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A look at improving knowledge management

This article appears in the issue March 2012 (100 Companies) [Vol 21, Issue 3]
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Another area for improvement in our field deals with KM metrics. I did a study last summer on KM metrics in which most of the metrics were system and output measures versus outcome measures. Organizations should be more interested in outcome measures.

Ways to improve

Another area where we can develop more rigor for KM usage is the "informal organization" or use of social networks. Katzenbach Partners in New York City (now part of Booz & Company) did a study on the informal organization and concluded that in most organizations, the informal organization is poorly understood, poorly managed and poorly leveraged.

One area where we can continue to better understand social networks is through the use of social/organizational network analysis. We also need to further study and operationalize critical success factors for knowledge management. I had performed a study previously and found five key categories why organizations were embarking on KM: adaptability/agility, creativity/innovation, institutional memory building, organizational internal effectiveness and organizational external effectiveness. Going back to the knowledge-based research, I also used fuzzy logic to help quantify some of those factors.

In our KM community, we also need to do a better job in applying and adapting techniques from other disciplines to use in KM. Because my background is in artificial intelligence (AI) and knowledge engineering, I see great synergies where AI can improve KM. For example, expert systems or knowledge-based systems technology has great similarities to what we do in KM in terms of knowledge acquisition and representation.

As 70 percent of lessons learned systems are ineffective (according to David Aha of the Naval Research Lab, and Rosina Weber of Drexel University) due to the reliance on passive analysis and dissemination instead of active analysis and dissemination (the push versus pull phenomenon), intelligent agent technology can help improve the current state of the art.

Aside from IT-based KM research issues, many people/culture/process-based KM research areas need to be addressed. Some examples include better understanding the interaction between knowledge codification and knowledge sharing networks; developing outcome measures and applying value network analysis; better understanding the relationships between change management, risk management and KM; understanding how to best embed KM processes into the daily working lives of employees and how best to formalize knowledge retention and transfer strategies.

Strategic intelligence

From a KM curricular standpoint, we can also improve in a number of ways. Better understanding of what I am calling "strategic intelligence"—the synergy between KM, business intelligence and competitive intelligence; looking at KM from an integrated, systems perspective; incorporating more practice-based modules into the KM curriculum (similar to what medical and now law schools are doing in the United States); examining KM from a multidisciplinary view; and looking at the synergy between KM and e-learning (in 2011, we published a book on that topic by Taylor & Francis).

So, we can summarize by saying that the KM field is still maturing, and we, as KM and intellectual capital educators, can help advance the field by putting more rigor behind it. Specifically, knowledge retention and transfer and cross-generational knowledge flows should be on the minds of senior leadership. We need to have improved methodologies for KM, and must continue to apply concepts from other disciplines, notably the knowledge-based research community. Also, we must think creatively in developing future KM programs and courses. 

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