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April 29, 2006

KM, and Measuring the Value of Change

We normally perceive change in terms of "good" change or "bad" change. That is, usually, the value of change seems readily measurable. But why is this? The answer is, because we have a preference before we notice the change, which gives us a reason to notice it.

The usual approach is to first define "value" -- namely, the difference that we want the future state of affairs to have from the current state -- and then to go about measuring the progress that action and time make towards achieving the desired difference. Here, the issue is that although actions and time are both highly various, we usually decide (prescriptively) which actions and timing we will watch.

Because that approach is such a routine for managers, it is all the more baffling to go about it the opposite and less frequent way -- defining the changes that action and time make, and afterwards figuring out what is the meaning of the changes to the current state of affairs. That is, in what sense are the changes recognizable, post facto, as "progress"? What, exactly, is progressing, and do we want it to?

Said that way, we might conclude that, as in the former approach, the majority of management's attention to the phenomenon of change is "regular" and pragmatic, concerned with oversight and with assigning old or current meaning to new observations.

Yet that passes up the home territory of insight -- or, as in the less frequent latter approach, assigning new meaning to old or current observations.

Put more precisely, the distinction is one of being practical versus theoretical. For most business concerns, the problem with theoretical value is that it's either too slow or too speculative to realize -- which makes it expensive to sustain.

Case in point: the influence of knowledge in the organization is often seen as stealthy or unmanaged, provoking changes of unknown value. But harnessing it for managed value frequently appears to lean towards the expensively hypothetical.

This is where many KM efforts get stranded today. Yet meanwhile, that makes it similar to the issue of R&D. In R&D, there is normally some investment justified by the expectation that significant future value will come from new products -- or said more forcefully, justified by the belief that innovation in production is necessary.

Justifications specifically call out the connection between motivation and measurement -- which indicates the importance of thinking about why to measure -- not just what to measure.

Given the example of R&D, the question in most organizations is whether innovation can also be culturally accommodated as a normal and important phase of the ongoing "production lifecycle" of adopted operational knowledge.

The way most organizations recognize their adopted operational knowledge is as "competency". Thereafter, the way they measure the value associated with changes in competency is by measuring the before-and-after difference observed in what looks like a capability: predictably achieving progress through selected modes of action in given situations. At least casually, everyone sees that as "performance", and thinks about performance improvement.

Of course, one of the practical approaches to incorporating changes of competency is simply to buy the already proved "better" competency -- as in hiring "experience". The catch here is not in the difficulty of finding such talent, but instead in correctly defining what it is about the talent that demonstrates the greater likelihood of providing greater predictability of progress. Ordinarily we look for that demonstration in proof provided by prior known situations, or "performance histories"...

But as we know, all workers must adjust to the complexity of the organizational environment, and those who might have seemed to be the most experienced people coming in do not always prove to be the most "effective" people later. For example, struggles with the current work environment (context!) can strongly frustrate the promise of previous experience.

In fact, the most distinguishing problem to solve through knowledge management -- the most challenging "given situation" to address with competency -- is to enable us to know what to do about what we don't know. The main example of this is an ability to solve new problems brought on by dynamics that currently lie outside of our control. (Politics, competition, economics, or whatever.)

Operationally, we can think of the level of that ability as a demonstration of "intellectual productivity"... the degree to which knowledge inputs generate situational effectiveness. But explaining that productivity puts a premium on recognizing improvements in problem-solving capability, instead of on the historical outcomes of solution efforts. Outcomes may indicate improvement, but managerially, the emphasis must be on the mechanism that produces the outcome.

Articulating the features of "successful problem solving" gives a list of (mainly behavioral) characteristics. Those characteristics are really the desired direct results (i.e., differences achieved) of applying knowledge management as an approach to changing operations.

Bluntly summarized, the main goal of knowledge management is to change how things are done, not to change what the doing delivers. The deliverables may or may not subsequently change because the production changed -- but the primary importance is in transforming the production itself in "necessary" ways, so that deliverables can change.

That gets us back to the motivational aspect: the challenge is to define what it is about the production that is necessary to change and why. If successfully articulated and agreed, that particular notion of "necessity" puts pragmatism into the overall KM effort -- easing justification of sustaining investment in the effort.

In re-engineering operations with KM, two broad categories of response to the necessities are: the type of knowledge to develop and incorporate; and, the mode of incorporating the knowledge in the real-time of operations. Aligning things across the two categories can happen as a matter of determining a best practice (process optimization), or as pursuit of an innovation (R&D), but the point is to ensure that this alignment actually occurs. The difference between the current degree of alignment and the target degree is the value of change to be observed.

Posted by Malcolm Ryder at April 29, 2006 6:26 AM

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