Knowledge Management and Data Mining

We are in the information age and as the demand for information and knowledge increases so did the need to access, process and disseminate knowledge and information effectively increases. This has led to the development of a process called Knowledge management. It is a high trending topic in industries and academic establishments globally similar to the buzz created by cloud computing in the information technology world. There is an array of definitions for knowledge management which is because of its dynamic nature.

According to (Charles & Chauvel, 1999) knowledge management is clearly on a slippery slope of being intuitively important yet intellectually elusive: * Important because, “With rare exceptions, the productivity of a modern corporation or nation lies more in its intellectual and system capabilities than in its hard assets… ” (Quinn et al. ,1996) as cited by (Charles & Chauvel, 1999) * Elusive because, “To define knowledge in a non-abstract and non-sweeping way seems to be very difficult. Knowledge easily becomes everything and nothing” (Alvesson, 1993) as cited by (Charles & Chauvel, 1999).

Knowledge management is a consequence of a combination of two factors: the business world’s enthusiasm for intellectual capital and the introduction of the corporate intranets. Though knowledge management can be defined in a lot of ways but it is essentially a continuous process that begins with data that is being processed to its eventual application to benefit an organisation. According to (DeJarnett, 1996) as cited by (Mahieddine, 2011) ‘Knowledge Management is…. Knowledge creation, which is followed by knowledge interpretation, knowledge dissemination and use, and knowledge retention and refinement’.

In my view the definition above is apt of all the several definitions because it summarizes the steps involved in the successful implementation of a knowledge management program in any establishment. To fully appreciate the term “knowledge management”, we need to be apprised of the transition from its purest untapped form which is data to wisdom. This is known as the knowledge hierarchy. This is important to know because it is useful in differentiating the terms data, information and knowledge which are very similar in meaning.

According to (Ackoff, 1989) as cited by (Gene, Durval, & Anthony, 2004), Knowledge can be classified into five categories: 1) Data: Data is raw. It is a fact with little or no importance in its current state. It is therefore not useful to anybody or any establishment. 2) Information: Information is data that has been processed to be meaningful by way of analysis. 3) Knowledge: Knowledge is a pliable mix characterised by experiences, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information.

It is stored in the mind of knowers. It can be found in the day to day activities and businesses processes in an organization (Davenport & Prusak, 1998) as cited by (Mahieddine, 2011). Knowledge can be divided into two functional categories. * Tacit Knowledge: This is an undocumented form of knowledge that is acquired from experiences, beliefs, perceptions, wisdom and intellectual property. It can be found in teams and individuals (Explicit & Tacit Knowledge, 2003) and an establishments operating culture. * Explicit Knowledge: This is a documented and formal form of knowledge.

It is stored in form of books, journals, lectures, formulae etc so as to be accessed by others. (Davenport & Prusak, 1998)as cited by (Mahieddine, 2011). Below is a diagram that demonstrates how tacit and explicit knowledge interact in the knowledge creation process (Nonaka & Takeuchi, 1995) as cited by (Explicit & Tacit Knowledge, 2003). (Nonaka & Takeuchi, 1995) as cited by (Explicit & Tacit Knowledge, 2003) goes further to explain the synthesis between tacit and explicit in the knowledge management creation process with the table below.


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