More News from the Dark Side

I pay close attention to feedback I receive on the KVC and other analytic frameworks we are developing.  Many times I make revisions based on this feedback — that’s why the KVC Handbook is now on its fourth major edition.

One of the things I’ve heard is that the KVC model is too idealistic.  Even as I confess to being idealistic by nature, I think that’s a fair criticism.  And thanks to your feedback I use this blog (and my Knowledge Clinics) to address things not in the current edition of the book.

In the private sector, examples of damaging deviations from the ideal are as easy to spot as this morning’s Wall Street Journal.  Last month, for example, I outlined the issue of what happens when the Knowledge Value Chain is broken by chance — or corrupted by intention.

Intelligence in war

This month we examine a case that has been in play for a while, from public affairs in the US.  (Though even our readers in South Africa and elsewhere should take note — things like this could happen there too!)

In general the issue is the reliability of intelligence in an active war theater — here the ongoing actions against ISIS.  Does this sound familiar?  It should — read my earlier post about General Michael Flynn’s criticism and subsequent reshaping of the intelligence effort in Afghanistan.

And those of you who (like me) are baby boomers will remember this issue as it played out in Viet Nam.

Cooking the chain

Like the private sector examples last month, the principle at issue here is what I call cooking the chain.  That is, determining first what the desired outcome is, then selectively gathering data that supports that “conclusion” to the exclusion of other more feasible alternatives.

In accounting, this is known as cooking the books.  In social psychology, it’s related to what’s called confirmation bias.  You decide what the answer should be, then you backfill and/or selectively choose the data to support that answer.  You may even need to twist, distort, recast, or spin the data — fill in your favorite variation — to meet your needs.

This is, of course, a complete perversion of the admittedly idealistic KVC model, which recommends planning your data collection process from the top down — but not actually shaping the data itself to fit a foregone conclusion.

Analytic integrity

This is what was alleged to be happening with US Central Command (CENTCOM) intelligence about ISIS.  This is serious business, since intelligence is designed to support high-level policy decisions.  As the report so directly states, “Analytic integrity is crucial to good intelligence, and good intelligence is crucial to making informed policy judgments.”  While Congress was being formally told that ISIS had been reduced to a “defensive crouch”, the realities in the morning news indicated otherwise — the frequent made-for-cinema horrors accompanied by the captures of the Iraqi cities of Ramadi (which has since been taken back) and Mosul (which has not, as noted on the map below).isis-territory-sept-2016

We now know that the analysts assigned to the situation knew better, too.  This is because one of them initiated a whistleblower action that resulted in the convening of a US Congressional Task Force to examine his allegations.  That group issued an unclassified report on August 10, 2016.  Just below the surface of the dry, bureaucratic details and gov-speak are some fascinating revelations, chief among them that:

  • intelligence products (i.e., reports) were “inconsistent with the judgments of many senior, career analysts at CENTCOM” and the wider intelligence community;
  • these reports were “more optimistic than actual events warranted”;
  • this was the direct result of pressure brought to bear on analysts to “distort or suppress intelligence” that would be seen as counter to the desired narrative, i.e., of a generally positive trend; and
  • CENTCOM leaders chose to rely on operational reports from forces in the field, “rather than the more objective and better documented intelligence reporting.”

And yet…

The Task Force finally reports being “troubled” that, despite the complaint filed in May 2015 and “alarming [internal] survey results” that followed in December 2015, nothing significant was done by those responsible to correct the situation.  On the contrary, the report notes that the CENTCOM leadership downplayed the significance of these events, calling such allegations “exaggerated”, and so far has not cooperated fully with some of the information requests from the Task Force.

The report details several changes made during 2014 that, though purportedly intended to improve the intelligence process, had the opposite effect of biasing it.  These changes included:

  • introducing a new layer of review into the process, which some analysts resented, and which slowed the flow of intelligence;
  • introducing a fusion center to serve as a round-the-clock focal point for intelligence — but neglecting to make its role, structure, or even existence widely known to analysts;
  • introducing a daily summary report that over time grew in length from a few pages to more than ten pages; and
  • eliminating the standing practice of coordination, whereby analysts have drafts of their work challenged by external analysts prior to being issued.

Culture flows from the top

The report implies that the systemic bias resulting from these changes originated from the then-current leadership of CENTCOM intelligence — described by their own analysts as “risk-averse and unwilling to accept uncertainty in intelligence analysis”.  Dissenting opinions, highly valued in the intelligence culture, were to be discouraged.

In a rare display of optimism, the report notes that, by the time of its release, these leaders had been replaced and certain of the problems addressed.

What would motivate someone to cook the books on something as important as intelligence related to our national security? These are serious allegations that could easily end careers if found to be true.   Though it mentions in passing the successive organizational changes made in CENTCOM at the time, the report stops short of concrete answers — or even speculation about such explanatory details.  Maybe there is also a classified version that contains these?  To be fair, the report does imply that this investigation is ongoing — so we’ll stay tuned.

There are plenty of historical analogues for this that could be instructive.  And certainly businesses are not immune to this — there is an almost cosmological force that propels “happy talk” to the top of the organizational scrum, while too often bad news is suppressed or tweaked beyond recognition.

The Value of Knowledge Makes Headline News

Information has its greatest value when it is most available to, and accessible by, people for immediate use in understanding their world. I not only believe this, I put this insight to work in my consulting and teaching.

To implement this, I often use stories from the headlines to illustrate my key points. There are so many examples illustrating the KVC in the news that I am confident that I can pick up a Wall Street Journal at random and find a real-world illustration of a key point.

I call this technique a “flash case” — since it has the teaching value of a standard business school case — but it has the key advantages that (1) it can be developed quickly and (2) it evolves over time as the actual events play out.

The Deutsche Bank Case

For example, I recently used the warning letters from the NY Federal Reserve Bank to Deutsche Bank (DB) about deficiencies their capital requirements reporting process. Yes, all that detailed, boring, low-level stuff — that can gut the fortunes of enterprises heretofore thought unassailable.

Deutsche Bank logoGraduate students in my audience at Columbia University were able to identify each aspect of the knowledge-value relationship in the case. Much of the discussion focused on this pivotal issue: was this a technology shortfall, or rather a systemic problem in corporate culture originating at the top? More the latter than the former was the class consensus — a view that has been largely borne out by subsequent events.

Around the same time as the capital reporting issues, DB was involved in the LIBOR-rigging scandal, in which several huge banks were found to have essentially fabricated data used to set key rates in the world financial markets. In April 2015 the bank was fined $2.5 billion by US and British authorities for its role in the scandal — more than any other single institution.

These and related issues led to a top-management shakeup at the bank in June 2015. DB’s stock currently sells for 1/3 of what it sold for at the beginning of 2014, and the cost of insuring the bank’s debt has risen significantly — a clear signal that the once-dominant institution is now considered a risky asset.

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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.

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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.)

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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.

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Forward to the Past

I used our recent office relocation to review some files I had not visited in a while. Though an arduous undertaking, it provided some surprising rewards. Among other things, I came across one of my first major projects, done with the great firm of Peat, Marwick, Mitchell — which soon after became KPMG.

Screen Shot 2016-02-10 at 1.41.56 PM

Out client was the Department of Taxation and Finance for New York State. I worked under a business economist, Don Welsch — a brilliant and fun guy, a real visionary.

My job was to develop a model for the economy of New York State, segmented into more than 100 sectors. The State wanted to have a computer model ready to go so they could change sales tax rates (read:  raise taxes) in selective categories if they needed to rapidly plug a revenue gap.

I had in effect become the firm’s reigning guru in sales tax during a previous engagement, and before that had studied data analysis and linear modeling at Yale. This was a unique opportunity to leverage both cognitive streams.

Things were different then

It hardly needs saying that many aspects of this assignment were much different then than they would be today. It was the Digital Dark Ages! (Though of course we didn’t know that at the time. We were just trying to get our work done in the best way possible.) The Internet was still a dozen years in the future. Personal computers were just barely on the horizon, and nowhere to be seen even at well-funded firms like KPMG.

Don had a relationship with Chase Econometrics, a timesharing service that we used for time-series data by dialing in on a TI Silent 700 terminal through an acoustic coupler, into which we plugged a landline phone. Results came back to us dot-matrix printed on proprietary thermal paper at a blazing 300 characters per second.Screen Shot 2016-02-10 at 1.53.17 PM

That was a quick sprint through the infotech graveyard — but, for its time, our process was pretty high-tech. The Silent 700 was one of the first portable terminals to achieve major commercial penetration, so we could work from an office (ours or those of our client), rather than have to go to a data center. Many of our consulting colleagues regarded us as futuristic space cadets.

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Piercing the Enterprise Bubble


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.)

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The Competitive Runway

I read the following headline recently in the Wall Street Journal:  “Consumers crave [PRODUCT], but [PRODUCERS] enjoying their best profits ever are reluctant to switch.”  (The words I’ve bolded here were specified in the article, but I’ll get to that in a minute.)

Headlines reminiscent of this have been written many times in business history.  They are often prelude to disaster in the form of self-imposed obsolescence.


Regarding Kodak, for example, one might in the late 20th century have written [digital technologies] and [film manufacturers] in the respective slots.  The profitability on film was so great that Kodak persisted in making and selling it, and famously did not invest soon enough in a switch over to digital. This was a titanic strategic blunder from which the company never recovered, eventually filing for Chapter 11 bankruptcy in 2012.

Willful ignorance

It happens constantly, in all industries, that consumer preferences migrate — sometimes so slowly that it’s hard to notice — until the change has become the new normal.  It’s more noticeable in B2C industries than B2B, but it happens in the latter too.

What makes these changes especially difficult to respond to is our near-universal tendency to gloss over and ignore that which could be unpleasant — or even fatal.  Our “all good news, all the time” corporate cultures make it tempting to look the other way and hope such problems will resolve themselves before metastasizing.

On the other hand, companies that have an innovation philosophy that demands that they “Obsolete ourselves before someone else does” have the upper hand.  Intel and Jobs-era Apple were famous for thriving under such regimes of continual, relentless self-betterment.

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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.

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    COMPETING IN THE KNOWLEDGE ECONOMY is written by Timothy Powell, an independent researcher and consultant in knowledge strategy. Tim is president of The Knowledge Agency® (TKA) and serves on the faculty of Columbia University's Information and Knowledge Strategy (IKNS) graduate program.


    "During my more than three decades in business, I've served more than 100 organizations, ranging from Fortune 500s to government agencies to start-ups. I document my observations here with the intention that they may help you achieve your goals, both professional and personal.

    "These are my opinions, offered for your information only. They are not intended to substitute for professional advice."


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