In my KVC Handbook v. 4, I draw a clear distinction between knowledge and information — essentially that knowledge is a more “processed” version of information. In speaking with people I find that this difference is still not totally understood — so will amplify here.
Simply put, the distinction is this: information is essentially inanimate — organized data that has been captured in databases, papers, books, news articles. Information is essentially mediated — by definition it exists only as embedded in a medium like those mentioned.
Knowledge, on the other hand, is essentially organic, and more specifically, human. What we mean when we talk about knowledge is invariably embedded in an animate being. (I’ll allow that this definition could recognize that animals have knowledge — but until such time as they can write or talk understandably to us about it, I’m willing to let that line of speculation go.)
A book on the shelf is information — until a person reads it, understands it, and absorbs it. Then (and only then) it has been converted into that person’s knowledge. (When the person subsequently socializes that knowledge and applies it to make decisions and/or take actions, then it has become intelligence. But that’s a discussion for another time.)
A third key element of difference is that information is easily, inexpensively and widely scalable. Thanks to digital technologies, information can be, and often is, distributed globally in seconds. Knowledge, on the other hand, is typically localized and difficult to “transmit.” (In fact, knowledge cannot be transmitted directly at all, but must be transformed into information — a process we’ll explore in a later post.)
There are those who speak of tacit knowledge — implying that there are also varieties of knowledge that are non-tacit, i.e., explicit knowledge. The scientist-philosopher Michael Polanyi is said to have first coined this distinction in the late 1950s, which has become widely-accepted, even canonical, in the Knowledge Management establishment. In 1995, Nonaka and Takeushi developed a model (“SECI”) for how individual tacit knowledge is converted into explicit knowledge, then socialized within the enterprise.
We think a wrong turn was taken. The KVC framework finds “explicit knowledge” a contradiction in terms; in our experience, all knowledge is, by definition, tacit. Knowledge that has been mediated — by speaking it, writing it, entering it into a database, etc.— is what we identify as, by definition, information.
We fully agree — and this was Polanyi’s original driving insight — that “we know more than we can speak.” Indeed, we find this a titanic understatement. It is a mere fraction of what we know that we can capture in its mediated form as information.
This is not to say, however, that knowledge and our information about that knowledge are not closely related. We might productively think of the mediated information about our knowledge as meta-knowledge, in effect an index to that knowledge — a series of pointers. Even in its much-reduced, codified form, such an index nonetheless plays the vital role of navigating us into, and within, the body of knowledge. Information enables our knowledge by providing us ways to access it.
Did you ever try to write down all the things you did, conversations you had, thoughts and daydreams you had within ONE day? James Joyce did this when writing his magnum opus Ulysses, which runs to more than 1000 pages — and barely scratches the surface of its characters (who, though fictional, are based on real people.)
In many cases, one might even in some cases question whether the transfer of knowledge to its information analogue has much value at all. An often-cited example is learning to ride a bicycle. An experienced bike rider could explain for days, in great detail, how it is that she rides a bike. Her student will listen for days on end, asking lots of questions — but without being able to ride himself.
While the explanation (the information) can serve as a foundation for developing the knowledge of how to ride, much more fruitful in that process is experiential — trial and error, practice, and just getting the hang of it.
What support can I offer for drawing this clear distinction between information and knowledge? I start by comparing the essential characteristics of information and knowledge.
Let’s compare the two in terms of their dynamism — the speed at which, and degree to which, they change. Information is essentially static. I have a book sitting on the shelf; when I open it a year from now I will expect it to have the same content it does today. And if, for any reason, it does not — then it has not fulfilled my basic requirements for a book. Information does its job by remaining reasonably static.
What about databases that are monitoring with sensors — for example, the health metrics of people wearing smart watches? Or your social media feed? While it’s true that they are continually being refreshed or added to — once that refresh process is completed, the information recorded as of a certain moment is there permanently.
So permanently, in fact, that there is now a “right to forget” legal movement to have Internet data be more dynamic — specifically, to have data scrubbed that would be incriminating, embarrassing, or is otherwise not wanted.
But absent some action to expunge such data, the default is that it stays around forever. Information is static at its essence.
Where information does not change, the underlying phenomena it describes do change, continually. As soon as you commit “knowledge” to a medium — essentially converting it into information — it starts to become “out of date” — it decays, in other words. Information has a shelf-life, a half-life, during which time it becomes progressively less useful as a representation of “what is”.
Knowledge, on the other hand, is dynamic at its essence. Knowledge is adaptive — continually shifting, being modified, being enhanced. Knowledge is “wet” — it is organic — it is human.
Knowledge does ITS job by being dynamic. Change, adaptation, and evolution of knowledge are essential elements of its character. If knowledge does not have these characteristics, it is not fulfilling its purpose.
Is information versus knowledge just a semantic distinction that makes little difference in the real world? I think not, and here’s why.
It has to to with that management thing that we encounter in the real world. I propose above that information is essentially static, and knowledge essentially dynamic — in other words, that these two are essentially different as economic resources. If this is true, it follows that the respective manner in which these resources are optimally managed within organizations will likewise be completely different. If, for example, you try to manage knowledge as if it were information, you violate the essence of the resource — dramatically increasing the likelihood that you will fail.
And this is exactly what happens in many “KM systems”. Tacit knowledge is made explicit through an elicitation process (whether moderated or self-powered), then put into a database for storage — where it can (in theory) be retrieved and reused — that is, converted back into knowledge by another user.
My experience is that, in practice, this “knowledge re-use” rarely works as effectively as expected, for a number of reasons. Primary among these reasons is that the knowledge, once made explicit by being converted into information, is no longer dynamic. It ages and becomes progressively less useful — often quickly.
Our temptation in conflating information and knowledge is to manage the former while asserting that it is the latter. We can think we are “managing enterprise knowledge” by, for example monitoring DOCUMENT metrics — for example, the numbers of times they are viewed, downloaded, “liked” or endorses, etc. Documents are information — static and by definition out-of-date.
Document access may be useful as an OUTPUT measure — but not as as a true metric of KNOWLEDGE, and even less as an OUTCOME measure, which is what really matters. It tells us nothing about whether the document was converted into knowledge (i.e., by being read, understood, and discussed), and even less about whether it was converted into Results, Outcomes, and Impact — the true measures of knowledge value.
By managing information while we intend to manage knowledge, we let ourselves off the hook by getting off the “value elevator” on a lower floor than we might optimally do. We measure what is easy to measure — instead of what matters.