The peculiarity (and the problem) of valuing information and knowledge lies in its context dependency. The same knowledge or information asset can be incredibly valuable in some circumstances, and worthless in others, with only minor changes in the environment. Moreover, as Dorothy Leonard points out, the same knowledge assets can be an enabler in some circumstances and a disabler in others:
[An organisation’s core capabilities] are being constantly enhanced from multiple sources. However, at the same time that they enable innovation, they also hinder it … ‘managers unwittingly collude’ to avoid actions that challenge accepted modes of behavior.
As early as the 1950s, Edith Penrose explored the challenge of valuing something with high but context dependent potentiality like capabilities and found herself at a loss for some kind of predictable formula. The Information Resource Management (IRM) movement in the 1970s tended to gravitate to the valuation of problems actually solved by information resources and systems, focusing on the importance of decisions enabled as a loose equivalent of transacted value. On the other hand, the core competencies movement of the 1990s moved up the value stream by generalising to broad competences and looking at strategic goal alignment as a form of valuation.
Some adopted a more rigorous finance-based approach, as with the intellectual capital movement aligning itself with the intangible asset accounting movement. Unfortunately, while intellectual capital has a plurality of frameworks it has failed, over 20+ years, to (a) agree on a common model that can be widely adopted, and (b) to explain the transition between a valuation’s high level strategic capabilities and how that value is derived from its constituent granular information or knowledge assets.
In short, valuing information and knowledge is a problem that has been beaten half to death. The most useful and accepted work tends to be at high, strategic and aggregate levels, and the least convincing or useful work has been at the granular asset level.
Some valuation approaches have had negative impacts on knowledge management as a discipline. A legacy of the IRM movement was to associate value with investment (cost), which led to a call for the justification of IM/KM initiatives through return on investment (ROI). This is a problem that many practitioners still struggle with today, since ROI approaches almost always fail to consider arguments about the potentiality of information and knowledge.
One key challenge for KM is that by converting implicit knowledge to explicit knowledge, potentiality is removed. Max Boisot discusses a number of these trade-offs in his writings, especially the trade-off between the value of achieving scale and reach when you make implicit knowledge explicit, versus the loss in adaptability and variety of applications in knowledge once it is customised and codified.
Faced with the question of whether it worth investing effort in converting something from implicit to explicit knowledge, decisions should be made pragmatically on the basis of short term utility, that is: “Given our context now and the problems we have now, how useful would it be to explicitise, versus the cost and foreseen volatility of this knowledge?” It is rare that you will be able to justify explicit knowledge capture based on long term value / potentiality because it is so unpredictable.
Following Max Boisot, however, it is important to understand the trade-offs when converting to explicit knowledge. Where adaptability and volatility of the environment are key considerations, it is unlikely that a purely explicit-knowledge strategy is going to be effective.
Thinking longer term means:
- Being able to read the longer arc of how the environment is developing (which generally takes experience)
- Having a reliable understanding of your organisation’s current portfolio of capabilities and shortfalls in relation to that arc, at a significantly higher level than the knowledge asset (this is where the core capabilities movement comes in), and
- Being able to translate a high level strategy into deliberate changes at the knowledge asset level with intentionality, over a significant period of time
The last factor is likely to be the most challenging piece of all, especially as this is an iterative cycle, and your readings of the environment will change. Taken together, these three factors make the issue of how and when to invest in strategic knowledge much more complex than simply valuing knowledge as assets.
The concept of value invites the notions of measurement and quantification. At a tactical, short-term level, this is not impossible. At a strategic level and longer term level, “intentionality” and “adaptiveness” are much more useful guiding metaphors than “value”. Even at the short term level, I find that “utility” is a better term than “value” when looking at knowledge investments, since the latter term implies an ability to quantify problem spaces that often resist such easy evaluation.
A knowledge audit helps to expose these very issues for discussion: defining and mapping the problem space, aligning knowledge with business needs, and weighing up options and priorities in relation to short term needs as well as longer term strategic arcs.
— Stephen Bounds also contributed to this article.