Competing in the Knowledge Economy : The Knowledge Value Chain (KVC)

When I first got to Yale, I was struck that our motto LUX ET VERITAS was an extension of Harvard’s Veritas.  I used to kid people that Yale was obviously twice as good—you got all the same Veritas, with the 100% added bonus of the Lux.  Whatever that was.

Product differentiation

Later I came to see this as a marketing strategy on Yale’s part—pure product differentiation.  Harvard was the first colonial university in 1636, and claimed the all-time killer motto, Latin for “truth”.  Coming along in 1701, Yale had to differentiate its ‘brand’ somehow.  It did that by anchoring with the “et Veritas” part, and adding the “Lux” (light) up front — kind of a new improved ingredient for the upstart rival.

It wasn’t until years later that I gave much thought to what the words mean.  I still believe that Yale has the better motto—for much different reasons than I did then.

Fact and insight

Years ago I heard an ‘insight about insight’ that stuck with me.  At that time, there was a big fat ‘phone book’ distributed every year with the names of several million telephone-owning New Yorkers (like every other phone district in the US.)  Some witty speaker made the point that this book contained 100% facts—but absolutely nothing in the way of insights, or enlightenment, whatsoever.  No summaries, no analysis—it was, in other words, 100% Veritas and 0% Lux.

Lux = light = insight

Fast-forward a couple of decades.  Much of what I deal with now as a consultant is getting my clients to understand that VERITAS is necessary—but not sufficient—in an intelligence product.  They must have both.  Their job as intelligence producers is not done until the LIGHTbulb goes on— until someone has an insight, an ‘aha’ moment.

And not just any someone.  It needs to be someone with power—position, authority, budget, mandate, and so on—to act on the intelligence.

That idea has become part of our model and methodology, The Knowledge Value Chain®.  At the bottom of the chain is the Veritas—the truth people.  At the top are the power people, in whose minds ‘light’ must shine before decisions are made and actions are committed to and executed.

Looking for light

This is a theme common to many organizations.  What used to be called ‘market research’ in many companies is now called ‘market insights’, or words to that effect.  It’s not just a semantic change— it’s a reflection of the fact that executives seek insights, not just information, in order to make decisions.

Research groups often struggle with this.  Typically any mandate to change comes from above.  A.G. Laffley, when he became CEO of Procter & Gamble in 2000, famously told his top executives to get out from behind their printouts and presentation decks, and talk directly with customers.  This was his way of generating more Lux amidst the mountains of Veritas.

View from the academy

There’s a branch of experimental psychology that deals with insight. There’s general agreement that the process of insight, though related to the process of ‘learning’—ingesting and absorbing information—differs from it in key ways.  Much of the research on insight has to do with identifying what those differences are.  For example, while learning is typically methodical and process-driven, insight is often sudden and unpredictable.  The implication is that insight can be driven by the disruption of a normal thinking process—shaking things up mentally, as it were.

So what?

Much of this research on insight focuses on how it applies to solving problems, and this also might provide us with a clue as to the difference between information and insight.  While much corporate research consists of information being shoveled out (‘supply push’), insight has to do more with solving problems (‘demand pull’).  I’m always amazed how easy it is to read a software manual when I need to figure out a problem—and what a drag it is to read just for its own sake.

Corporate research would do well to focus on the problems and challenges that their clients need to overcome.  Information used in solving problems clearly adds value and has ‘stickiness’, while information disseminated in a vacuum often does not.

If you’re an intelligence producer, remember to sprinkle some Lux on your Veritas before serving.  If you’re an intelligence user, demand it.

Last time we looked at where our health care funds in the US are spent.  At more than one-sixth of our GDP, it’s undeniably a huge factor in our financial lives.  Who pays for all this?  Ultimately, of course, we all do—but the mechanisms by which this happens may surprise you.

Since non-personal spending ($407 billion) is accounted for somewhat differently at the federal level, our focus here is just on the $2.2 trillion in Personal Health Care (PHC) spending in 2010.  A similar pie chart as in the previous post, but this time broken out by funding source, looks like this (figures in millions):

About one-third of PHC spending ($746 billion) is by private health insurance companies—Aetna, ‘the Blues’, Cigna, Humana, United, Wellpoint, and smaller companies.  Medicare, federal health insurance for the elderly, accounts for nearly a quarter of spending ($494 billion).  Medicaid, a joint federal-state program for the poor and disabled, pays for $372 billion.  Of that, about two-thirds is federal, one-third from state and local sources.

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Everyone knows health care is expensive, and is a significant part of our individual and collective budgets.  How expensive, exactly?  And how is that money spent?

In 2010 we in the US spent $2.6 trillion on health care. That’s 2.6 with twelve zeros behind it, or 2.6 million millions if (like me) you get lost in the zeros.  That comes to about $8,500 for each US resident, and more than $11,000 for each adult over age 18.

GDP Data (IMF as reported by Wikipedia)

This is nearly 18% of our $14.5 trillion Gross Domestic Product as a country.  For perspective, it’s larger than the entire US durable goods manufacturing sector ($2.3 trillion), which includes, among others, the computer industry ($377 billion) and the auto/truck industry ($360 billion).  If US health care were its own country, it would stand at number five in the world, behind Germany and just ahead of France.

Where does it go?  The following chart summarizes our outlays of funds (in millions):

Five of every six health care dollars are spent on Personal Health Care (PHC) — the professional direct care we actually ‘consume’ when we visit the doctor or take our medicine.  The other categories, together about one-sixth of health spending, represent much of the ‘overhead’ of health care.  Administration ($176 billion) includes state and local government program admistration expenditures, as well as the portion of private health insurance that does not directly pay for care.  Public Health activities like vaccinations and disease prevention make up another $82 billion.

Structures & Equipment ($100 billion) includes new building construction and capital equipment.  Research ($49 billion) represents non-profit and government activities like grants for medical research.

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In the interest of full disclosure, this post is not about algebra, nor is it about financial instruments.  It’s about various kinds of business research ‘raw materials’ and how to discern their quality if you are a producer or user of such research.

Whether you are navigating the waters off Tuscany in a cruise ship, or analyzing a new business opportunity, the quality of the data you use is integral to the outcome you will deliver.  While the availability and use of high-quality data does not guarantee a desired outcome, the absence of it renders the desired outcome more a result of chance than of strategic intention.

Manufacturing re-thought quality

When I originally developed the KVC model, it was partly a test to see how well best practices in manufacturing could be applied to organizational intelligence.  In so doing I borrowed heavily from earlier work I had done in TQM (Total Quality Management).  One of TQM’s most basic principles is that in any manufacturing process, it is no longer acceptable to just get to the end of the process — the finished goods — and weed out the ones that do not meet quality standards.  Instead, it is much more efficient to build quality checks into each stage of the process, reducing the number of adverse quality-related surprises at the end of the process.

Intelligence can, too

So it is with intelligence — the ‘manufacture’ of a knowledge-based product.  One of the implications is that each step up the value chain tends to replicate (and in my experience, amplify) any quality shortfalls embedded in earlier stages.  ‘Garbage in, garbage out’, as the expression goes — though we prefer its converse, ‘Quality in, quality out.’

Another implication is that wherever possible, in developing your source data, you go to the lowest possible level on the chain at which it’s available.  If information and intelligence are the branches and leaves of the ‘knowledge tree’, then data points are the roots.  You want to get to the ‘root-most’ level in order to start your analysis.

To the extent you accept at face value someone else’s processing or analysis of the data, you may save yourself some analytic time and effort — but also you leave yourself open to inheriting and building on their errors.

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