What is the difference between Information and Knowledge?

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.

The short version

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.

KVC_Triangle_FNL_RGB_w_name_jpegKnowledge, on the other hand, is essentially 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.)

But what about “explicit knowledge”?

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 field. 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; we define all knowledge as quintessentially tacit. Knowledge that has been mediated — by speaking it, writing it, entering it into a database, etc. — is what we identify as information.

We fully agree with most experts — 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 (information).

“Explicit knowledge” is an index

We might productively think of the mediated information about our knowledge as 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 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 trial and error, practice, and just getting the hang of it.

Information is static

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.

But what’s “static” about real-time data?

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.

But life is dynamic

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”.

And knowledge is dynamic

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.

Does this matter?

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.

When we manage information as if it were knowledge, we court failure

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.

in short, by managing information while we purport 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 counts.

Positioning Knowledge for Value

The early-winter holiday break is an opportunity to recharge our batteries and refocus our strategies. Amidst visiting with family and friends, I took time to reflect on the recent past and what the future holds.

Among other things, I realized that over time my clients have been paying more attention to the top half of the Knowledge Value Chain (how knowledge is used) and less about the bottom half (how knowledge is produced.)

What drives knowledge strategy?

Ideally, knowledge strategies spring from, and are tightly linked to, top-level enterprise strategies. In practice, however, many of the problems in knowledge production spring from misunderstandings of, or lack of clear linkages to, enterprise value.  Some of my research on this is cited in the KVC Handbook.  This knowledge-value gap raises several existential questions about knowledge-centric activities, among them:

  • How does knowledge support our enterprise mission and strategies?
  • What tangible benefits does knowledge provide us?
  • Is our knowledge strategy optimized in an economic sense?

Any lack of clarity at the top of the pyramid tends to get driven down through the chain, where it causes tactical and executional confusion and ineffectiveness.  Those of you in the trenches will know what I mean…

Benefits-driven positioning

If you are a knowledge producer, do not wait for those problems at the top to get sorted out — seize the initiative yourself!

We’ve been advising our clients: Always position your product (and I use this term to include services) from the point of view of the needs of, and benefits to, your user/customer/client/patron. Not — as so many of us do instinctively — from how your product works, why it is wonderful, or even why it’s better than your rivals’.  The diagram below summarizes TKA’s discovery process for working with clients on this. Read the rest of this entry »

Busy Season

I hope each of you is enjoying this new year — whenever it is that you celebrate its beginning.

For me, 2017 is already full of new beginnings and revelations.  To recap:

Launch of KVC Clinic v.2.0

In the fall of 2016, we launched an expanded version of our KVC Clinic. KVC Clinic elementsThis includes three days of on-site work with each intelligence or knowledge services group, as well as depth interviews with internal clients. It’s a hybrid event incorporating experiential team learning, organizational diagnosis, and customer research.

We received enthusiastic engagement from our initial host team, and were able to quickly develop a solid set of recommendations going forward. We are delighted that this client has added the KVC as a major 2017 initiative in their knowledge services program. Read the rest of this entry »

Value Gets Lost

“I’m stuck at the bottom of the pyramid.” “My value is unclear to people who matter.” “I’m invisible.”

In conducting “Points of Pain” exercises during TKA’s workshops and on-site clinics, too often we hear things like this from competent and hard-working knowledge producers. In study after study, roughly half of the challenges expressed by PRODUCERS of organizational knowledge or intelligence involve questions or concerns about the value they generate.

More often than not, the questions are not about producing value per se — usually producers are pretty clear and confident about that. The major gap is that their client USERS do not understand this value — and that therefore they have trouble attributing the value of knowledge back to those who originally produced it.

Our output is their input

In economic terms, any knowledge or intelligence work product, while typically the OUTPUT or end product of a knowledge or intelligence process, is subsequently the INPUT or raw material for a client’s work stream. Knowledge users take over where knowledge producers leave off — that’s one of the fundamental lessons of the KVC framework. During the handoff — the Communication step — the knowledge work product is transformed into intelligence — the basis for decisions, actions, and the production of “enterprise value” (for example, a product that brings revenues into a business).

A KVC Clinic client recently pointed out to us that the KVC triangle graphic makes it appear as if value is only produced by people and processes at the top. This was a fundamental misunderstanding of the model — for which (of course) I take full responsibility.  And hereby try to correct, please read on…

Triangle and trapezoid

The Enterprise Value (EV) Triangle

The Enterprise Value (EV) Triangle

What we mean by the word Value in the “little triangle” at the top of the KVC triangle is more properly specified as “Enterprise Value” (EV). Value is produced at each one of the seven steps in the KVC process (see the KVC Handbook p. 54). But value is only realized — i.e., made manifest and measurable in terms of revenues or other organizational outcomes or results — at the top.

Using one of my favorite analogies, the people who pick the grapes ultimately get paid by the people who buy the wine — but there is a value chain of activities that separate these two economic events in time and space.

The Knowledge Value (KV) Trapezoid

The Knowledge Value (KV) Trapezoid

With knowledge services, the problem is that the production of “Knowledge Value” (KV) — what another client called “the trapezoid” below Communication — is often separated from the production of EV, in two respects: (1) separated in time, and (2) separated in organizational location.

Read the rest of this entry »

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.

Read the rest of this entry »

The Research Matrix

The other day I received an email from “Susan”, an alumna of the Columbia IKNS program whom I had the good fortune to work with as one of my students there. Susan’s question to me was on research, which that program touches upon but doesn’t cover in great depth, and in which I have lots of experience.

Susan’s client is exploring the feasibility of entering a new industrial services market related to energy.  Susan was looking to me for guidance about how to structure and price her research proposal to them.  For my purposes here, it doesn’t matter what the industry is — though I can say it’s B2B, and not one of those industries (like technology or health care) that is often in the news.

The value question

I prefer to demystify things wherever possible, so I proposed that Susan start by determining the client’s value question: What do they want to find out, and why (i.e., what do they plan to do with the answer)?  Susan had studied the KVC model at Columbia, so she knows how value is created from knowledge by making decisions and taking actions based on that knowledge — and that Planning (“Step 0”) starts at the top.

Columns A and B

Then I went into TV detective mode. Working down the chain:  what do we know, and what do we want to know?  We essentially have two columns, A for what we know, B for what we don’t know.  A is essentially our “value-relevant knowledge inventory”, and B is our “open to buy” knowledge purchase order.

Our mission

Simply put, our mission is to move things systematically from Column B to Column A.  That’s the essence of the research agenda, represented in the diagram by the orange arrow.

Except that it’s not often that simple.  (Bet you could see that coming.)  Knowledge users/clients are people, and people are essentially and eternally curious.  So Column B starts life as a set of Minimally Required Knowledge (MRK) to solve our business problem — our “need to know” items.  (Philosophers call this paring-down approach Occam’s Razor.)

Read the rest of this entry »

Meetings Will Make You — or Break You

Most of us work in virtual meetings often, some of us almost exclusively.  People call in using Google Hangouts, Skype, GoToMeeting, WebEx, JoinMe, Free Conference, and so on.  (I’m speaking here of “virtual meetings for the rest of us,” not the high-end meeting rooms costing hundreds of thousands.)

The hybrid meeting

I’ve been part of and/or hosted lots of physical meetings, and lots of online meetings.  In a meeting I hosted the other day, I encountered a variation — the digital-analog hybrid, where some of the people are remote and some are in the room.  (I’m in New York City, where everybody passes through at one time or another.)

It’s amazing what happens around the table that people on the phone do not have access to.  Off-mic side comments, glances, smiles, tones of voice — a panoply of meta-meaning that provides richness and context, and that only those physically in the room benefit from.

This led to a misunderstanding with one of my three co-presenting colleagues who was not present in the room.  I should mention that presence or absence had nothing to do with how important each person was to the meeting, nor how important the meeting was to each of them.  It was based simply on their availability to come to NYC at that time.  (Some were connected from as far away as Europe.)

The problem

For my purpose here, it’s not so important what exactly transpired.  Let’s just say it was a miscue deriving from an agenda item that was modified at show-time by consensus of those physically around the table — but not made clear — and here’s where as meeting leader I fell short — to those calling in. In other words, it was a problem enabled by the hybrid nature of the meeting.

What I’d like to share with you — because many of you may experience this too, with regard to meetings, or other conflicts with peers — is how we resolved it.

Read the rest of this entry »

Piercing the Enterprise Bubble

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Harkness Tower, Yale

A few weeks ago I attended a reunion at my alma mater, Yale University.  As they always do, Yale offered up some of its most articulate faculty and administrators to describe the current state of affairs at the University.

The array of talent, initiative, and innovation on display was dazzling.  By the end of the two days, many of those of us who attended college decades ago were ready to sign up for another round — things have changed that much in the interim.

The Yale bubble

One surprisingly interesting session featured current administrators and faculty commenting on the current state of the University.  One dean mentioned what she calls the Yale bubble. It seems that students expect, and routinely receive, such high levels of performance from themselves and from the institution that they experience culture shock when they step outside its boundaries.

As one current student put it, “At Yale, it can be easy to get absorbed in our work, our activities, and friends. It can be easy to surround ourselves with a nice little Yale bubble.”  She goes on to describe how she and some of her friends broke out of that bubble to raise money for a disaster relief effort after a hurricane in the Philippines.

More recent events could be interpreted as showing the downside of the Yale bubble — a potential loss of balance and perspective as to what really matters.

Corporate culture — for better and for worse

Every enterprise creates its own nexus of practices, protocols, traditions, mythologies, and values — strands that together weave the fabric we call corporate culture. When you count over 300 years of history, $24 billion in the bank, and US presidents and other world leaders among your alumni, as Yale does, it’s easier than average to pull this off.

But every enterprise builds this cultural bubble, whether intentionally or not, and whether successfully or not.  It’s an essential part of what binds people to the enterprise — and thereby to each other — in collective pursuit of some common goal.

In some cases the enterprise bubble is “bubble-istic” — fluid, transparent, and porous.  It alternately expands and shrinks to fit new circumstances.  It is welcoming and inclusive.

In other cases, the enterprise bubble is made of steel and concrete.  It is hard, inflexible, exclusionary, and restrictive.  (North Korea might fit this model, for example.)

Read the rest of this entry »

Paths to Value

It makes my day when I am asked a question I can’t answer completely and easily. The students in Columbia’s Information and Knowledge Strategy program rarely fail to disappoint in this regard.

Where do KPIs come from?

One IKNS student has been using the KVC framework in her “day job” to design program evaluations for a global NGO. Her question had several aspects: “Who designs Key Performance Indicators? Is that job, a profession, or what? How and where do you learn how to do it? How do you know it’s right?”

My first instinct was to answer from my personal experience. Shortly after I left college — in The Digital Dark Ages — I was tasked to design  a set of performance metrics for day care agencies under contract with the State of New Jersey. I was officially a “Contract Administrator”, not a metrics designer. I didn’t know what I was doing — metrics design was just part of my job. I found a book on it, and somehow managed (like the one-eyed man in the Land of the Blind) to pull it off to everyone’s satisfaction.

The Balanced Scorecard industry

KPIs reached industrial strength with the development of the Balanced Scorecard in the early 1990s. People had begun to realize that financial performance metrics only took you so far — but typically these were the results of other events and activities that were operational in nature. Measuring those operational things (customer satisfaction, for example) was seen to have its own value. Metrics became a mini-industry — businesses were created to design and implement KPIs and (later) enterprise dashboards to monitor them.

In all this, there’s good news and bad news. The good news is that enterprise management has entered a new age of empiricism. Everyone wants to be evidence-based and metrics-driven, instead of gut-feel-and-instinct driven as previously.

The bad news is that in our quest for metrics, we are relying heavily on our ability to find the right metrics. Metrics do not grow on trees; they require resources (people, time, technologies) to develop and to implement. They themselves are investments, each having some (greater or lesser) ROI.

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Status Spring 2015

This spring has been uncommonly busy, and I regret that has caused me to slip a couple of self-imposed deadlines here.  Here’s what we’ve been up to.

Blended Value

I continued to work with TKA Director and branding expert Jay Gronlund on our Blended Value initiative — which seeks to redefine strategic enterprise goals beyond the limits of shareholder value, and to incorporate Environmental, Social, and Governance (ESG) factors in a balanced mix.

Knowledge-Based Security

Next up was a small pilot intervention for a section of the Special Operations Command of the US Department of Defense.    It seems that “knowledge” has great value not only where economic conflict is concerned, but also where geopolitical conflict is concerned — especially in this era when the nature of military conflict is continually shifting.

Knowledge to Value

Following that was a major address to the second annual summit of the International Management Consulting Association, with many of their members working in the Middle East and Eastern Europe (include the former Soviet republics).  I presented an initial treatment of a new methodology “Knowledge to Value”, which is designed to systematically mine enterprise knowledge in support of business development strategies.

Read the rest of this entry »