Optimal Knowledge Management


12 February 2007

Interest in productivity and how to manage innovation and know-how appears to be growing. Our article on employee productivity gets about four times more hits than the next most popular post. I imagine that more and more people in the West are waking up to the fact that innovation and high productivity is the only thing that keeps jobs here (though the link from USA Today probably didn’t hurt).

Snippet from USA Today article

USA Today blog post mentioning us

A recent paper from Harvard considers the optimal implementation of knowledge management. With a slight reformatting for clarity:

We derive three main results [about the optimal management of know-how].

  1. First, information about successes is typically more useful than information about failures, since successful methods can be replicated while failures can only be avoided. This supports firms’ focus on ‘best practice’.

  2. Second, recording mediocre know-how can actually be counter-productive, since such mediocre know-how may inefficiently reduce employees’ incentives to experiment. This is a strong-form competency trap.

  3. Third, the firms that gain most from a formal knowledge system are also the ones that should be most selective when encoding information (i.e., the ones that are most at risk from the competency trap); namely, large firms that repeatedly face problems about which there is little general knowledge and that have high turnover among their employees.

Beyond these main principles, we also show that it may be optimal to disseminate know-how on a plant-level but not on a firm-level, and that storing back-up solutions is most valuable at medium levels of environmental change.

I am not sure I am comfortable with the methodology (I prefer practical results and observed measurements to theoretical mathematics, and in any case they could get to the core of the conclusion with much less differential and integral calculus), but their conclusions broadly rings true. Does anybod have any experimental data in this area?

However, on the third point in particular notice the emphasis on formal knowledge systems. I am thinking that a more comprehensive list along their main dimensions would look something like this:

A model for selecting optimal knowledge systems based on employee turnover and the degree to which there exists general knowledge about the type of problems you are trying to solve.

For what we call unknown problems (problems about which there is little general knowledge) the main focus is on collaboration: working together to define solutions to the organization’s problems. For companies that are mainly faced with known problems the issue is one of sharing this information effectively.

On the other dimension, with low turnover you benefit from an unstructured approach. The task is mainly a social one of finding the right people with the right expertise or attitude. An emerging, bottom-up approach is entirely appropriate. But in the high-turnover situation you need a way of structuring th ework to ensure that your relatively inexperienced workforce are addressing the right problems. You need to impose more of a top-down structure for the collaboration or sharing.

In terms of technologies, sharing can be achieved with document management systems while collaboration is more suitable for a wiki. The social element comes from some way of expressing oneself (e.g. blogs) and a way of connecting people (RSS aggregators and lists of experts).