Free Custom «Challenges and Solutions to Organizational Knowledge Management » Essay Paper

A knowledge base refers to a type of a database that is used to manage knowledge by facilitating the processes of computerizing knowledge collection, organization and retrieval. Thus a system that is knowledge based will be able to output justified intelligent decisions. Knowledge base management began in the 1990s and was supported by advancement in communication technology(Grant & Grant, 2008). The knowledge can be either machine readable or human readable. In a machine readable knowledge base, the processes of storing knowledge is computerized so that deductive reasoning can be used automatically with such knowledge. The human readable knowledge base on the other hand simply facilitates the process of accessing the knowledge in order to solve a particular problem.

There is a clear distinction between data, knowledge, information and wisdom. Data refers to the set of symbols that inform the process of generating information. This means that data on its own is meaningless and can not be used to make decisions (Bellinger & Castrol, 2004). It is instead processed in order to produce information. Thus information is the product of data processing. Unlike data, information is meaningful and can be used to make decisions (Bellinger & Castrol, 2004). Knowledge is produced if the relationship between data and information exists in a consistent manner. Thus it refers to the process of applying available data and information in solving a problem (Bellinger & Castrol, 2004). Finally wisdom refers to the understanding whose validity has been evaluated.

 

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A knowledge base differs from traditional database due to the fact that it involves learning and answering of questions rather than mere extraction of data. While the traditional database is simply a collection of data resources, a knowledge base incorporates human knowledge in solving problems usually through a computerized system(Maier, 2007). Unlike the traditional database, a knowledge base system is dynamic. This is based on the fact that it is "an artificial intelligence system that involves learning"(Maier, 2007).

The challenges involved in knowledge base management systems include the following. First, data accuracy is a challenge since the quality of information and knowledge will be compromised if the data used to generate them was not validated(Maier, 2007). Second, data interpretation will also affect the quality of knowledge. This is because interpretation influences the level to which information is considered to be meaningful(Maier, 2007). Third, data has to be relevant in order to answer the questions that transpire in the process of generating information and knowledge. Finally, there should be a relationship between the knowledge and the decision-making process. This means that the knowledge will only be useful if it truly supports the decision-making process.

The following socio-technical considerations are associated with the implementation of a knowledge base management system. First, the organization must decide on the goal to be achieved(Maier, 2007). This is a technical consideration since it relates to the processes of the organization such as production. Second, the organization must decide on the methodology of achieving the goal. This is also a technical consideration since special knowledge and expertise is needed to achieve the desired goals(Maier, 2007). Finally, the individuals who are in charge of the process of achieving the goal must be identified. This is a social consideration since it relates not only to the competence of the responsible persons, but also to the impact of the goal on the rest of the organization's workers.

A wisdom base management system would be desirable for an organization. However, implementing it would be very difficult. This is because wisdom develops when a person is able to understand the underlying principles that inform the patterns that are associated with the generation of knowledge (Bellinger & Castrol, 2004). Thus unlike knowledge, wisdom develops within a context of its own making. This makes it difficult to develop a standardized criterion for generating wisdom through an artificial system.