Knowledge Management

The Big Guide to Knowledge Management

IT terminology can be misleading. Is knowledge management (KM) the same thing as information management, intellectual capital, or data and document management? It’s not, says Mike Simpson of CIH Solutions, but it does contain those categories. In an article for The ITSM Review, Simpson covers the basics of knowledge management for the ITSM outfit.

KM Truths

When does data become knowledge? Answer: when static documentation gets mixed with the information retained by personnel. And make no mistake, says Simpson, the value of information ought to take precedence over its capture and retention.

The Impetus for KM Systems

One approach to building a knowledge management system involves a central repository maintained by a few, well-disciplined staff, but available for access by all. On the surface, this approach seems self-evident, but there’s a deeper impetus behind its design:

…the background problem…was one of long-term ill-discipline in the day-to-day management of key information. Individuals, both staff and sub-contractors, would create multiple documents, held in isolated repositories or held on local drives, resulting in poor retrieval and inaccurate information…The problem is a familiar one.

The type of information for which knowledge management should be essential is what Simpson calls “High business value.” High business value information includes “all vital and irreplaceable business records, documents, information and data that are associated with sensitive areas like customer data, compliance, security, personnel, finance, and legal and commercial activities.” When it comes to incurring costs and risks associated with information, this category is the most vulnerable and is most associated with problems due to breached security, inaccuracy, and slow retrieval.

A Brief KM Framework

The ideal KM framework consists of two levels and five stages in its life cycle. Layer 1 is structured for easy access, with a hierarchy and a controlled vocabulary of tags. Layer 2 consists of a linear, thesaurus-like structure for “search and discover.” The five stages are briefly outlined as audit, map, classify, assemble, and integrate.

The purpose of the audit is to categorize all relevant information before that information is identified and plugged into a customized database. Some information will transfer to the KM database, while the rest will stay in local repositories. Classification consists of creating the hierarchical, controlled vocabulary of Layer 1. Assembly involves descriptive “metadata tables” consisting of information titles, document numbers, and so forth. These metadata tables are then used to manage the population of the KM database.

For more details about building a KM database system, visit:

Eric Anderson

Eric Anderson is a staff writer for CAI's Accelerating IT Success. He is an intern at Computer Aid Inc., pursuing his master's degree in communications at Penn State University.

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