A primary goal of the Knowledge Domain Analysis is to learn from the collection of knowledge articles created and used in the Solve Loop. This analysis contributes to organizational learning and promotes continuous improvement of the KCS system on many levels. In order to make sense of the thousands of articles that are typically created in the Solve Loop, it is helpful to consider the content in subsets or collections of related articles. These subsets of the knowledge base are known as knowledge domains.
Knowledge domains are virtual collections of related articles. In a Human Resources organization, for example, a domain would be the collection of articles about a topic or benefit, like vacation policies. In a technical support organization, a domain may be the collection of articles about a product family or a technology or group of technologies. Knowledge domains are seldom about one product. They are not precise or absolute in their boundaries; knowledge domains often overlap. A knowledge domain is the collection of articles that makes sense to review to identify patterns and clusters. Therefore, the purpose or intent of the analysis defines the collection of articles that is relevant.
For example, if we analyze article reuse patterns (Pareto analysis) to identify pervasive issues in a product (which would then be candidates for root cause analysis and corrective action), the collection of articles that relate to the product is the knowledge domain. If we want to provide an account team with a profile of a customer’s experience over the past year, the collection of articles linked to a specific customer’s closed incidents is the relevant knowledge domain.