When developing knowledge management systems (KMS) there exist the need to deliver the right knowledge to the right people at the right time and in the right context. Easier said than done… Right? Well, if you incorporate a sound information architecture (IA) in your design and implementation of your KMS, this will be exactly what you need to correctly facilitate the flow of knowledge.
IA connects people to their content (information & knowledge) that includes the high level rules that govern the manner in which information concepts are defined, related, realized and managed by the enterprise. The major IA components are the Content Model, Metadata Schema and Taxonomy.
A Content Model provides the framework for organizing your content so that it can be delivered and reused in a variety of innovative ways.
A Taxonomy is a hierarchical classification or framework for information retrieval. Taxonomies represent an agreed vocabulary of topics arranged around a particular theme. A taxonomy can have either a hierarchical or non-hierarchical structure. However, typically taxonomies are presented in a hierarchical fashion
Metadata is an important aspect of the IA and in particular the Content Model. Metadata is primarily used for labeling, tagging or cataloging information or structuring descriptive records. Metadata (fields and attributes) are assigned to a content type to provide a means to describe it and provide the means in which to find content once it becomes part of a KMS.
The marriage between IA and KM is really a one sided affair. A KMS needs IA to be effective, but IA is not dependent on KM. When it comes to effectively labeling, structuring and categorizing explicit knowledge types within an KMS having the right information architecture is essential. The purpose of IA for a KMS is to ensure that the right people have access to the right knowledge at the right time… and in the right context.
The knowledge we are speaking about can be either explicit and/or tacit. Explicit knowledge is formalized, codified and easily expressed in words and symbols. This is often called the know-what. Explicit knowledge can take the form of, but not limited to lessons learned, knowledge articles, FAQs, Standard Operating Procedures (SOPs), Job-Aids, and Best Practices. Explicit knowledge can be represented by any tangible asset that conveys how to do something or describes how a decision was made about something. Tacit knowledge is knowledge found within the minds of the practitioner. This can consist of intuitive know-how, experience, learnings, and practices. This knowledge is difficult to capture and is usually passed on to others through mentoring, storytelling, and other socialization methods.
A Knowledge Management System usually involves just explicit knowledge. IA does a great job in providing the infrastructure in order to capture, catalog, store, retrieve, and find explicit knowledge. However, IA plays a significant role in bringing in tacit knowledge sources within the KMS. This is done primarily through the use of expertise locators and social communities. Expertise Locators leverage IA to provide the infrastructure (metadata schema) to capture attributes about the experts with the organization and associate the expert to social communities and explicit knowledge sources within the KMS. The attributes to describe the experts can include such things as areas of expertise, educational background, projects worked on, years of experience, and research material published. Essentially anything that will help describe the expert in order for a user of the KMS to understand what the expert may know and who can contribute to making a decision or has the ability to solve a problem.
When it comes to knowledge management, IA is essential to the adoption and success of any knowledge management system implementation.