Posted by: zyxo | January 21, 2008

Aspects of knowledge management (part 0 : my start)

Since I began to search about knowledge management some months ago it became clear that first there is al lot more about it than just a document database and second that no single source covers it all.

I want to make a sort of complete powerpoint presentation on the subject, so I first have to find some order in the mess of subjects that have to do with … yes , the subject.

A mess ? Yes indeed. What follows is that heap of subjects I found. Until now I did not bother to sort them out. That what’s coming in the upcoming parts.

If you are curious, search a bit in the heap.

All comments, suggestions and other positive feedback is extremely welcome.

The heapAn information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information rather than for him not to have it
service oriënted architecture and KM
share ideas, reward the accepted ideas, prediction market on the ideas
idem for knowledge bits, wiki pages
all the different ways of learning (classical, internet search, mentor, colleague’s…)
a “who to ask” database
Quality of knowledge versus availability. Availability first !!
Culture/ cash /
Do’n t impose a too rigid structure
Gap analysis to know where knowledge is missing or not accessible
will sharing bring me more money than keeping it ?
Do you sell it or do you give it away ?
put “sharing” (yearly) objectives/evaluations
“Figures => data=>info=>knowledge=>wisdom=>sharing
Each colleague studies one topic a year (a part from his normal work) and shares the info with the others (presentation)
Anyone can suggest ideas for the above
KQM needed: knowledge Quality manager to make sure that the kwowledge base does not become a garbage can (eg. titles as “info” and the like prevent info in twofold …)
“Once upon a time, saying the words ?knowledge management? was the fastest way to get thrown out of a meeting. And not only would you be ejected, you would never be invited back.”
Cynefin :

  • simple domain

Few cause-effect relationships, simply connected, so that cause and effect connections are predictable and repeatable. Problems that are solved tend to stay solved, at least in the medium-term. Bureaucratic structures work as a way to organise action, because best practice can be built into procedures. The learning sequence is sense-categorise-respond.

  • Complicated domain

Multiple causes and effects, where cause-effect relationships can’t be immediately recognised, but can be understood through research. Professional structures handle this domain best, using communities of practice to evolve and confirm best practice. The preferred learning sequence is sense-analyse-respond.

  • Complex domain

Multiple causes and effects with complex feedback loops. Causes are only perceivable after the end, and work as explanations rather than predictions. Problems keep returning despite many attempts to resolve them, and solutions imported from elsewhere don’t work. A lot can be learned from mistakes. Collegial structures work best, and the learning sequence is probe-sense-respond.

  • Chaotic domain

Multiple and turbulent cause-effect connections, so turbulent that there is no point in thinking about cause and effect. There is no point in trying to develop solutions: it’s best to act quickly to create stability. Any structure is of little help: just act-sense-respond.

“strongest correlation in betting was found among people who sat very close to one another, trumping even friendship or other close social ties…. This is tangible evidence, the authors argue, that information is shared most easily and effectively among office neighbors, even at an Internet company where instant messaging and e-mail are generally preferred to face-to-face discussion.”

So much for working virtually or formal knowledge management. Maybe it does all come down to proximity! Or perhaps workers waste time in idle chit chat with neighbors. Take away the neighbors and then they have to communicate with actual co-workers. Of course, if you can group workers in close physical proximity, that indeed has value. But given global teams, that often is just not possible.

Knowledge ~data model
Management ~process model
A complete analysis is necessary of all the processes that generate info and also need info
Data smog

So far the heap


  1. The KM field is a mess, and the empirical research in KM is rarely conducted properly. KM esearchers are confusing dependent variables, etc….

  2. Øvind,
    It seems definitely that way, but, the more I look at it, the more I think I can make something of it. I certainly will try.

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