There is a lot of knowledge in an organization, some of it easy to codify (capture), and much (most) of it difficult to do so. Understanding how best to commit resources for knowledge-sharing should be in some kind of a decision-making framework that is easy for anyone to understand. This is a first attempt to do that.
[This post is a follow-up from my building institutional memory post].
Brian Gongol made an interesting observation on three categories of institutional memory. Decision memories are probably the most important, and likely the most open to rationalization in hindsight. The good decisions always seem obvious after the fact.
event memories, which are things like the construction of new facilities or the arrival of new employees
process memories, which note how things are done in order to save time and ensure their reliable repetition in the future
decision memories, which explain how the institution chose one path or policy or course of action over another
We can expand these three categories with Ewen La Borgne’s observation on the types of artifacts left by work projects. Outputs are quite explicit, while expertise is mostly implicit knowledge. Networks can be mapped, and are therefore explicit, but interpreting them requires implicit knowledge.
Information and outputs produced
Expertise (knowledge and know-how)
A network of connections
Put all of these together in order of difficulty in codifying memories/artifacts and the following graphic is my working interpretation. Explicit knowledge is easier to codify and more suitable for enterprise-wide initiatives, while implicit knowledge requires personal interpretation and engagement to make sense of it. Note that these six categories only serve as examples and are not a complete spectrum of knowledge representations.
So what types of knowledge management (KM) frameworks could help us support the codification of these knowledge artifacts? One way to look at it would be from a perspective discussed by Patti Anklam a few years back. Patti explained the differences between Big KM, Little KM and Personal KM and this distinction could be useful. Big KM is good for knowledge that can be easily codified, and Little KM can provide a structure for teams & groups to try out new things (in a Probe-Sense-Respond way). PKM puts individuals in control of their sense-making, but the organization can benefit from this by making it easier for workers to share knowledge.
Finally, there are certain types of tools and and platforms that would be more suitable for sharing of each type of knowledge artifact. I describe only a few in this image, but it gives an idea of how one could structure a full spectrum of knowledge-sharing in order to support institutional memory.
From here, one can now ask what types of platforms would help to codify and share the knowledge that is important to any organization. For larger organizations, all three types of KM are most likely necessary. Too often, Big KM is seen as sufficient, but in complex work environments, Little KM and Personal KM are also needed and should work in conjunction with Big KM. These are three important pieces, that should remain loosely joined in order for each to do what it does best.