Knowledge management

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Knowledge Management or KM is any process which incorporates the desire to expand our range of inquiry with the need to simplify our decisions, options or actions. [1] This can involve both human and technological applications.

Overview

The knowledge management process strives towards a two-fold result of:

  • organizing existing knowledge, and
  • facilitating the creation of new knowledge.

Towards this end, KM has always existed on an informal basis i.e. brainstorming, colleagues chatting, ad hoc filing systems, mentoring etc. As an emergent business practice, KM has seen the introduction of the chief knowledge officer, and the establishment of corporate Intranets, wikis, and other knowledge and information technology practices.

Theory

KM uses a specific lexicon to describe or model the dynamics of the knowledge exchange, i.e. communication, creativity and organization. For example in the last ten years, the Internet has seen groups establish discussions on the use of intellectual capital as a value metric; the meaning of tacit vs explicit knowledge; and more.

The KID Approach

To implement a KM strategy, it obviously helps to understand the relevance of terms like knowledge, information and data (KID). As Peter Drucker writes, 'information is data endowed with relevance and purpose. Converting data into information thus requires knowledge.'

Denham Gray uses the example of baking a cake to define knowledge, information and data:

  • data - the different ingredients i.e. flour, water, eggs, sugar etc.
  • information - the recipe i.e. mix flour, eggs and water, preheat oven to 400 etc.
  • knowledge - the know how the cook uses to bake the cake, to best utilize the data and information available. [2]

Don Mezei compares knowledge, information and data to levels of context. As a form of appropriation, we are more apt to discuss law in terms of knowledge than data, while we are more apt to describe the path of an electron or density of a metal in terms of data rather than information. [3] So overall:

  • Data is used to describe the world that extends between inorganic matter and biological forms i.e. fuzzy logic.
  • Information is used to describe the world that extends between biological forms and social groups i.e. photosynthesis.
  • Knowledge is used to describe the world that extends between social groups and intellectual ideas i.e. United States Constitution.

In another example, he illustrates how knowledge, information and data can be understood using the context of the alphabet, i.e. how information can be construed from data etc:

  • (T)(h)(e)(o)(l)(d)(s)(h)(o)(e) - data
  • (The)(old)(shoe) - information
  • (The old shoe) - knowledge
Tacit vs Explicit

Nonaka/Takeuchi conclude that all knowledge creation begins with the individual. [4] This process involves the interaction of tacit and explicit aspects, like yin and yang. Tacit knowledge is knowing that has been internalized, and is difficult to share or express. Explicit knowledge has been externalized, and is easier to exchange. As an organization, we strive to mirror the knowledge creation process of the individual. [5]

Practice

As an emergent business practice, KM seeks to leverage the competitive advantage that comes with improved or faster learning and new knowledge creation.

Technology and the Internet

Since it's adoption by the mainstream population and business community, the Internet has led to an increase in creative collaboration, learning and research, e-commerce, and instant information. With improved technologies, gone are the days of dusty bookshelves, missed messages and snail mail. In many respects, the practice of KM is an in-step or mirrored evolution that coincides with the growth of both IT and the Internet.

E-learning, web conferencing, and collaborative software are examples of the knowledge management process. Each application can expand the level of inquiry available to an employee, while providing a platform to achieve specific goals or actions.

Data Mining

Data mining is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics, machine learning and pattern recognition.

  • Knowledge management - an organizational process which transforms intellect into intellectual capital. [6]
  • Intellectual capital - represents the intangible assets of a company which contribute to its valuation.
  • Chief Knowledge Officer - an executive responsible for maximizing the knowledge potential of an organisation.
  • Knowledge - that which can be acted upon.
  • Personal knowledge management - the organisation of an individual's thoughts and beliefs.
  • Enterprise knowledge management - the strategy, process or technologies used to acquire, share and re-use an enterprise's knowledge and understanding.

Conclusion

Just as Collins and Porras (Collins, J. and Porras, J. (1997) Built to Last, New York: Harper Collins) write that 'successful companies simultaneously preserve the core and stimulate progress', knowledge is managed by expanding our range of inquiry while simultaneously refining the understanding that drives our decisions, options or actions.

References

[1] Mezei, D. (2006) posted on theoryofkm.com. Mezei, D. (2002). The One Minute Knowledge Manager; Mezei, D. (2000). Unified Theory of Knowledge.

[2] Gray, Denham. (1998) archived message on brint.com

[3] Pirsig, R. (1992) LILA: An Inquiry into Morals, New York: Bantam Books.

[4] Nonaka, I. and Takeuchi, H. (1995). The Knowledge Creating Company, New York: Oxford University Press.

[5] Nonaka, I. and Takeuchi, H. (1995). The Knowledge Creating Company, New York: Oxford University Press.

[6] Mezei, D. (2003) posted on theoryofkm.com

See also

Blogs

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