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February 23, 2006

KM for productivity - driving Decisions versus Recommendations

Does knowledge improve performance?

The default answer should be "Yes". But this makes sense only if certain other things are true.

The fast explanation is that knowledge improves the decision-making that shapes activity -- and with higher-quality activity the intended outputs should be more influential, as planned, on the conditions prerequisite to desired outcomes.

But let's drill deeper. If the shape of the activity is what powers its ability to produce influential output, we seek certain outputs that have explicitly demonstrated their powers to either support or cause the outcomes we want.

That is, we are logically connecting methods to impacts to effects. Indeed, when we pursue high performance, it is the entire set of connections that we need to either design or select... build or buy. Those are the decisions, the ones about what connections to make and what to connect, that are most affected by knowledge.

More specifically, how does knowledge affect those decisions?

An important attitude towards discussing this question is to appreciate the difference between recommendations that are transferrable (aka, content) versus decisions that are knowledgeable (aka, expertise).

In both cases, we have a goal of gaining greater awareness of states, options, implications and consequences. The operational contrast is interesting, however.
- With content, which we procure, we wind up mainly dealing with facts and recommendations.
- With expertise, which we produce, we mainly deal with insights and decisions.

Intuitively and empirically, we know that to get from facts to recommendations might require the intermediary steps of having insights and making decisions -- which is why we think of content as having "embedded expertise". But this just emphasizes that understanding expertise is the starting point for determining where value originates. Thereafter, the value has to be successfully exploited in the work procedure.

To start with, all decisions are choices. Focusing on expertise, the point is that awareness allows subsequent choices to be more logically and accurately indicative of the expectations (e.g. performance) we attach to them.

This awareness may become available before and/or during the effort to make choices. But either way, when we say "better decisions" we are really saying "smarter choices" -- while not yet saying "most desirable outcomes". The fact is that the most desirable outcome (a goal) might not be one to which currently recognizable choices exhibit a very well-founded ("smartest") path. Now if the intent is to have the desired outcome through hell or high water, then the primary challenge is to be more knowledgeable -- literally, to generate more awareness -- so as to further substantiate the choices.

That is, being knowledgeable is a certain kind of behavior that works mainly on information processing -- the kind of thing represented by what is called "business intelligence"... And in that sense, managing "knowledgeability" is a whopping big business, focused on creating timely (and even just-in-time) expertise. Driving it all is the talent for determining what needs to be discovered and then to discover it.

But given that, it's difficult to avoid also positioning knowledgeability as a source of knowledge, and we wind up seeing decisions as a form of knowledge. We communicate them and we repeat them in practice, meaning that they migrate to being recommendations, credible as long as their effects and side-effects prove not to be too negative.

In line with that, prefabricated knowledge that is procured instead of produced has its most common currency in the form of recommendations. Recommendations basically present thoughtful conclusions whose derivation might be tracked back through the information processing that preceded them. But the main difference here between recommendations and decisions is that recommendations emphasize the context of a decision, while decisions per se emphasize the construct (ingredients) of the decision.

We usually study recommendations for their relevance, and basically start with relevance as the filter for selecting and approving them. In contrast, for selecting and approving decisions, we study them to find out how meaning is generated from facts.

The question is, where do users of decisions and recommendations get their pre-dispositions about meaning and relevance -- and what is the process for evolving those into the most productive positions?

If we see decisions and recommendations as being manageable, it follows that we are concerned with organizing how users of decisions and recommendations are led to meanings and relevance.

That guidance initially happens primarily through the requirements issued by defined business processes and tasks, which are the way that the organization provides dynamic structure to business production. The requirements tell users what to look for and why.

Taking note of that, it is evident that "emerging" or "incoming" knowledge then interprets the requirements -- by comparing known previous decisions and recommendations to available current (and especially new) ones.

The results of this comparison may propose changes -- each change having associated cost, risk and benefit that is to be aligned with the objective of the business process or task.

That alignment will usually be evaluated in terms of whether the proposed inputs (cost, risk and benefit) increase the probability and completeness of meeting the objective. This makes for four general evaluations in terms of productivity:
- more likely, with more complete enablement
- less likely, with more complete enablement
- more likely, with less complete enablement
- less likely, with less complete enablement

Those generic flavors of relative productivity usually get specified as detailed formulas pertinent to the particular type of process or task. But the more important point here is to have the visibility on how interpretation of requirements is the leverage point for knowledge to influence productivity.

Interpretation of requirements can have two outcomes itself.
- On the one hand, various ways to meet the incumbent requirements can be discovered, validated, renovated, and so forth.
- On the other hand, the incumbent requirements themselves may be challenged enough to begin changing, towards better correspondence with the process or task objectives.

Therefore, it is important to distinguish productivity from performance.

Performance emphasizes the actual outcomes and whether they are more or less close to the intended outcomes. Performance doesn't give points for doing the wrong thing well.

Productivity, however, emphasizes the effectiveness of the manner in which the actual outcomes were generated.

High performance is fostered by high productivity, but they are not synonymous. Under a relatively unchanging model of high-performance, productivity may be accomplished in different ways at different times or places. What high performance wants is for an underlying productivity to enjoy agility -- as a way to assure that the high performance might be sustained against changing situations.

To imbue agility in the productivity, knowledge has a key role to play -- namely, finding alternative effective manners, on a timely basis, for fostering and generating desired outcomes. As the business need for agility increases, the role of knowledge becomes more and more critical and valuable. Making that role effective is what knowledge management is all about.

Posted by Malcolm Ryder at February 23, 2006 9:21 AM

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