In a manufacturing environment, inventory turns are a good proxy metric of the health of the business. The more inventory that flows through the business is an indication that sales and production are both going well.
As jobs increasingly shift toward ‘knowledge work’ (despite the enormous contributions of Peter Drucker, I really hate the “knowledge worker” term that he coined), it makes sense that organizations would look for something equivalent to manage, measure and improve the productivity of knowledge work.
However, knowledge inventory has very different properties than physical inventory.
The idea of “knowledge turns” have been around since at least the 1990s, but there is not an accepted definition of what constitutes a “knowledge” turn.
Unlike physical assets, the same knowledge can be in multiple places at the same time, and in many cases that knowledge is not proprietary.
Knowledge inventory isn’t depleted when a knowledge asset is used. That same bit of knowledge can be used again. The particular bit of knowledge that is used over and over again is particularly valuable. It can be used in the same context or in a completely different context. It is the same knowledge
If you own the rights to that bit of knowledge that is an incredibly valuable piece of knowledge. This is why we see so many individuals and organizations try to patent and or trademark certain bits of knowledge.
Craft Knowledge and the Knowledge Factory
Craft knowledge is a little bit like reinventing the wheel. A problem comes up and you use your knowledge to solve that problem one instance at a time. This can be highly valuable work depending on the nature of the problem. However, applying a craft knowledge solution to a problem that has already been solved in the knowledge factory is not efficient.
The knowledge factory, on the other hand, is putting the same piece of knowledge out there again and again to solve the same problem. The solution exists and you use it. There may be variations, but the basic idea is that little or no new creation is needed. In this case, it is a matter of finding and acquiring (if necessary) the knowledge that already exists. Sounds easy in theory, but…
Framing the Knowledge Problem
Unfortunately, these are often the areas that are the hardest to reframe.
Solving a knowledge problem often has to do with how it is framed. The way that you frame a problem has a lot to do with how you go about solving that problem. The bigger the problem, the more opportunity there is for reframing. Problems with deeply entrenched and ingrained framing are often the areas that would be best served with different framing.
This is not to discount the value of expertise and experience. Watching an expert perform a task or solve a problem in their domain can be a marvel. They make it look easy and they are rarely thrown by any curve balls.
The problem comes when there are institutions built around vested interests in various knowledge domains.
The deeper and narrower that fields of study get, the more the need for cross disciplinary idea pollination is necessary. However, the access points for ideas to cross pollinate may actually need to be more serendipitous than planned.
It can be challenging to reframe a problem the more deeply you are embedded on a track. The more committed that you become to an idea, the harder it is to see alternate ideasm
Good Frames, Bad Frames, Same Frames
Good framing opens up the solution space more widely, while bad framing tends to focus on a narrow solution set early on. Sometimes this is efficient, but other times it can lead to less than optimal results.
The unfortunate reality is that knowledge itself suffers a bit from how it is framed. Looking at situations as purely a technical problem leaves a lot of possible solutions off the table that may be more effective.
Sometimes the need for quick action leads to reductionist thinking. This narrows the field of vision and limits options.
Ironically, information technology empowers and emboldens reductionist thinking. Programmatic approaches to problem solving are by definition reductionist. A series of infinite choices systematically reduced to a few options buried somewhere in the settings and definitions of a program or operating system (sometimes even both, say hello sound and volume controls on PCs).
The Problem of Algorithms
Algorithms are assumptions built on assumptions built on assumptions that are built on incomplete data.
Each assumption might be completely logical based on the previous assumption. The data underlying the assumption might be completely accurate too. There is probably even a best practice or two built into this, but reality is not that simple.
The algorithm only knows so much, but makes “logical” guesses based on the context that it knows about.
Predictable but Not
Human behavior is often predictable… Especially, the behavior of individuals that you know well. You often know what an individual will end up doing before they actually think that they know.
Collectively the behavior of a group of people may also be predictable on average. However, making decisions based on averages is often sub-optimal for everyone. There is no average. It is only the results of decisions taken by individuals over time in response to a whole bunch of other factors that no one really knows about. This may average out, but all behaviors occur at a point in a time with a certain context. Current information systems can only help so much. Sure Amazon can start moving inventory around when it senses an impending order, but there are not many of us that have that volume of information to make these kind of optimizations.
As the Knowledge Turns
It is really up to the individual users to think about how to leverage knowledge. Collecting information for information sake is not productive. Data, information and knowledge need to be used to be of value. Having information for reporting can be useful, but not necessary useful for creating value.
We need a cycle of knowledge that feeds back into the system. Gathering and circulating that information may be the way to start quantifying the knowledge turns in your organization.