KM History


grandfather1

How will he learn what Papa knows?

The title for this post is taken from a 1993 RAND report written by two friends and former colleagues.  It is occasionally useful to revisit the first principles when discussing weighty matters such as KM.  Or, as was the case for my friends, U.S. Strategic Forces.

A recent conversation on Twitter involved a fairly innocuous blog posting that discussed briefly the notion of tacit and explicit knowledge.  The problem, for me, is the definition for tacit knowledge in this blog was “that which has not been recorded, written, printed, or otherwise captured in some medium.”  Explicit knowledge, by contrast, has been.  Therefore, the challenge is to make tacit knowledge explicit – because knowledge is only transferred through explicit mediums.  To quote:

Unless converted into explicit knowledge, it cannot be shared because it is ‘trapped’ in one’s mind.

The post also referenced a second gentleman, who posed an even more pithy and awful definitional distinction:

He says that the tacit-explicit distinction is abstract and, in reality, knowledge is ‘either findable by your computer or it is not findable by your computer.’ 

Rather than just letting it go as the Bride often advises, I sent a brief message to the first gentleman, expressing my nonconcurrence with his definitions.  Through the magic of Twitter, this became a conversation enjoined by several souls, and I was finally challenged to provide some primary sources that inform my apparent heartburn.  

In all honesty, while the ensuing discussion may appear “abstract” to some, the nature of knowlege should be at least partially understood if one is to consider themselves a practitioner of knowledge management.  Else, content yourself to the vital and growing field of information management – there is no shame in this whatsoever.

It is important here to note that the original post was intended to briefly acknowledge the academic distinctions, but more to exhort people to share the knowledge trapped in their heads.  I agree with this noble intent, but fear the post does violence to related theory.  Believing that knowledge is only transferred once it has been made explicit leads to mechanistic, engineering approaches to knowledge management that have not proven their worth.  Crank it out of people’s heads, churn it into a shared taxonomy or tag it somehow, and then – and only then – is it useful to others.  I would like to know the exact date that the apprentice learning model was made obsolete by advanced information technology.

While a tidy approach to KM (actually more an approach to information management), the call to “make tacit knowledge explicit” ignores much of what we know about how the world actually works.  To be more precise, we are learning the limitations of what we can know as a result of research across the disciplines of sociology, neuroscience, anthropology, and others.  

Last caveat, I do not have much argument with the practitioners who offered via Twitter that tacit knowledge can be made “partially explicit,” or with the gentleman who offered that the fragmented chatter on Twitter was actually an idea way to begin sharing tacit knowledge.  The promise of social media indeed is that serendipitous connections of people, linked via fragmented information, is a step towards knowledge management that recognizes the fruitlessness of other approaches – including ones that seek to harvest tacit knowledge into explicit knowledge bins.  

Here then, my brief list of “first principles” to understand before drawing conclusions regarding the “implementation” of KM.  If these are true, they should change your view on “making tacit knowledge explicit.”

0. Principle zero: define the terms.  Where did we get this term “tacit knowledge?”  Michael Polanyi described it this way:

Thus to speak a language is to commit ourselves to the double indeterminancy due to our reliance both on its formalism and on our own continued reconsideration of this formalism in its bearing on our experience.  For just as, owing to the ultimately tacit character of all our knowledge, we remain ever unable to say all that we know, so also, in the view of the tacit character of meaning, we can never quite know what is implied in what we say.

While technically true that “not findable on your computer” agrees with this paragraph, I find that characterization falls short of Polanyi’s meaning.

1. We don’t know how we know what we know, or make decisions; and therefore unwittingly misrepresent what we know when asked to describe the process.  Lakoff claims that understanding “takes place in terms of entire domains of experience and not in terms of isolated concepts.”  He shows how these experiences are a product of:

  • Our bodies (perceptual and motor apparatus, mental capacities, emotional makeup, etc.)
  • Our interactions with our physical environment (moving, manipulating objects, eating, etc.)
  • Our interactions with other people within our culture (in terms of social, political, economic, and religious institutions) p.117

Gompert, et al., examined the dual roles of information and intuition in decision-making in their investigation into how to increase “battle wisdom” for U.S. forces.  Asking General Patton how he made the decisions he did will not prepare you to respond similiarly in like circumstances.

Snowden puts it this way:

There is an increasing body of research data which indicates that in the practice of knowledge people use heuristics, past pattern matching and extrapolation to make decisions, coupled with complex blending of ideas and experiences that takes place in nanoseconds. Asked to describe how they made a decision after the event they will tend to provide a more structured process oriented approach which does not match reality.

Medina agrees:

The brain constantly receives new inputs and needs to store some of them in the same head already occupied by previous experiences.  It makes sense of its world by trying to connect new information to previously encountered information, which means that new information routinely resculpts previously existing representations and sends the re-created whole back for new storage.  What does this mean?  Merely that present knowledge can bleed into past memories and become intertwined with them as if they were encountered together. Does that give you only an approximate view of reality? You bet it does. p.130

2. We learn through fragmented input and internal cognitive patterns, embedding extensive context from our environment at the time of learning.  Medina, discussing the work of Nobel Laureate Eric Kandel (2000), relates how the brain rewires itself.

Kandel showed that when people learn something, the wiring in their brain changes.  He demonstrated that acquiring even simple pieces of information involves the physical alteration of the structure of the neurons participating in the process. p.57

Fauconnier and Turner discuss cognition – in part –  in terms of guiding principle for completing patterns, as humans seek to blend new concepts onto what they already know.

Pattern Completion Principle: Other things being equal, complete elements in the blend by using existing integrated patterns as additional inputs.  Other things being equal, use a completing frame that has relations that can be the compressed versions of the important outer-space vital relations between the inputs. p.328

Brown, et al, take on traditional teaching methods in their work showing that “knowledge is situated, being in part a product of the activity, context, and culture in which it is developed and used.”

The activity in which knowledge is developed and deployed, it is now argued, is not separable from or ancillary to learning and cognition. Nor is it neutral. Rather, it is an integral part of what is learned. Situations might be said to co-produce knowledge through activity. Learning and cognition, it is now possible to argue, are fundamentally situated.

The context within which something is learned cannot be reduced to information metadata – it is an integral part of what is learned.

3. We always know more than we can say, and we will always say more than we can write down. For my third principle, I am borrowing directly from Dave Snowden’s extension of Polanyi.  (Snowden’s blog should be at the top of your KM reading list):

 The process of taking things from our heads, to our mouths (speaking it) to our hands (writing it down) involves loss of content and context. It is always less than it could have been as it is increasingly codified.

Having read through the first two principles, it should now be evident that relating what we know via conversation or writing or other means of “making explicit” removes integral context, and therefore content.  Explicit knowledge is simply information – lacking the human context necessary to qualify it as knowledge.  Sharing human knowledge is a misnomer, the most we can do is help others embed inputs as we have done so that they may approach the world as we do based on our experience.  This sharing is done on many levels, in many media, and in contexts as close to the original ones so that the experience can approximate the original.  

The grandfather above will not conduct after-action reviews regarding his fishing experiences, write a pamphlet about fishing, and upload it to the family intranet.  Rather, he will take the boy fishing – where he will show him to tie lures, cast effectively, breathe in the experience, and hopefully learn to love what he loves.   

References:

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated Cognition and the Culture of Learning. Educational Researcher, January-February, 32-42.

Fauconnier, G., & Turner, M. (2002). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books, Perseus Books Group.

Gompert, D. C., Lachow, I., & Perkins, J. (2006). Battle-Wise: Seeking Time-Information Superiority in Networked Warfare. Washington, DC: National Defense University Press.

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago, IL: The University of Chicago Press.

Medina, J. (2008). Brain Rules: 12 Principles for Surviving and Thriving at Work, Home, and School. Seattle, WA: Pear Press.

Polanyi, M. (1974). Personal Knowledge: Towards a Post-Critical Philosophy. Chicago, IL: University of Chicago Press.

Snowden, D. J. (2008, October 10). Rendering Knowledge.   Retrieved January 5, 2009, from http://www.cognitive-edge.com/blogs/dave/2008/10/rendering_knowledge.php

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Chain of events: Acquaintance writes email, referencing this blog from APQC.  I respond with a rant, augmented by a couple of acidic twitter messages to release steam.  These rants are posted to my Facebook status line, and results in a brief conversation there with a FB friend – who initially believes I’ve lost my mind.

And now here.  Why here?  I’ve already responded to the acquaintence, and interacted with the FB friend, and overall made my point.  Well, I’m blogging now to establish some measure of permanence to my thoughts.  My apologies then to those two individuals who have already been subjected to my rant.

The APQC blog asked a very reasonable question:  “What’s the Deal with Lessons Learned?”  The author then posits several reasons:

“What is it about capturing and applying lessons learned that so often trips us up and causes us to never get past the “capture” step of the process? Is it that the mistake or error that prompts the lesson is so context-dependent that we think others couldn’t benefit from it and therefore we don’t capture it at all? Or could it be that whatever repository these lessons disappear into is so unorganized that retrieving them in order to apply them is a huge undertaking? Or is it simple communication–in other words, we simply don’t share our lessons learned proactively with those who might benefit from them? Or some combination of the above?”

My answer: E!  None of the above.

My acquaintance works in the Pentagon alongside his command’s “lessons learned” people, and shared that they go in the field, watch exercises, and then let people know where they made they repeated mistakes.  He was asking the same question:  why don’t these programs work as intended?

In organizations where the machinery is larger than the man, where we serve and tend to the machines, where human behavior and decisions are minor aspects of the overall production line – then things like “lessons learned” along with six sigma, Lean, etc., make some sense and have proven results.  The trouble comes when we apply these mechanisms in organizations where the human predominates.

My response is below, slightly edited, but retaining all the snarkiness.  I should add that I was responding in the context of military training and operations.  In most organizations, my opinion is strongly against “lessons learned” programs.  

Regarding lessons learned…  Let’s think about this for a moment.  The underlying presumption regarding “lessons learned” is that what worked before, will work again – and the context around the new situation will not differ enough to make the “lesson” insufficient to the new challenge.  This is arrogant, demonstrably false and dangerous.

First off, when gathering these lessons, we interview people regarding their decisions.  Trouble is, people don’t know how they make decisions.  Not truly, they fill in gaps of reasoning where they actually went with deep intuition.  Finding hard to explain their intuition, they inaccurately weight other decision variables, dutifully captured by the interviewer.  And the lie is born.
Second, context matters.  It actually matters to consider the situation as it lies, and the application of sterile “lessons” that carry a (now lost) context will result in only random chances of success.  Complexity science reveals the teleological realities – you cannot predict events in complex systems; you can set boundaries, establish attractors and modulators and monitor for patterns.  In addition, these systems are highly sensitive to starting conditions (see Lorenz).  Where do “lessons learned” fit against what we know about context-sensitive complex systems?
Fortunately, no one actually uses lessons learned databases to make decisions.  When you are faced with a challenge, do you turn to the ‘lessons learned’ database, or to a trusted friend who may have faced similar challenges?  The latter is likely true, and you update this friend with your current circumstance so that he can match it against his experience – you both then discuss what may be different this time and the limitations of his experience…and then you learn together.
So what should your colleagues be doing?  Collecting “lessons observed” and distilling principles that may be more universal than the specific lessons – but more importantly, they should enhance the connection of professionals.  Consider the success of Companycommander, where Company commanders are able to collaborate and share experiences in near-real time.  Why is this such a success when the Army for years has had the CALL program?
Given this, which should your colleagues be doing?  Mimicking CALL, or CompanyCommand?
Lessons learned programs don’t work because they don’t align with how we think, how we decide, or even an accurate history of what happened.  Other than that – totally worth the investment.

As part of this reform legislation, I’ve been asked to provide a definition for KM.  I’ve managed to avoid this for, oh, 11 years.  But no longer.  There are at least 47 definitions of KM, as compiled by one blogger.  Many good, many not.  I can’t choose one, I need to craft one that I can live with, even if my name will not be associated directly with it.

So here it is.

Knowledge Management refers to the management of the components and enabling of relationships from which knowledge emerges: used to enhance decision making, spark innovation, and comprehend weak signals in the information environment.  Knowledge management does not focus on managing knowledge itself; rather, it seeks the positive interaction of the component elements that can be managed to lay the foundation for better decision making, innovation, and adaptation.

Ok, not pithy, but then again – not everything can be reduced to an elevator speech.  Let’s see if this one makes sense to the lawyers.

My brother-in-law is an economist by training, and imparted the following wisdom to me last week:  “Every bottle of wine costs no more than $2.50 to produce, the rest is just a lot of hands picking your pocket.” Which got me to thinking: – how do you account for the delta between $2.50 and $40, $60, and higher prices for wine?  I certainly accept the costs, and do not -– as my brother-in-law does -– seek out wine bargains with cost as my only driver. 

  This gap is the marketplace of intangibles, enabled by knowledge.  Value network analysis (http://www.value-networks.com/) is the latest business methodology to help firms understand what truly brings value to the enterprise, by capturing the relationships across which these intangibles move.  Knowledge management (KM) is a related discipline, in that it recognizes the intangible nature of knowledge, both individual and organizational. Rather than believing that organizational knowledge dwells in documents or policies; KM, properly applied, extends to encompass the networks across which knowledge flows.

  Prusak, one of the fathers of the KM business field, points to three origins for KM:  ubiquitous computing, globalization, and a knowledge-centric view of the firm.  It has both intellectual and practice antecedents.  Intellectual areas include economics, sociology, philosophy, and psychology.  Practice areas include information management, quality movement, and human capital movements.  The coming together of these practice areas, informed by these intellectual disciplines, is termed – unfortunately – “knowledge management.”

  Snowden, another of these fathers, writes of three generations of KM over the past decade or so: 
1. Information for decision support (spurred on by the technology revolution, which was dominated by perceived efficiencies of process engineering);

2. The “SECI” model (popularized in a book by Nonaka, and purported to show the movement of knowledge from tacit to explicit – Socialization to Externalization to Combination to Internalization).  This led to many unfortunate attempts to “capture tacit knowledge” or “make tacit knowledge explicit through technology,” etc.  A field day for IT vendors, and a black eye for the KM profession through frustrated objectives;

3. A recognition that knowledge is paradoxical – a flow (context) as well as a thing (content).  context is highly dependent upon individual and group cognitive processes, which cannot be captured in a computer (for one: we are pattern processors, computers are information processors). 

  There are voices who disagree with these two gentlemen to some degree, particularly Snowden who is a delightfully confrontational Welshman who is trying to bring the insights regarding complexity into the KM field.  Others believe knowledge is all flow, there is no knowledge in static artifacts; while still others believe the task is to enhance “knowledge processing” to produce more and better “knowledge.”

  A shaky foundation, for which I apologize, but I want to illuminate discord as well as agreement as we go along. 

  To close this first episode, I had a conversation yesterday with a former DoD SESer, who observed that with computing, the Pentagon moved the job of information management from secretaries to individuals – and the results were less than satisfactory with regards to storing and retrieving information.  This observation is critical, as we did believe at one point (or at least behaved as if we did) that staff assistants shuffling and filing papers could be replaced by information technology alone.  The system of papers and filing cabinets included the knowledge of the secretarial profession, which was not reproduced by giving everyone a word processor and email.  The need for effective knowledge management is obvious to all, but the implications remain murky for most organizations.

A debate is underway, or should I say continues, regarding the nature of knowledge. If this sounds like an obscure debate regarding philosophy, cognitive science, and complexity, well, it is. But it also drives management behaviors if you are to tackle KM.

Either knowledge is inherently personal, inextricably connected to experience, unarticulated brain functions, culture – that is, a process that is impossible to deconstruct or replicate – or it is a product that can be subjected to evaluation if not proof. Or perhaps it is both. Or perhaps it is many things, the beloved trinity of tacit, implicit, and explicit.

I haven’t fully cast my lot in with the process folks (Ralph Stacey, etc), but neither am I comfortable with the product view. Joe Firestone is a friend, but I just can’t square his views with any useful practice. Lambe recently observed off-handedly that data is not a primitive of information in a rant against the mindless DIKW pyramid, and I realized: of course. Data is the product of a decision to capture and represent something, a knowledge product or a product of a knowledge process.

I’ll need to resolve thus for myself soon if I am to be of use, but first need to convey the landscape fairly as a first conversation regarding the discipline.