7. Knowledge management: a cross-disciplinary domain

7. Knowledge management: a cross-disciplinary domain[14]

Knowledge management draws from a wide range of disciplines and technologies.

• Cognitive science. Insights from how we learn and know will certainly improve tools and techniques for gathering and transferring knowledge.
• Expert systems, artificial intelligence (AI) and knowledge base management systems (KBMS). AI and related technologies have acquired an undeserved reputation of having failed to meet their own — and the marketplace’s — high expectations. In fact, these technologies continue to be applied widely, and the lessons practitioners have learned are directly applicable to knowledge management.
• Computer-supported collaborative work (groupware). In Europe, knowledge management is almost synonymous with groupware … and therefore with Lotus Notes. Sharing and collaboration are clearly vital to organizational knowledge management — with or without supporting technology.
• Library and information science. We take it for granted that card catalogs in libraries will help us find the right book when we need it. The body of research and practice in classification and knowledge organization that makes libraries work will be even more vital as we are inundated by information in business. Tools for thesaurus construction and controlled vocabularies are already helping us manage knowledge.
• Technical writing. Also under-appreciated — even sneered at — as a professional activity, technical writing (often referred to by its practitioners as technical communication) forms a body of theory and practice that is directly relevant to effective representation and transfer of knowledge.
• Document management. Originally concerned primarily with managing the accessibility of images, document management has moved on to making content accessible and re-usable at the component level. Early recognition of the need to associate "metainformation" with each document object prefigures document management technology’s growing role in knowledge management activities.
• Decision support systems. According to Daniel J. Power, "Researchers working on Decision Support Systems have brought together insights from the fields of cognitive sciences, management sciences, computer sciences, operations research, and systems engineering in order to produce both computerized artifacts for helping knowledge workers in their performance of cognitive tasks, and to integrate such artifacts within the decision-making processes of modern organizations." That already sounds a lot like knowledge management, but in practice the emphasis has been on quantitative analysis rather than qualitative analysis, and on tools for managers rather than everyone in the organization.
• Semantic networks. Semantic networks are formed from ideas and typed relationships among them — sort of "hypertext without the content," but with far more systematic structure according to meaning. Often applied in such arcane tasks as textual analysis, semantic nets are now in use in mainstream professional applications, including medicine, to represent domain knowledge in an explicit way that can be shared.
• Relational and object databases. Although relational databases are currently used primarily as tools for managing "structured" data — and object-oriented databases are considered more appropriate for "unstructured" content.
• Simulation. Knowledge Management expert Karl-Erik Sveiby suggests "simulation" as a component technology of knowledge management, referring to "computer simulations, manual simulations as well as role plays and micro arenas for testing out skills.