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

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.

One of the ways this happens is when people rely on second-order ‘derivative’ work at the start of their process.  This is work that ‘derives’ from another, or (in Knowledge Value Chain terms) enters at a higher level in the chain.  For example, an analyst may cite a newspaper article about a new study that has been released, instead of citing the study itself.  This is usually because he hasn’t taken the time to find the root source, read and analyze it, and draw his own conslusions.

The result is a data quality vulnerability — one that’s typically easy to overcome.  When you see a newspaper article cited that refers to a study that someone is announcing, it is essential to (where possible) obtain the original study, read it critically, and do your own analysis and interpretation of the results.

Apples, oranges, and pineapples

I’ll give you a recent example from my casebook.  We have been studying various issues related to heath care for more than a year now.  Most recently, we’ve done work around the aging of the population and what market opportunities and challenges it presents.  One of the unusual things about this assignment is the sheer volume of high-quality material readily available.  The US government publishes lots of data — the Census Bureau, Centers for Disease Control and Prevention, and Center for Medicare and Medicaid Services were especially useful to us.  In addition, many universities have centers that study health and/or aging, and there are not-for-profit groups (like the Urban Institute) that also do so.

In short, instead of a dearth of data, there are so many sources that we were still discovering new ones more than ten weeks into our study.  And of course, none of them totally reconciled in terms of their target population, what they measured, how they measured it, when they measured it — we were dealing with apples, oranges, and pineapples in terms of the comparability of our data.

The case of the poisoned pie chart

At the beginning of our aging study I found one source that was especially useful, a monograph published by a leading not-for-profit research organization.  Its source list was a rich trove of ‘root’ sources that we culled to build a ‘knowledge base’ for the market opportunity we were evaluating.

One of the key facts we were looking for was the breakdown among funding sources for elderly long-term care — Medicare, Medicaid, private insurance, self-pay, and so on.  The monograph contained an analysis and informative pie chart, but — as I’m recommending here — we had also obtained the source material cited (in this case, National Health Expenditures data produced by the US government).  We do this as a matter of course, not so much for checking on our sources, but more as a way of digging more deeply into the source material, and to develop still more sources from their citations.

In this case I could get the total figures to agree with this authoritative derivative source, but not the allocations among these sources.  After spending an hour or so trying to reconcile the difference, I went back to the monograph and read the part of the text in which it explained the pie chart.  There, to my amazement, I found the breakdown very similar to the one I had developed independently.  As far as I can tell, there had been an error made by the person (quite possibly not the author) who created the accompanying pie chart.

If I had used only the ‘derivative’ source, I would have given an incorrect result to our client — complete with a citation to a ‘reliable’ source.  Only by finding a ‘root’ source and checking against that was I able to deliver a result that met our standards of data quality.

It’s not totally clear in this case that an incorrect answer would have significantly affected our client’s decision to pursue an entry into this market.  But in this case — as is often the case — the cost of higher-quality data was not much greater than incorrect data.  So having the best you can get becomes a good work habit to develop.

Cheap gas in a BMW

If you were driving a new BMW M3, would you put low-grade gasoline and motor oil into it?  Unlikely, since the value and usefulness of what you have could be degraded significantly by doing so.

Yet companies do this all the time – put bad data into a highly-tuned decision-making machine — often without knowing it.  The quality of data is not always immediately obvious to its end user, since by that time it has been transformed by the ‘intelligence manufacturing’ process.  (It’s usually clear to me where the vulnerabilities are, but that comes with having done business research for a long time.  I’ve seen most of the mistakes you can make, and made a fair number of them myself. )

I’ll give you some of the signposts I use for data quality in a future post.  But one of the things that immediately starts me checking is the use of derivative sources, where the roots are available.

I just listened to a fascinating webinar in which five authors recounted their experiences, both personal and professional, with information overload.  One of the speakers, Jonathan Spira, reports that he has measured this phenomenon, and that it costs the US economy over $1 trillion per year!

Shifting the blame

But in naming the phenomenon ‘information overload’, it seems to place the blame on the information—not on ourselves, where it belongs.  People bemoan the distractions offered by ubiquitous information devices like smart phones and tablets—similar to the complaints that were made when the telegraph and the printed book were introduced!

This seems to us like blaming obesity on food.  “Gee, there’s so much food out there, if I ate it all, I’d get really fat.”  (Yes, you would…and with an obesity rate running over 30% in many US states, some people appear to be trying to do just that.)  If we were to talk about obesity as a food overload, it would sound pretty silly.  Though our general abundance is certainly an enabling factor, most people realize that you have to eat intelligently and selectively to stay healthy.

Maybe what we’re all experiencing is better described as collective attention deficit, or of being focus-challenged.

The information metabolism

Organizations have this problem too, and typically don’t fare much better.  In my article “The Information Metabolism” (Competitive Intelligence Review, Fall 1995), I compared the intake and processing of information to the intake and processing of food—eating and digestion. Though my tone was whimsical, I was only half-kidding.   I believe they are closely analogous, as my article described:

Some organizations are ‘information starved’…Others are ‘fat’ with information—they acquire it, but can’t use it effectively to create value.  Still other organizations ‘binge’ on information—they get lots of it at certain times (like strategic planning season), but not enough the rest of the time.

This was written at the dawn of the Internet Age, and the situation has accelerated dramatically since then.  I’ve seen this up close in companies, and it can be quite distressing.  Some are literally awash in so much data that it erodes their ability to process and use it effectively to manage their business.

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If you have any experience with investing, you know about rebalancing your portfolio.  Every so often—at the end of every year, say—you need to reassess your investments.  Some may have grown, such that you’re too heavily invested in a particular stock or sector in the economy.  In other areas, you may find that you have less invested than you would like, given the prospects for another higher-potential sector.

Why do we need to do this?  Because the world changes, and the asset mix may no longer meet our needs.  I may feel that the economy is risky, and I’d rather be more invested in safer things like bonds than stocks.

In addition to the ebb and flow of securities prices, my needs may also change.  I may need to take out money for my child’s education, or for other unexpected expenses.  I may feel that I’d rather have more insurance.

The knowledge portfolio

It’s much the same way for organizational information and research assets.  They are financial investments—not costs—and each one brings to the organization a return in the form of optimized decisions and business results.  I find it helpful to think of all the research, information, and intelligence in an organization as its ‘knowledge portfolio’, with each research initiative, report, and staff position representing a specific asset in that portfolio.

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Stop me if you’d heard this one.

“[COMPANY] is in the final stages of preparing a bankruptcy filing, clinching a long fall for a company with humble beginnings that helped change the way Americans buy [PRODUCT], but failed to keep pace with the [CHANGE] rocking every corner of the [INDUSTRY] landscape.”

Today (February 12, Wall Street Journal) the missing words are ‘Borders’, ‘books’, ‘digital transformation’, and ‘media’, respectively.  Tomorrow there will be another company in another industry that pays the ultimate price for not “seeing” what has been going on for some time.

I put seeing in quotes because seeing has two major dimensions—perceiving what is happening at an organic level on the one hand, and incorporating it into a world view and subsequent actions on the other.  Social scientists have long documented that cognitive dissonance, the human mind’s ability NOT to perceive what it finds contradictory or threatening, is a powerful force.

We like to think that organizational intelligence is the eyes and ears of the organization, and in a very literal sense it is just that.  But what the eyes and ears capture—physical sight and sound—must be processed by the human brain in order to yield meaningful images.  Likewise, so must relevant signals in the competitive environment not only be swept in by the organization’s eyes and ears, but also processed and analyzed by a sense-making and action-taking capability.

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“I love you, you’re perfect…now CHANGE.”  That’s what the market in effect continually tells excellent companies—because the world always changes around them.

Companies tend to do best today what they did best yesterday…not what they’ll need to do best tomorrow.

In working with companies of various sizes, in various industries, I’ve noticed that the most significant strategic challenges are those brought on by some fundamental type of industry change.  Anticipating and adapting to industry change is at the heart of what competitive strategy is.

I’ve also noticed that not all industry changes are of the same magnitude or carry the same degree of strategic implications.  I’ve begun informally categorizing these changes with regard to their impact on industry players.  We’ll call these, in ascending order of how difficult they are to manage successfully, Levels 1, 2, and 3.

Level 1 industry change – “New kid in town”

In a Level 1 industry change, a major player enters or leaves the industry.  This could be a major buyer, a major competitor, or a major supplier for the industry.

Examples of Level 1 change abound, and you can usually pick up a financial journal at random and find one.  I just now did exactly that, and find two pages into my search an article reporting that Warner Music Group is selling its music publishing business (leaving one less player there), in hopes of clearing anti-trust to be able to buy the foundering EMI (leaving one less player there as well).  EMI owns, among other assets, the master recordings by my all-time favorites the Beatles.

Level 1 changes can require significant adjustments in any industry.  However, relative to other types of industry change, a Level 1 change impact on existing other players is low.

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I recently had a conversation with the publisher of a start-up niche business-to-business magazine.  We had pointed out several things that we felt were opportunities for him.  For one, our view—confirmed with some informal research—that the “look and feel” of his product was not what it could be, and that with a small investment, he could bring it up to the much higher standard we feel he needs.

His reply was that, even though he’s always interested in making improvements, they weren’t needed here.  The reason?  He had just met with his chief rival, the CEO of another similar publication, and they both had agreed that my client’s product had the better approach.

This illustrates perfectly what in my experience is one of the most common hazards of conventional competitive intelligence—one I’ll call competitive myopia. Competitive myopia is defined as a focus on a competitor or rival that is so intense that it causes distraction from larger strategic threats or opportunities.

Competitive myopia results in a mindset that says:  If we just manage to beat the other guys, we’ll do fine in our market.  “I don’t have to outrun the bear,” goes the joke about the two men meeting the hungry bear in the woods.  “I just have to outrun YOU.”

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I speak often with Eric Garland, a leading futurist, thinker, and blogger.  We typically have a free-ranging and—to us, at least—entertaining and enlightening conversation.

This time he recorded it.  Here are some of the notes we hit:

• Businesses have always wanted and needed to know about each other’s activities.  Until the 20^th century, this was mostly handled by direct conversations among business leaders.

• The conditions that created the need for modern competitive intelligence in the US began to be laid by the anti-trust legislation of the early 20^th century.  When businesses were legally prevented from sharing information, they had to devise some other way to obtain it.

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I recently managed to get the Knowledge Value Chain® boiled down to an eight-minute introductory video—then had a couple of people comment that it should ideally be half that length!  So here, for you the time-challenged, the short-attention-spanned, and the just plain busy, are the key things that—if I had two minutes with you in the proverbial elevator—I’d try to make sure you came away with.

• Creating value from data is a process. It takes time.  It has various elements that must be coordinated.  It may not be immediately obvious how to do it—it takes planning and practice.  The good news is that you can learn the process, and you can improve at it.

• Each step in the process has a cost and benefit added. The skill is in keeping the latter higher than the former by minimizing the costs and barriers to value, and by maximizing the benefits.  If you can do that at each step of the process, then the whole process itself will have a positive ROI.

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In a recent post Surfing the value waves I mentioned having purchased an iPad. Here’s the value calculation that drove me to buy it.  Of course it’s cool and fun—but does it make economic sense?

It first occurred to me in discussions with my wife—who wants a big, flat, thin TV like all our friends have—that we could EACH get an iPad, skip the TV, and pocket the change.  The iPad is not quite “there” in terms of programming, but for what I mainly watch (movies and sports), it’s halfway there (the movies half.)  TVs have gotten so cheap, this was actually about break-even—so there wasn’t a lot of change to pocket.

In this sense the iPad (and the Internet in general) is a textbook example of a disruptive rival to the TV—not as good in many respects, definitely cheaper—but with the future potential to knock the whole competitive game into a different orbit.

But then I started adding up the other gadgets that an iPad could replace.  I should mention that while I do not own all of these, as a “weekend warrior” musician and photographer, I own more than most people.  Your mileage will probably vary.

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One recent Tuesday (June 29^th) first thing I got a very excited e-mail from my marketing colleague and friend Professor Plum.  “Did you see the thing in the [NY] Times about the Russian spies who were arrested?”  (Indeed, I had read it earlier with great interest.)  “I know one of those guys, Don Heathfield…he works with a business development firm I’m also working with.”

Interesting coincidence.  Being the research guy I am, I looked up Heathfield and found that his company had a product FutureMap that sounds similar in some respects to a product we’ve been working on at TKA—something to monitor trends and events that could affect a company.  I emailed my futurist colleague and friend Colonel Mustard a link with a “Check this out!” subject line.

Within five minutes Mustard called me back.  “You’re not going to believe it, but I know one of these guys—Donald Heathfield.  I met him through the World Future Society.  He was always asking me for introductions, and to have my clients try out his software.”  We had a rollicking conversation during which Mustard pointed out the many things in Heathfield’s story that didn’t quite add up.

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