Document Engineering - A Preferred Partner Discipline in Knowledge Management

After 20 years of investigation and application of Knowledge Management (KM) there are still various views and expectations on it resulting from its trans-disciplinary character. It is a kind of meta-discipline with a lot of partner disciplines, e.g. personnel development, organization, process and quality management, information management, document engineering and communication. The reason is the complex character of knowledge itself, which is defined in KM as the capability for effective action. A major dimension of this capability is naturally the content dimension, i.e. which knowledge area or object-activity-domain is it about, e.g. "document engineering". In any knowledge area the knowledge has three types of carriers: individuals with their experiences, education and inherent capabilities, groups like teams and communities with their compound capabilities based on joint understanding and networked complementary capabilities and finally information, carrying more or less codified and documented knowledge. Across all three knowledge carriers three questions or dimensions of knowledge quality are interesting in any knowledge area, which is important, e.g. for a business: How deep or profound is it, e.g. the level of expertise of a subject matter expert or a best practice description? How much is it distributed and inter-connected, e.g. which experts, groups and documents are involved and how? How is it codified and documented, e.g. the quality of defining, structuring and documenting the content? This is the starting point for KM: it provides adequate processes or instruments to improve or adjust the knowledge quality to the needs, e.g. of a business. But there are already the various partner disciplines of KM active to support, e.g. learning and training, inter-connection by collaboration, information formalizing and distribution - why do we still need KM? The partner disciplines may have profound capabilities in their fields, but they are driving a kind of one-dimensional KM. The full power of KM is to combine their solutions to more powerful multi-dimensional approaches. Examples will be discussed for major KM processes to improve knowledge quality: Locate knowledge and learn: These KM processes are useful, when knowledge is missing and it is looked for in the own organization or somewhere in the world to re-create and further develop the capability for effective action, where it is needed (a pull process). Typically one is looking for a specific expert, team, community or document and learns from it. Figure out knowledge and transfer: These KM processes are useful, when valuable knowledge is locally available in an expert, group or information and it should be transferred to another place or knowledge carrier (a push process). A typical example is the debriefing of a team after finishing a milestone or the debriefing of a leaving expert and transferring the knowledge to the successor(s). Network and collaborate in a knowledge area: These KM processes, if implemented well, represent a kind of high-level systemic KM, where the pull and push processes, see above, are running in parallel as needed. A typical example: A knowledge worker engages himself in a community of practice (for a specific knowledge area, e.g. with internal members and/or business partners). He/she responses to questions of members, posts own requests and collaborates on jointly important issues. This can be done face-to-face and/or virtually, e.g. on an adequate software platform. The value added by the meta-discipline KM is therefore essentially to evaluate, involve and integrate contributions of the partner disciplines and provide models and processes for "orchestrated" solutions across all three types of knowledge carriers: individual, organization and information. From the KM perspective there are various requirements and expectations on partner disciplines like document engineering, e.g. to support knowledge codification, search and navigation, learning and knowledge evaluation and bundling as well as information abstraction. Examples will be discussed.