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Tapping into the knowledge inherent in a group of people within an organization seems to be the current “holy grail” of knowledge management. But what exactly does that mean, anyway?

Before I began my research into the subject, to my admittedly narrow view the term “knowledge management” meant categorizing knowledge areas, then identifying appropriate experts in each of those categories. Thus, when a situation arises beyond my capability, I always know whom to call upon as the expert in that area. But that’s not all by a long shot.

Jeffrey Phillips’ blog entry Centralize Data, Decentralize Knowledge suggests there are two critical paradigms to consider in the execution of knowledge management.

On data, he says:

From a documents and data standpoint, it makes sense to centralize this stuff… once a document is created or data is generated, it rarely changes and if it does there’s probably a record of the change. Many people have greater access to data that is centrally stored and managed. Additionally, data in these forms usually has “meta-data” associated with it that helps individuals search and find the information. That makes the information more accessible and more useful.

Management of knowledge, he says, is much more difficult:

Knowledge is situational and specific to certain contexts and events, and changes frequently. Rather than attempt to consolidate knowledge, we should attempt to keep knowledge decentralized but understand “where” it is and “who” has it. To a certain extent we are trying to create a “hub and spoke” system for managing corporate information assets. Hard, physical data and documents are stored at the center and constantly consolidated and aggregated. Knowledge provides the spokes to connect the data and how to interpret it with the users of the information.

Now it seems to me from a strictly formal point of view, this is essentially correct. But what is being described is a system of “knowledge capture” that encourages “knowledge sharing” and it’s important to realize the two are very different animals. Knowledge sharing is dependent upon networking to function – the more connections, the more knowledge is shared. Social networking has demonstrated this quite well (see the picture above.)

The hub-and-spoke model he alludes to means the flow of information depends on the knowledge expert (the spoke) knowing exactly where the knowledge is and how to get it. But there is always some lag there. What if the expert is on vacation, or just away from their desk? To whom would you turn?

What if you could tap into the knowledge of entire organization? Imagine this scenario:

Project Manager Joe has a small project that entails replacing an obsolete piece of equipment for a client. The equipment is critical to the function of the plant, and there are no viable alternative technologies to use, so the only solution is to build an exact replacement. This requires special materials which are not commonly available. When a search is begun to find vendors, no one he knows can help, so the circle of inquiry gets wider and wider, until finally he hits upon someone who had a similar need previously and could direct him to an appropriate vendor. Start to finish on this one question alone: three months!

What if there were a system whereby Joe could post his question to a central site viewable by everyone in the company? (Nearly 50% of the company is in the engineering department, so Joe has effectively asked a huge number of people at once.) Because everyone in the company regularly scan the postings, very quickly that same employee above answers Joe and directs him to the needed source of material. Elapsed time: 4 hours. The additional benefit: suddenly everyone else knows the same bit of information!

Say, wait a minute, there is such a system - it’s called a blog and it’s available now! Now all that has to be done is find a way to link this to the knowledge base. Oh, wait, we can do that too – with tagging, categorization, and linking.

So what’s the holdup?

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