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Demand Driven

Knowledge is a by-product of interaction.

 

A few questions come up when we talk about knowledge management:

  • What knowledge should we capture?
  • What knowledge is important or valuable?
  • How do we validate the knowledge we have?  

 

The KCS methodology proposes that we let demand answer these questions. We want to respond to the issues that come up in the context of getting work done. We want to reuse what we know and capture the things that are new. We don’t want to imagine, anticipate, or fabricate issues that have not happened.  This idea is supported in the academic work that has been done in the area of autonomic systems and systems thinking. This work focuses on the idea that these systems are influenced by the events and actors in the system, and as a result they are self-optimizing.

 

While we may want to be proactive, we are generally not good at predicting the future in the absence of relevant, past experience.  Even with past experience our success rate is pretty small. If we agree that we can not predict future events, then the next best thing we can do is to be really good at sensing and responding.  The demand driven principle proposes that our focus should be driven by the needs (demand) of those we serve. Demand will tell us what knowledge to capture and what knowledge has value, and reuse of the knowledge we will validate that knowledge in the Solve Loop.  In the Evolve Loop, patterns of knowledge reuse can help develop predictive and preemptive capabilities that will improve the efficiency of the organization. The patterns of reuse can also help us identify high impact improvements to functionality, processes and policies that can produce dramatic business improvements based on the collective experience of the knowledge workers.

 

This is a very different way to think about organization and process.  We have talked a little bit about how most of our business practices have developed as a result of the last hundred years of managing manufacturing or production lines.  Historically, these have been deterministic systems. In a manufacturing model, we know the desired outcome (the product) and the resources and skills it will take to create that outcome. Manufacturing processes use predefined processes and resources in a command and control model to minimize variation and maximize output. Problem solving is a non-deterministic process. By its very nature, the outcome of resolving a new issue is not known, nor do we know the process or what resources will be required to resolve it. The processes and resources needed in a nondeterministic model are emergent; they are based on the context of the situation.  Our approach, and the resources required, change as we learn more about the situation. 

 

KCS is based on just-in-time action, not just-in-case action.  It is very difficult to predict the future value of an interaction.  We want to capture the experience (knowledge) and then let demand drive our attention to the knowledge that has value. This is part of the elegance or efficiency of the KCS model. We want to solve the issue at hand by reusing, improving, and if it doesn't exist, creating knowledge articles.  We do not spend time reviewing or improving knowledge that will never be reused.  The knowledge that is reused is validated, based on demand, through reuse: reuse is review.  The best people to validate knowledge are the people who use it every day. 

 

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Last modified
19:10, 13 May 2016

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