The Knowledge Payout

Knowledge Management would be better off as a discipline if it leaned into the management side more, and relied a little less exclusively on the knowledge side. It’s possible to read articles or even whole books on KM and find little discussion specifying the knowledge that people use, specifically how they use it in producing value, and/or what it costs.

Productive knowledge is transitive and transactional; it is necessarily “about” something.  That something is the work that knowledge users do in producing value in the process of doing their jobs.

This reminds me of my college physics classes, where they would start discussions by positing, “Assume there is no gravity and no friction.”  This is because many basic models in mechanics work best under these idealized conditions.  But in the real world, where (regrettably) we all have to deal with both gravity and friction — the models need substantial modification before they adequately describe how things actually work.

I kept my previous post, The Research Matrix, blissfully free of such real-world considerations.  Intentionally so, since the principles of “knowledge science” — like those of physics — admittedly work best without them.

The value context

In reality, knowledge exists only in a value context — it provides benefits, but also incurs costs.  “Value” is simply the ratio of benefits to costs.  My intention here is to add that context to my previous post, and to offer you a systematic framework with which to assess knowledge benefits and costs.Value =

As our B2 list (“Nice to Know” items) grows, we still want to move things from Column B (What We Don’t Know) to Column A (What We Know) — but, in considering costs, we will surely need to make tradeoffs.  Life could be a dream if time and budgets were in infinite supply, but they are not.  So we need a “value of knowledge” payout table that considers the benefit (to solving our problem) and the cost of each “unit” of knowledge. This will enable us to create knowledge priorities driving a research agenda — essential in our world of all-too-finite resources.

“Cost versus value” is a theme I develop in the KVC Handbook (p. 40). At its most basic, it says that while the marginal COST of a given unit of knowledge is (more or less) fixed, its marginal BENEFIT is contextual based on the knowledge already possessed by the user.  Marginal benefit typically decreases over time spent in a research process, because you are adding progressively less to what you already know.

We can relate benefit and cost to each other by means of a KNOWLEDGE BENEFIT/COST RATIO (KBCR) — the “bang for your knowledge buck,” and functionally equivalent to our knowledge value or ROI.  KBCR is the function that we are seeking to maximize.

Cost versus benefit

To start simply, let’s assume we have four classes of “don’t know” items – DK1, DK2, DK3, and DK4, each defined by the relative costs and benefits of acquiring those items, as shown in the diagram.  Two of these (DK1 and DK2) are high benefit — but one of these (DK1) is low cost, while the other  (DK2) is high cost.  Clearly the KBCR (represented by the green arrows) is higher for the high benefit-low cost DK1 items than for the high benefit-high cost DK2 items.  We will place the DK1 items at the highest priority in our research agenda.Knowledge payout 2

Low-benefit items (DK3) can have a KBCR that is not too terrible, if their cost is also low, as shown.  But they can also become time-wasting distractions if their costs are underestimated.

Low-benefit items that have a high cost (DK4) are clearly “losers” — steering clear of which will boost our overall knowledge ROI.

Low-cost items could, for example, include the results of online searches. High-cost items typically include primary research, for example, speaking with a subject-matter expert.  The relative benefit of a unit of knowledge is typically defined by the outcome-relevance of that knowledge (“Will it make a difference?”), the potential impact of the decision itself (“What is at stake?”), and the extent to which that knowledge is “novel,” i.e., not currently possessed in any form.

This framework is still idealized, since both cost and benefit are continuous economic functions — with actual values other than just “high” or “low”.  But this outlines the basic approach.

So what?

I’ve tried to make two major points here:

  1. In designing a value-based research agenda, there needs to be explicit consideration of both the benefits and costs of acquiring new knowledge, as trade-offs will need to be made in the real world.
  2. The benefit, and by implication the VALUE, of knowledge is user-contextual — it depends on how useful that knowledge resource will be in making a business decision, creating and executing actions, and producing value for the enterprise (revenues, profits, KPIs achieved, etc.)

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    COMPETING IN THE KNOWLEDGE ECONOMY is written by Timothy Powell, an independent researcher and consultant in knowledge strategy. Tim is president of The Knowledge Agency® (TKA) and serves on the faculty of Columbia University's Information and Knowledge Strategy (IKNS) graduate program.

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