ABSTRACT:
Knowledge Management (KM) is touted as a corporate cost savings process that can bring enhanced performance capabilities with broader availability and utilization of key corporate knowledge assets. This paper explores some of the current concepts, information sources, and various issues surrounding Knowledge Management systems application, and identifies potential reasons why KM is failing to deliver the promised, expected results. I purport that concentrating on a single Knowledge Management approach, excluding other methodologies, is not an effective approach.
Introduction
For more than a decade, individuals, corporations, consultants, and intellectuals have been discussing, trying to define, and trying to implement Knowledge Management systems. Numerous references chronicle successful Knowledge Management implementations and causal credit for significant cost savings. Kenneth T. Derr, chairman and CEO of Chevron is quoted, at the 1999 San Francisco Annual Knowledge Management World Summit, as stating, “Of all the initiatives we’ve undertaken at Chevron during the 1990s, few have been as important or as rewarding as our efforts to build a learning organization…. Improved management of knowledge was instrumental in reducing operating costs from $9.4 billion to $7.4 billion from 1992 to 1998…”(Velker, 1999).
The Chevron claim of knowledge management success is not unique. Similar claims of success can be found using very little research effort. Graef (1997) identifies 12 techniques used to value corporate intangible assets such as intellectual assets. With twelve methods of measuring intellectual assets, from my point of view a critical business question is, “Why isn’t every business realizing dramatic cost savings, process improvements and improved profit margins using this critical asset management process?”
Knowledge Management Application Definition
The lack of a quick answer to the previous question resides in the lack of a clear and universal acceptance of a Knowledge Management definition. Academic and trade resources (Kemph, 2001) state there is no single definition of Knowledge Management KM). Turban & Aronson, (2001) state that “ . . . there is no uniform definition or consensus on what … knowledge management specifically mean[s].” Thus, with no clear, single, universal definition of KM, defining, implementing and quantifying a KM system is problematic.
For the purposes of this article, I define Knowledge Management as those systems that manage the corporate knowledge asset that is generated throughout the organization. This knowledge base will include both corporate tacit and explicit knowledge. I propose that the KM system is a spiraling process that first identifies knowledge, validates the knowledge, stores the validated knowledge, provides a knowledge filter, and provides user knowledge access. The critical key is that the KM system is accessible to the users of the knowledge so the knowledge can be used, transformed, and enhanced.
A spiralling knowledge base process provides corporate knowledge with one of at least three outcomes. First, others can reuse the knowledge in the organization for similar knowledge needs applications. Second, rather than being reused, the knowledge can become in some other way invalidated. When the knowledge is invalidated, the KM system will provide processes to delete this data set. Finally, the knowledge can be transformed into new knowledge. In the latter process, the corporation expands upon its critical knowledge asset to develop new processes or products that provide for company growth.
With the preceding definition of Knowledge Management--those systems that manage the corporate knowledge asset that is generated throughout the organization--as this paper’s reference point, and acknowledging that, as Schulz and Jobe (2001) note “Empirical research on organizational knowledge is still in its infancy,” some common implementation pitfalls are emerging. To better understand the KM system pitfalls, this paper identifies some of the issues surrounding the various KM system implementation aspects. Specifically, items dealing with personalization, codification, and technology issues will be briefly discussed.
Personalization KM
Knowledge personalization involves developing a system that captures tacit knowledge sources. Tacit knowledge is that knowledge that is difficult to place in written form. Hansen, et al. (2000) describe KM personalization as focusing “… on dialogue between individuals, not knowledge objects in a database…. Knowledge that … is transferred in brainstorming sessions and one-on-one conversations.”
So, tacit knowledge is that knowledge that is difficult to describe or write down. Smith (1983) states that “… we can know more than we can tell.” As an example of tacit knowledge, the wine connoisseur who can identify subtle undertones of black currants, ignoring the differences in taste bud capability, holds tacit knowledge. This individual can describe the process, she/he can even write the process down, but this will not make the user of the transferred knowledge able to detect the same taste sensations.
In the wine connoisseur example, the ability to detect the subtle tastes and identify the associated flavor isn’t a knowledge that can be validated, managed, or transferred using a stored KM system. Yet, if you are physically able to taste the subtle flavors, you have a much greater chance of gaining this tacit knowledge by associating with those who have the knowledge already.
Nanaka & Takeuci (1995) present tacit knowledge as a process where organizations transfer knowledge through a socialization process that allows the development of new ideas and perspectives. Tacit knowledge pitfalls involve potential knowledge sabotage and the difficulty in capturing this knowledge base.
Sabotage of tacit knowledge transfer is a very real pitfall. If the corporate culture isn’t one of cooperation and sharing, then the probability of successful tacit knowledge transfer is slim. If employees feel that when others know what they know (their tacit knowledge) they can be replaced, then passive or active sabotage of tacit knowledge transfer is a very real possibility.
Codification KM
While corporations may fail at capturing the tacit knowledge base there is another potential common pitfall of total reliance on knowledge codification. Hansen et al. (2000) describe codification as knowledge that “… is extracted from the person who developed it, then made independent of that person, and reused for various purposes.” Codification is the process that creates knowledge storehouses. When performing an Internet search on “knowledge management,” it is codification that provides 788,000 “hits.”
Codification provides a valuable KM process capability. Without codification, the ability to allow explicit knowledge transfer is severely limited. Two codification pitfalls include under- or overutilization of this process. Underuse of codification hinders the spread and use of this corporate asset. This fosters the state of reinventing the wheel over and over again.
Schulz & Jobe (2001) point out that “One could argue that firms are codification machines …” which, in far too many cases, may be true. Without a clear focus on what the Knowledge Management system is to deliver, many firms charge down the path of storing everything within the KM system. The end result is often information overload.
Information overload is often a direct result of codifying, and storing in an electronic database, all the company’s procedures, policies, drawings, specifications, references, and just about anything anyone can write down. Unfortunately, when someone tries to find the piece of knowledge they need, the corporate Intranet search returns the equivalent of 788,000 hits. Information overload creates the condition where, as Cross & Baird (2000) point out, “People usually take advantage of databases only when colleagues direct them to a specific point in the database.”
Overuse of codification creates an information overload. In some respects, an information overload is worse than underutilization. In an overload state, people start to avoid using the system, which then creates the same negative impact as the underutilization. The information overload also creates monster databases that must be managed and supported. As will be pointed out in Section 5, Technology Solutions, database maintenance isn’t cheap.
Technology Solutions
Technology is sometimes looked upon as the Holy Grail that will save the company and move it forward. Technology provides for the transfer of knowledge that is codified and packaged into formats that allow its transmission to others (Schulz & Jobe, 2001). Technology involves the telecommunications infrastructure as well as the computer (IT) infrastructure.
Telecommunication systems are the glue that bind all the physical, diverse Knowledge Management system infrastructures and users together in a unified effort. With a shortage of academic research on the impact of telecommunication infrastructure and no clear recommended guidelines, corporations tend to “buy” best guess needs. Best guess needs tend to be what the local information technology “telecommunications guru” says the company needs. Typically this information source is providing input based on personal desire rather than factual infrastructure assessment. Unfortunately, if you guess low and insufficient bandwidth is allocated, system response times slow down and user satisfaction is affected. For example, if your data requirements demand a full 10-megabit Ethernet link and the guru only specified a 1.5-megabit “T-1” network, you would have less than 10% of the capability covered. In this scenario, the KM system fails due to the glue rather than the applications.
With the opposite extreme, the firm over commits its telecommunication needs. This creates a condition of paying for more than you need, a condition that most firms would probably not do.
Closely coupled with the telecommunication infrastructure is the Knowledge Management IT system selection. While there are many KM software systems available and great strides have occurred, these tools are not a single key to success.
Process Implementation Traps
Cross & Baird (2000) clearly identify that “… organizations must do more than accrue and store knowledge in order to improve their profitability.” Some key points are starting to emerge that will increase the probability of successfully implementing a Knowledge Management system. First and foremost, organizations must avoid the pitfall of being unfocused in their approach. To become focused, the firm must know what type of business it is in and how it conducts its unique corporate function. The final mixture of KM system implementation is driven by fully understanding if the corporate function is to replicate previous work as a standard offering or if the majority of work involves unique development. Taking critical time to analyze questions such as how is the implementation driven by this knowledge? What might a company, whose work is standard and repetitious, do differently from a company that produces a unique product? What decisions are different? will help the organization become focused in its’ approach.
The second pitfall involves believing that the panacea is 100% personalization or 100% codification or trying to do both equally. Depending on the company’s core function either personalization or codification will be predominant but not the only system. Lacking any detailed quantifiable research starting with an 80/20 split (personalization/codification) is a good estimate.
A third pitfall involves not adequately supporting the process. Knowledge Management system implementation and ongoing support is not low cost. Hansen et al. (2000) identify that Ernst & Young had spent greater than $500 million on IT and people, and on a smaller scale, Access Health started with a $16 million investment and then added an additional $40 million to adequately scale the system for their users. These systems are not low cost to implement and they do require ongoing support. Failing to budget sufficiently will debilitate the KM system and lead to failure.
A fourth pitfall is the failure to maintain the KM system. As the company knowledge asset changes, methods and procedures are required to update the system to reflect the new knowledge and to delete the invalidated knowledge sources. Fogarty (2002) makes the case that adequately learning to manage data helps prevent the organizations from strangling on their own information.
Conclusions and Recommendations
Knowledge Management systems are not being effectively implemented and utilized. A recent Korniferry International survey found that at least 70 percent of 5,000 knowledge workers and business leaders in global, technology-savvy firms are “reinventing the wheel daily…” (Miller, 2000). Data exist that indicate that a “one-size fits all approach” is invalid.
To avoid the wrong path down the Knowledge Management system implementation process, the company must first become focused in its approach. The ultimate KM system must become part of the organization’s normal working process rather than a special function. Failing to plan, focus, and integrate the process as part of the company normal routine results in lost time, effort, and money.
To be successful, the firm must understand its core business strategy, understand what it doesn’t know, develop a process approach, and allow adequate funding.
Future Research Areas
As Schulz & Jobe (2001) point out, empirical research in the corporate Knowledge Management world is limited. Many opportunities exist for further detailed empirical research. Two important areas that warrant further study include:
1. Telecommunications Knowledge Management system needs.
2. Appropriate mixture of personalization, codification, and technology by industry type.
References
Cross, R., Baird, L. (2000) Technology Is Not Enough: Improving Performance by Building Organizational Memory, Sloan Management Review, Vol. 41, No. 3
Fogarty, K. (2002) Learn to Manage Data, not Crises, Computerworld, Vol. 36, No. 16
Graef, J. (1997) Measuring Intellectual Assets, Montague Institute Review, www.montaguecom/le/.e1096.html
Hansen, M.R., Nohria, N., Tiernye, T. (2000) What’s Your Strategy for Managing Knowledge, The Knowledge Management Yearbook 2000-2001, Part Two, 2000, Cortada, J. W., Woods, J.A., (Eds), CWL Publishing Enterprises, Butterworth-Heiniman, Boston
Kemph, R. (2001) Integrating Knowledge Management Into Project Management – A Practical Approach, Project Management Institute Seminar/Symposium
Miller, L. (2000) Wanted: Improved Communication, Internal Auditor, Vol. 57, No. 5; pp.13
Nanaka, I., Takeuci, H. (1995) The Knowledge Crating Company, Oxford University Press, New York
Schulz, M., Jobe, L.A. (2001) Knowledge Management; International Business Enterprises, Journal of High Technology Management Research, Vol. 12, No. 1
Smith, P. (1983) The Tacit Dimension – Michael Polanyi, Doubleday, New York
Turban, E., Aronson, J.E. (2001) Decision Support Systems and Intelligent Systems, 6th Ed., Prentice Hall, Saddle River, NJ.
Velker, L. (1999) Knowledge the Chevron Way, KMWorld, Vol. 8, No. 2.
About The Authors:
Morgan Henrie, PMP, is a Ph.D. student at Old Dominion University and at the University of Alaska Anchorage, studying Russian Far East methods of project management. He is a certified Project Management Professional, holds a Master of Science Degree in Project Management and undergraduate degrees in Electronic Engineering and Technology Management. Mr. Henrie is the President of MH Consulting that provides project management and business management consultation, nationally and internationally, to the oil and gas industry as well as the telecommunication industry.
Dr. Oliver Hedgepeth is an Assistant Professor of Logistics in the College of Business and Public Policy at the University of Alaska Anchorage. His interests are in knowledge management, the management of business logistics, and project management. He earned his Ph.D. from Old Dominion University in Engineering Management, and has worked 28 years with the U.S Army as a civilian analyst developing computer models and artificial intelligence applications for logistics systems.