One of the by-products of the KCS Practices is improved success with search. If we are only finding relevant articles when we search, we don't have to worry about how big the knowledge base is or if it contains "old stuff." If it is not relevant, old stuff should not show up in our search results. In fact, we could make the case that on those rare occasions when we need the old stuff, the value of the seldom-used, old articles is higher than the set of frequently used articles, since the knowledge about the frequently used articles also exists in the knowledge workers' heads. Imagine a situation arises about an old issue, requiring knowledge that people have long forgotten or those who knew it have left the organization. Having access to the older, seldom-referenced knowledge can be of tremendous value. But only if it shows up when it is relevant.
However, findability is a common problem as organizations grow their knowledge. Archiving old articles treats the symptoms of findability, not the cause. Relevance is the key. Relevant search results are enabled by a combination of: context, structure, rich environment statements, and search technology. KCS addresses the first three - the content factors- but it does not address search technology. While search technology can help, it can not overcome deficiencies in our content. If we are having findability problems, the first place to look for opportunities to improve our search success is to review our context, structure, and the richness of our environment statements. More information about the role technology plays in KCS is covered in the Process Integration section.
Some have tried to improve relevance by reducing the number of KCS articles in the knowledge base. This reduction will compromise the completeness of the knowledge. The greatest value from the knowledge base comes from it being a complete collection of the organization's experience and our ability to quickly find what we need when we need it.
This is not to say that knowledge base cleanup and maintenance should never be done. There is definitely a need for ongoing knowledge base maintenance, but it should be done in a way that improves the findability of what we collectively know, not by reducing what we collectively know. Maintaining a knowledge base is like tending to a garden: it requires constant weeding. We have to be sure we can distinguish the weeds from the flowering plants, some of which may only occasionally produce beautiful flowers. The "reuse is review" and the "flag it or fix it" Solve Loop activities play an important role keeping our knowledge up to date as we interact with the knowledge base. We have to compliment that with a knowledge base maintenance strategy that looks at the collection knowledge in a given domain. This is an important part of the Knowledge Domain Analysis process and is typically done by the Knowledge Domain Experts (KDEs).