 1998.
[4] NSA adopted the definition from the APQC. See Brooks, C. C.,“Knowledge Manage- ment and the Intelligence Community,”Defense Intelligence Journal, Vo. 9, No.1, Winter 2000, p.17. This issue of the journal is devoted to KM in the U.S. IC.
[5] Table 3.2 is based onAdvancing Knowledge Management in DoD: A Primer for Executives and Practitioners, Directorate of eBusiness & Knowledge Management, OASD/C3I, September 2000, p. 2.
[6] Defense Planning Guidance FY02-07, April 2000, p. 102.
[7] Some texts refer toembeddedknowledge as a third category; this knowledge integrated in business processes can include either unconscious human process skills (tacit) or explicitly coded computer programs (explicit).
[8] Polanyi, M.,The Tacit Dimension, Garden City, NY: Doubleday, 1966.
[9] Polanyi, M.,The Tacit Dimension, Garden City, NY: Doubleday, 1966, p. 7.
[10] Descartes published his Discourse on Method in 1637 and described his four-step problem-solving method of analysis and synthesis in “Part II—Principal Rules of the Method.”
[11] Pascal, B.,Pensees, “Part IV, The Means of Belief,” 1660, para. 277.
[12] Devlin, K.,Goodbye, Descartes: The End of Logic and the Search for a New Cosmology of the Mind, New York: John Wiley & Sons, 1997, pp. vii and ix.
[13] This is not to imply that the human mind is a new intelligence target; leadership inten- tions and customer purchasing intentions are the targets of national and business intelli- gence, respectively. New representations will permit more accurate modeling of these targets.
[14] Categories are adapted from: Davidow, W. H., and M. S. Malone,The Virtual Corpora- tion, Chapter 3, New York: Harper-Collins, 1992.
[15] The U.S. Defense Modeling and Simulation Office distinguishes model as “A physical, mathematical or otherwise logical representation of a system, entity, phenomenon or process,” and a simulation as, “A method for implementing a model over time.” Models are essentially static representations, while simulations add dynamic (temporal) behavior.
[16] Weick, K.,Sensemaking in Organizations,Thousand Oaks, CA: Sage Publications, 1995.
[17] Davenport, T. H., and Prusak, L.,Working Knowledge: How Organizations Manage What They Know, Boston: Harvard Business School Press, 1998.
[18] Nonaka, I., and H. Takeuchi,The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation,New York: Oxford University Press, 1995.
[19] Nonaka, I., and H. Takeuchi,The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation,New York: Oxford University Press, 1995, p. 72.
[20] The ASP business model is also called managed service provider, netsourcing, or total serv- ice provider models.
[21] Sveiby, K. E., “The ‘Invisible’ Balance Sheet,” September 8, 1997, updated October 2001, accessed on-line January 1, 2003 at http://www.sveiby.com/InvisibleBalance.htm.
[22] “Effective Use of Intelligence,”Notes on Analytic Tradecraft,Note 11, CIA Directorate of Intelligence, February 1996, p. 2.
[23] O’Dell, C. and Grayson, C. J., Jr.If Only We Knew What We Know,New York: Free Press, 1998, p. 133.
[24] Pastore, R., “Noodling Numbers,”CIO, May 1, 2000, p. 122.
[25] Strassman, P. A., “The Value of Knowledge Capital,”American Programmer,March 1998.
See also Strassman, P. A.,Knowledge Capital,New Canaan, CT: The Information Eco- nomics Press, 1999.
[26] Sveiby, K. E.,The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, San Francisco: Berrett-Koehler, 1997. ICM is similar to the introductory concept illustrated in Figure 3.7.
[27] See Skyrme, D.,Measuring the Value of Knowledge, London: Business Intelligence, 1998.
[28] Kaplan, R. S. and D. P. Norton,The Balanced Scorecard, Boston: Harvard Business School Press, 1996; see also Kaplan, R. S., and D. P. Norton, “Using the Balanced Scorecard as a Strategic Management System,”Harvard Business Review, January–February 1996.
[29] A Consumer’s Guide to Intelligence, Washington D.C.: CIA, n.d., p.42.
[30] This example model follows the approach introduced by Kaplan and Norton inThe Bal- anced Scorecard, Chapter 7.
[31] Hagel, J., and M. Singer,Net Worth, Boston: Harvard Business Review Press, 1999.
[32] This figure is adapted from, “E-Commerce Survey,”The Economist, February 26, 2000, p. 9.
[33] The concept ofjust in timedelivery results from efficient supply chain management and results in reduced inventories (and cost of inventory holdings.) The inventory reduction benefits of just in time delivery of physical products can be high; for intelligence, the bene- fits of inventory (information) reductions are not as great, but the benefits of making
information available at the right time can provide significant benefits in reducing infor- mation overload.
[34] Nylund, A. L., “Tracing the BI Family Tree,” Knowledge Management, July 1999, pp. 70–71.
[35] See Davenport, T., “Knowledge Management, Round 2,”CIO Magazine, November 1, 1999, p. 30; for a supporting viewpoint, see also Karlenzig, W., “Senge on Knowledge,”
Knowledge Management, July 1999, p. 22.
[36] Firestone, J. M.,Accelerated Innovation and KM Impact,White Paper 14, Executive Infor- mation Systems, Inc., December 1999.