All Life Is Problem Solving

In my last blog, I filled in some of my thinking about the evaluation function of the KAO, by presenting four difficulties associated with KM impact evaluation that would figure prominently in KAO operations. The four difficulties vary in importance depending on the approach to KM used in KM programs and projects. In this blog I’ll specify the three approaches and begin to discuss their relations to the four difficulties and the relevance of the combination of approaches and difficulties to the organization of the KAO. The three approaches are: the Decision Interruption Approach; the Expectations Gap Approach; and the Ecological Approach.

The Decision Interruption Approach designs and implements KM interventions introducing strategies, policies, programs, techniques, and tools that enhance knowledge processing by creating systems for interrupting ongoing decisions people make in order to integrate further information or knowledge into the decision process. In the paradigmatic Partners HealthCare case, a system was developed to examine Doctors’ prescription orders after they were entered into the system, but before they were implemented, to match the orders against a knowledge base developed by a committee of experts recording the track of previous experiences with patients having symptoms similar to those being treated by Doctors entering the new orders.

Where the knowledge base didn’t match the orders, they were stopped, and alerts, including the information in the knowledge base about the mismatch between the order and experience and orders used in previous similar cases, were transmitted to the Doctors involved, thus triggering further consideration by them. Doctors could either agree with the knowledge base recommendation, stick with their order, or enter a revised order with a completely new alternative, but if they chose not to accept the recommendation of the knowledge base, they were required to provide their reasons for disagreement and for preferring their own prescription order.

What a system of this type does is to interrupt a decision, force the decision maker to face a question about its correctness, and the existence of a potential problem, consider whether a problem exists, and if there is one, think it through, solve it, and add to the organization’s knowledge base while doing so. Later on, the committees having primary responsibility for the knowledge base have to consider the new knowledge claims added to it and come to terms with them by evaluating them further, taking into account information about how well the Doctors’ decisions worked.

This multi-level organizational process increases the number and frequency of knowledge life cycles, enhances distributed problem solving, creates learning feedback across organizational levels, and grows knowledge more rapidly. Such applications can be developed to upgrade decisions in many areas of decision making, and where this is done, the result is the decision interruption approach to Knowledge Management, which I’ve written about at greater length here.

The Expectations Gap Approach designs and implements KM interventions introducing strategies, policies, programs, techniques, and tools that enhance knowledge processing by creating systems for better evaluating expectation gaps and responding to them by solving the problems that underlie them and by sharing those solutions with those who need them. Some paradigmatic cases of this approach include Toyota, Navy Reactors, and Alcoa, all cases discussed in Steven Spear’s Chasing the Rabbit. This approach is the very comprehensive one of introducing social and cultural changes in an organizational system implementing four capabilities: 1) the capability to always compare specific expectations with actual outcomes in an effort to seek out problems that must be solved; 2) the capability to “swarm” and solve problems quickly, and to solve them in such a way that a) the “root causes” of problems are understood and taken account of in the solutions; and b) that those solutions are designed in such a way that clear expectations are set for maintaining capability 1); 3) the capability to share the solutions produced by capability 2), and 4) the capability to enhance the expectations gap approach itself.

Steven Spear’s book, also discussed here, here, and here, contains plenty of concrete illustrations of what a system providing these capabilities is like. In addition, my series with Steve Cavaleri on the Problem Solving Pattern contains many Toyota illustrations.

The Ecological Approach designs and implements KM interventions introducing strategies, policies, programs, techniques, and tools that enhance knowledge processing by creating systems that provide generalized support for seeing problems, creating and/or sharing knowledge. This approach is the most widely used in Knowledge Management. In the early days of KM it focused on Best Practices Systems. As an interest in the people side of KM developed it emphasized Communities of Practice programs and projects, supplemented with Information Technology support for collaboration. Contemporaneously, new IT approaches appeared focused around Enterprise Portal and Content Management implementations. Still more recently, a new revival of KM is being fueled by interest in social computing, social media, social networking, and so-called KM 2.0 collaboration applications supported by narrative approaches.

The emphasis in using the Ecological Approach has been much more on “knowledge sharing,” than on knowledge creation, but a knowledge creation emphasis is to be found in many projects including those emphasizing the Nonaka SECI model of knowledge processing. Two paradigmatic cases of this approach to KM are the World Bank and Halliburton cases, which I’ve discussed here. Put briefly, the World Bank case evaluated its own effort as failing to produce any visible impact on the day-to-day loan operations of the Bank, despite its expenditure of $280 million. On the other hand, the Halliburton case, which tempered an ecological community/portal-based approach with a venture capital model that focused KM interventions on specific problem areas in operations claimed a record of positive ROI in every one of its first 19 projects. Having discussed both the difficulties in evaluating KM activities and different approaches to KM, in my next blog in this series, I will consider the implications of the approaches combined with the difficulties for the proper organization of the KAO’s evaluation function.

To Be Continued