Knowledge, unlike physical things, operates on a principle of abundance. That is: the more we share the more we learn. This is one of the key differences between KCS and a knowledge engineering approach. KCS is a "many to many" approach, where knowledge engineering is a "few to many:" a few subject matter experts providing knowledge to the masses. The KCS concept here is that everyone who does knowledge work or who interacts with knowledge has something to contribute.
The best people to create and maintain the knowledge are those who use it every day.
Ideally, those who use knowledge will improve it based on their experience. Even the simple Solve Loop act of indicating use of a knowledge article is a critical element of KCS. Frequency of use is one indication of the value of an article. The Evolve Loop analysis of patterns of article reuse enables us to identify opportunities for improvements in the workflow, content standard, products, services, and/or policies.
If the knowledge base is used as the first point of reference for knowledge workers, then they will reuse, improve, and create knowledge as they are doing their work. This helps ensure the knowledge is described in the context of use, which is a key element in knowledge findability.
There is huge value in capturing the collective experience of all who interact with knowledge. If a group of people share their ideas and experiences through a conversation, inevitably they all leave with more information or knowledge than they had before the conversation took place. No one leaves with less. When we share ideas, we are not giving them up; we still have them. The process of explaining an idea to others often helps us gain clarity and understanding, and we can expand or strengthen an idea by understanding the perspective and experience of others. It logically follows that the more people we have sharing their experiences, the more complete and accurate the knowledge base will be.
If we can capture the collective experience of the community of knowledge workers, it will always be more complete and accurate than what any individual, even an expert, knows. KCS leverages the collective experience and acknowledges that all the people who interact with knowledge have something to contribute to that knowledge. The power of collective thinking and experience is supported by a number of independent research projects documented in the books The Wisdom of Crowds, Group Genius: The Creative Power of Collaboration, and The Medici Effect: What Elephants and Epidemics Can Teach Us About Innovation.
Capturing our collective experience means capturing our resolved issues as well as issues we are in the process of resolving (work in progress). Capturing and sharing what we know about an issue we are working on allows others who might encounter the same, or similar, issue to know that we are working on it. It also provides visibility of the issue to others who might be able to contribute or collaborate on resolving it.
The Internet provides us with immediate access to an amazing amount of information. It turns out not all of it is correct! However, most of us have figured out how to find the best information for our purpose by exercising judgment and triangulating - that is, we will look at a number of different answers from different sources and decide on what makes sense. Would any of us give up our access to the vast information available on the Internet for a single, much smaller, trusted source of information?
A good example of the power of the collective experience is the comparison of the now defunct Encarta (Microsoft's digital encyclopedia) to Wikipedia. Encarta used the traditional knowledge engineering approach of knowledge from a few for the use of many. Wikipedia, on the other hand, was a lot of people contributing knowledge for the use of many. This model is sometimes called crowd sourcing: it is a many to many model. The amount of knowledge contributed by many people, in many different languages, to Wikipedia is staggering. But, is Wikipedia accurate; can the content be trusted? A number of studies over the past years have shown the error rates, normalized to volume, as well as bias levels in Wikipedia to be comparable to the error rates and bias levels in the Encyclopædia Britannica. Turns out encyclopedias have mistakes as well. The difference is that the time to correct an expert-written encyclopedia is dramatically longer than the crowd-sourced Wikipedia. If the people using knowledge care, they will fix things very quickly in the Wikipedia model.