5.3 Knowledge Discovery/ Detection

5.3 Knowledge Discovery/ Detection

          In this section, we will examine KM initiatives involved in knowledge discovery & detection. This step deals with discovering the knowledge that an organization possesses. As we know once knowledge is created, it exists within the organization. However, before it can be reused or shared it must be discovered/detected, recognized, categorized and organized. Now we are going to discuss the former two aspects, while the latter is explained in the following section.

  • Discovering Explicit Knowledge: This is largely a process of sorting/exploring knowledge through documents, records and other artifacts, as well as discovering knowledge within existing data and knowledge repositories. IT can help in discovering hidden knowledge by looking at patterns and relationships within data and text. The main tools/practices in this case include intelligence gathering, data mining (finding patterns in large bodies of data and information), and text mining (text analysis to search for knowledge, insights, etc.). Intelligence gathering is closely linked to expert systems (Bali et al, 2009) where the system tries to capture the knowledge of an expert (Botha et al, 2008).
  • Discovering Tacit Knowledge: Discovering and detecting tacit knowledge is a lot more complex and often depends on the attitude, behavior and targets of the management and how does management gain an understanding of what their employees actually know. Since tacit knowledge is considered as the most valuable in relation to sustainable competitive advantage (Nonaka & Takeuchi, 1998). This is a crucial step and usually involves observation and awareness. There are several qualitative and quantitative tools/practices that can help in discovering and detecting tacit knowledge; these include knowledge surveys, questionnaires, individual interviews, group interviews, focus groups, network analysis, and observation. IT can be used to help identify experts and communities. Groupware systems and other social/professional networks as well as expert finders can point to people who are considered experts and may have valuable knowledge.
  • Discovering Embedded Knowledge: This implies an examination and identification of the knowledge trapped inside organizational culture, routines, processes, products etc. which has not already been made explicit. For this purpose, the management essentially asks "why do we do something in a certain way?" Observations and analysis, and the use of reverse engineering and modeling tools are very useful in this process.

          It is important to note that the sources of knowledge that an organization has access to may extend well outside the organization. This type of knowledge is called extra-organizational knowledge. This can exist in both formal and informal settings. The former refers to management driven initiatives like partnerships, while the latter refers to the informal networks of individual members. We are interested in the former, which can be located and managed at least to some degree. Gamble and Blackwell (2001) identify several such sources like alliances, suppliers, customers, etc. Knowledge from alliances and partners can exist in joint projects, shared knowledge/experts operational data and so on. Suppliers and customers can provide product feedback, trends, developments etc. Within their respective limitations, similar tools as above can be used to discover knowledge and/or knowledge sources.

          In Knowledge discovery and detection process the adoption of common practices makes the process of knowledge detection much easier. For example, teams could be asked to document aspects of their work using prescribed format & standards. IT can be used in this context as a means of feedback, communication, and cooperation between partners, and also as a way to gather, analyze, and "mine" knowledge, information and data.