Knowledge Management Challenges and Solutions to Organizational

Knowledge Management

Challenges and Solutions to Organizational Knowledge Management in KBMS

The evolution of Knowledge-Based Management System (KBMS) from being limited to only a selected number of taxonomies or approaches to supporting a greater number and versatility of roles continues to expand the versatility of these systems (Faucher, Everett, Lawson, 2008). The transition of defining data elements as part of a SQL-based data structure to being integral to knowledge-based and role-based knowledge models is a current trend (Bellinger, Castro, Mill, 2010). The challenge going forward is to streamline the transformation of data and information into knowledge that can be contextually captured and used in a wider variety of scenarios than is possible given the constraints of these systems today (Grant, Grant, 2008)

Analysis of the Current KBMS Platforms and Architectures

The continual challenges of categorizing explicit and tacit knowledge into a consistent data structure that can support user-driven taxonomies is the current state of development on KBMS systems (Bellinger, Castro, Mill, 2010). Tacit knowledge resists the defining of parameters and is the most difficult to capture, (Grant, Grant, 2008). Explicit knowledge has been shown to be easier to capture, yet often lacks context from a strategic standpoint (Faucher, Everett, Lawson, 2008). Reconciling each of these sources of knowledge while enabling technological platforms that can scale to meet role-based needs is forming a new area of KBMS system development and use. No longer concentrating on how to use capture explicit and tacit knowledge the current generate of KBMS systems looks to define constraint-based modeling approaches to determine the optimal path through data (Bellinger, Castro, Mill, 2010). In addition, the use of rules-based modeling techniques that allow for taxonomies to be created literally by role and by histories of request types shows significant potential over the long-term as well (Faucher, Everett, Lawson, 2008). Rules- and constraint-based technologies are being used to intermediate differences between knowledge that has been gained through explicit vs. tacit approaches as well. In addition to these approaches, the use of role-based modeling and development has also streamlined the development of KBMS systems. An additional development is the creation of specialized KBMS architectures and platforms specifically for Knowledge Intensive Firms (KIFs) which are often the early adopters of more specialized taxonomies (Grant, Grant, 2008). KIF needs and requirements are significantly different than the mainstream KBMS platform users, and therefore require a significantly different series of Application Programmer Interfaces (APIs) to streamline their operations (Grant, Grant, 2008).

KBMS and Change Management

One of the most critical tasks these APIs perform across all users of KBMS systems is the fine-tuning of user interface design and field designation to the data attribute level (Bellinger, Castro, Mill, 2010). This ensures that the users of these systems can continue working along the processes they have already established. They don't need to change how they work to use the KBMS, which turns into a major advantage over the long-term for their adoption.