Knowledge Management Systems by Combining

Knowledge Management Systems

By combining the tacit and explicit knowledge within an organization and throughout its value chain, any business increases the potential it has for becoming a learning organization (Pun, Nathai-Balkissoon, 2011). The intent of this analysis is to evaluate how the three types of knowledge management systems contribute to enterprise becoming learning organizations. Enterprise-wide knowledge management systems, knowledge work systems and intelligent techniques are defined in this analysis from the standpoint of their contributions to creating learning organizations.

Analysis of Knowledge Management Systems and Learning Organizations

The first is the enterprise-wide knowledge management system that seeks to create taxonomies around structured and unstructured content, making the knowledge quickly applicable to specific business initiatives, processes and strategies (Ajmal, Koskinen, 2008). Enterprise-knowledge management systems are also specifically designed to support role-based access to the decision-maker level, continually striving to make the information in a company more relevant to the decision makers who need it (Pun, Nathai-Balkissoon, 2011). Knowledge work systems are designed to create a foundation or platform of knowledge which can over time be aligned to the specific needs of an organization over time

(Ajmal, Koskinen, 2008). The structure of the work knowledge management systems often align to the business models and information flows necessary to keep transactions accurately and continually flowing over time. A knowledge work system may also include a series of constraint models and rules engines to further streamline the access, analysis, classification and presentation of knowledge from repositories and platforms (Ajmal, Koskinen, 2008).

Intelligent techniques can also be used for managing the capture, translation and assignment to taxonomies of tacit and explicit knowledge over time (Ajmal, Koskinen, 2008). An example of intelligent techniques include the use of rules- and constraint-based engines to manage product and service configurations over websites and through guided selling online applications. Intelligent techniques are also commonly used for the defining of self-configuring taxonomies that can are stable enough to define information correlations yet flexible enough to support multiple roles throughout an organization (Pun, Nathai-Balkissoon, 2011).

Creating and Sustaining A Learning Organization