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

I’m a lucky guy.  Nearly every day I walk between home and office along the Hudson River, just north of where it widens out into New York Harbor.  As an amateur photographer, I have begun to pay closer attention to — and often photograph — the scene (as below).

Each day the sky as the sun sets over New Jersey is different.  Sometime clear, sometimes cloudy, often mixed — with many variations in cloud types, formations, heights, and so on.  Each day the river water is different — sometimes calm and almost glassy, sometimes choppy and almost ocean-like, with thousands of variations in between.

The tides create a 4-5 foot variation in river height on a typical day, as well as variation in the direction and interactions of the channel flow and the surface texture.  Sometimes the air is still, sometimes pleasantly breezy, sometimes downright windy.  Each day of the year, the sun sets in a slightly different place.

In the five years I’ve been doing this, I do not recall seeing the identical sky-water combination more than once.  There are simply too many factors that change over too wide a range, and that interact in complex ways to see much repetition.  The building and piers are the only relative constants — and sometimes those change too.

New York Harbor is an open, dynamic, complex system, with an innumerable number of factors interacting continually.

What the flux?

This reminds me of the pre-Socratic Greek philosopher Heraclitus’ concept of flux – most famously depicted by the realization that you can never step in exactly the same river twice.  By the time you do, it has changed.

To me, it also stands as a useful metaphor for our economy.  With economic affairs, we are always in flux.  In my strategy and intelligence work, I remind my clients that their ‘economic universe’ today at 4pm is not the same as it was yesterday at 4pm, or the day previous.  Things have changed – possibly in imperceptible ways — but possibly in ways that over the long run will have a noticeable impact on their organization’s performance and even viability.

Perhaps even more importantly, at 4pm tomorrow, and the day following that, the same will be true — the world will be different – perhaps just slightly so, but different.  Change is the only constant in our economic universe.

Shift happens

Our whole system of economic thought is built largely on principles borrowed from ‘classical’ physics called Newtonian mechanics.  As many of us know it:  for every action, where is an equal and opposite reaction.  Gravity.  Equilibrium.  Deterministic models that predict that when X happens (for example, you throw a ball), Y follows as a consequence (it travels some distance, then hits the ground and bounces).

The physics of the 20th century disrupted that Newtonian model when Einstein, Bohr, and others introduced quantum mechanics — such that extremely small objects like electrons were no longer seen as fixed in space, but rather as positioned probabilisticly within a range.  And always in flux.

It’s possible that our economic modes of thinking are stuck in the 18th century and have not yet been re-calibrated to include 20th century insights like quantum theory.  And perhaps they don’t really even include 6th century BC concepts like flux.

A basis in stasis

Certainly this is the case in competitive analysis.  In a recent LinkedIn discussion on models of strategic analysis, I argued that ‘industry analysis’ based models (like Porter’s ‘Five Forces’) tend to be static and to underweight dynamic considerations in the economic environment.  My view is that the reason some people are now reacting against these models (Columbia professor Rita McGrath, for example) is that we are now confronted with continual and undeniable change and volatility in nearly aspect of our economic lives.

Major financial crises occur once or twice per decade.  The average stock is now held by institutions for four months — in 1960, the average was eight years.  Instead of equilibrium, in effect we now have ‘cascading disequilibria‘ — such that no sooner do we adjust to a ‘new normal’, than it’s displaced by an ‘even newer normal’.

Organizational strategists seek ‘models’ that closely track reality, such that they can be used to forecast what might lie ahead.  To extent these models are consistent with the underlying reality, they are deemed valid and useful.  To the extent they differ from reality — either at the outset, or over time as the represented reality continues to evolve away from them — they are decreasingly useful.

A different world

Many of our strategic models still used today were created in the period 1965-1985. This was during the Cold War, when nuclear standoffs were the ‘game theoretic’ endpoints discussed by strategists like Thomas Schelling and Herman Kahn.

This period was also pre-Internet, when industry boundaries seems less fluid, people’s buying habits seemed more stable — and everything just seemed to move more slowly and predictably.

Our strategic models now seem like ‘messages in a bottle’ from a competitive world significantly different from our own.  There is growing and justifiable concern that they have outlived their relevance and usefulness.

Quantum strategy

We need ways to monitor and measure the continual changes in the ‘economic universe’ of an entity (a company, for example) over a period of time, so that we can forecast – or at least make educated guesses about — the direction and magnitude of those changes in the future.  So as not to inundate us with more data, this requires knowing which of these factors are most important.

I am not advocating a macro-futures scenario development exercise a là Naisbitt’s Megatrends or Gore’s The Future — though these have their place.  What I’m proposing would focus on micro-futures — movements in more local drivers that together create significant impacts on the entity being modeled.  It would be easily communicated to, and digested by, decision-makers whose job it is to produce value and results using the output.

This will enable us to finally drag strategy into the 21st century — from the Newtonian era to the quantum era.  Quantum strategy is real-time, evidence-based, algorithmic, and probabilistic.

Is strategy in your organization based on outdated models?  Is it a once-a-year ordeal, the results of which are obsolete almost as soon as it’s finished?   Strategy should be a management tool for making decisions and guiding actions in a competitively dynamic world.  TKA has been working on something like I’ve described here;  watch this blog for an announcement soon.

Back in the 20th century, it became fashionable in business strategy circles to speak of ‘first mover advantage’.  This idea — that if you got there first and staked out a market, it would forever remain yours to dominate — was used to justify funding business plans that would have otherwise seemed, at best, sketchy.

First-movers fall short

Then people began to notice that this is not always how the world really works.  In technology, we could see this evolution in fast-motion.  The ‘first movers’ in online user-to-user communications were MCI Mail and CompuServe.  They were eclipsed (and the latter was bought out) by AOL, which in turn became the vehicle to buy the largest media conglomerate in the world (Time Inc.) — but whose more modest recent successes have come by reinventing itself as a ‘brand company’ (whatever that is).  Google and Microsoft now dominate those categories, which have in effect become loss leaders for their other businesses.

Visicalc was the first dominant electronic spreadsheet — until Lotus 123 arrived, which in turn was hugely successful — until Microsoft Excel came along.  Palm was the early leader in what we now call ’smart phones’ — and now struggles as a division of HP.  Ashton-Tate was the king of database software — a category that has since effectively disappeared entirely.  Similar happened to just about every early tech mover.

Then, somewhere along the line, true innovation started to develop a bad reputation.

Fast followers follow

Soon the idea of intentionally being a ‘fast follower‘ began to gain adherents — then began to move toward canonization as a smart, modern business strategy.  Articles started being written on it.  ’Everyone knew’ that Microsoft, poster child for the concept, became what it was largely by copying others and by buying existing innovations that others had created — Windows largely copied the Apple Macintosh, and they bought Word and even the original DOS from others.  Microsoft’s subsequent successes helped legitimize ‘fast follow’ as a strategy to be seriously considered.

The canon goes, “Let them make the investments in R&D. Let them create the markets.  Let them make the mistakes.  Then we’ll just adopt what they do.”  In poker logic, this goes, “We’ll see you and (maybe) raise you one — then we’ll reap most of the return without making much investment.”  Put that way, it seems to make sense.

However, I submit (though few dare say it) that it’s also because it’s easier than determining what the market wants and will want in the future.  It becomes an ‘E-Z Strategy Finder’.  There is even a whole management discipline (competitive intelligence), aspects of which tacitly encourage and thrive on this (often hidden) assumption/myth.

Read the rest of this entry »

The late Ted Levitt, professor at Harvard Business School, used to tell this apocryphal story:  The CEO of a tool manufacturer gathers his executives into a meeting and says, “People, I have bad news.  I’ve discovered our customers don’t want quarter-inch drill bits — they want quarter-inch holes.”

What we sell vs. what they buy

In my view, that would count that as good news — since the executive has figured out that there is an important distinction between what our customer buys and what we make.  Your company makes and sells a product or service; your customer buys value – a benefit as he defines it.  They may be the same thing — your product may completely (or at least sufficiently) satisfy the customer’s need.  But when they diverge, you’re in trouble if you don’t realize it and adjust quickly.  Some companies never catch up when such a value shift occurs.

To play out Levitt’s example:  You make and sell a ¼” drill. What your customer needs and buys is ¼” hole.  Hypothetically there are other ways to get that ¼” hole:

• hire a handyman who has a drill;
• buy a pre-drilled piece of lumber; or
• buy something that can be assembled with clamps instead of holes, thereby doing away with the need for holes altogether.

The point is that, on a value basis, these are also your competition (in addition, of course, to the other drill makers.)  You should keep them on your radar, and maybe even borrow from what they do to compete with yourself — and thereby pre-empt them from doing so.

A current example:  the PC industry

Companies and whole industries make this mistake over and over.  A textbook example is currently playing out in the personal computer industry.  Personal computers as we know them became common business tools in the 1980s.  The growth figures roared along, and with them star companies like Dell and Hewlett-Packard.  Both of these companies now seem to have lost their bearings, and could now be seen as fallen angels — largely due to the shifts in technology that went on around them.

Read the rest of this entry »

My colleague Robert Reiss studies CEOs — especially their successes and failures — in order that others might benefit from them.  His Internet show The CEO Show produces interviews from these CEOs on leadership, and how they make their organizations ‘tick’.  His book (with Jeffrey J. Fox) The Transformative CEO (McGraw-Hill, 2012) gathers some of their stories, principles, and accomplishments.

Robert characterizes the CEO’s primary job as getting the organization from here to there.  Whatever resources are required to do that, it’s his or her job to make sure they’re in place — fully resourced, focused, and firing on all cylinders.

Robert sensed intuitively that the CEOs he interviewed on his show were adding substantial value to their organizations.   But was what seemed subjectively true also objectively verifiable?  When Robert wanted an independent test of his hypothesis, he turned to The Knowledge Agency®.

The TKA test

TKA devised a test using a widely-accepted benchmark — the change in the stock price during their respective tenures as CEO.  But market conditions were very different over the differing time frames we tested — even staying flat in a down market like that of 2008-09 would be judged superior. To adjust for such variations, we gauged the results against the return from the overall market, as measured by the return on the S&P 500.

So our benchmark metric was stock price return in excess of the return from the S&P 500.  We tested the eleven of Robert’s ‘transformative’ public companies having market capitalizations over $1 billion.

The envelope, please

The results were astonishing.   As shown in the table, the median gain was 44 percent over the benchmark, with the range from a slight loss against the benchmark to an excess gain of more than 5400 percentage points.  These results were for periods of time ranging from 5-23 years.

Only one company (Xerox) did not beat the market over the CEO’s tenure — and on further examination we found that in fact it did for most of that CEO’s tenure, until the sharp recession of 2008-09.

Click here to download a short article from The CEO Forum magazine based on TKA’s Transformative CEO study.

Read the rest of this entry »

‘Big data’ wants to choke your organization. Don’t let it happen.

What is big data?

The McKinsey Global Institute report Big data:  The next frontier for innovation, competition, and productivity (May 2011) is a relatively well-informed and hype-free description of the opportunities presented by Big Data.  They define Big Data as “datasets whose size is beyond the ability of typical database software to capture, store, manage, and analyze.”

It’s important to note that they define it in terms of a functional capability (the last five words of their definition), rather than any absolute in terms of dataset size.  If we can manage and analyze it, by their definition it’s no longer ‘big’.

I’d argue that McKinsey’s definition does not go far enough, and that we should actually be concerned with doing something value-productive with the resulting analysis — an argument I’ll develop further below.

How big data works

What we used to call ‘reality’ has now morphed from ‘analog reality’ to ‘digital reality’.  Nearly everything has digital sensors on it — all this data goes into one big pool — it’s similar to what we used to call data warehousing, except this time on steroids.

Then various metrics are captured and analyzed for correlation.  When certain factors move together with a certain level of statistical reliability, they are said to be ‘correlated’.  It’s tempting to assume they are also related in some causal way — but this is often premature at best.  What is needed in addition — and it’s a big methodological step — is a mechanism of causation.

Big Data is essentially a data-up approach.  The economic business case for it rests on the fact that data collection and storage have become relatively inexpensive – often wholly automated through collection sensors, telecommunications, servers, and so on.  The rationale continues that if it incurs little cost to collect, it’s best to collect it just in case — even if the reason for doing so is initially unclear or non-existent.

The hidden fallacy

This rationale behind Big Data sounds reasonable enough — but there’s a catch, a big one.  The central problem with this logic is that it neglects to price in (i.e., it treats as ‘free’) the most costly and rare resource in the ‘knowledge value chain’ — the human attention and processing needed to convert the analyzed information into decisions and actions.  Without that (so the KVC model says) there is little possibility of creating value, however that’s defined by your organization.

If the human processing element were built into the equation, the ROI would look much different.  Then it would become clear that just because you CAN gather some data, does not necessarily mean you SHOULD from a cost-effectiveness point of view.

Read the rest of this entry »

In my previous post I told the admittedly harrowing story of my near-demise in a flood caused by ‘Superstorm’ Sandy.  I consider this a personal intelligence failure of epic proportions.

Why would I — who sell my research and advice to companies on things related to threat, early warning, and shifting trends — publicly acknowledge this personal shortcoming in my own behavior?

You could say it’s part of the confessional ‘new intimacy’ in journalism.  But frankly I sense that my experience, far from being atypical, is characteristic of the way individuals and organizations (groups of individuals) misuse ‘intelligence’.  These are mistakes most of us make much of the time.  Only if we can harvest a little insight about why they happen, can we begin to cut down on them.

In other words, I had inadvertently turned my life into a field experiment in applied intelligence — albeit one completely uncontrolled.

A KVC analysis

In my monograph “The Knowledge Value Chain:  How to Fix it When It Breaks”, I put forth the idea that intelligence, far from being a ‘cycle’ as often portrayed, is actually a linear connection that leads from data up through intelligence and up through the production of value.  One of the axioms of the model is that it’s essentially serial in nature—each step must be adequately fulfilled in order for the whole process to work.  If a link in the chain breaks, the chain itself breaks.

In this case, the bottom of the chain, DATA, was very much in place:  I had plenty of data.  As background, I had seen the movie An Inconvenient Truth when it first came out, and will always remember the scene simulating the overflowing of the Hudson River into downtown Manhattan (which is where I live, and which is essentially what happened.)  More immediately, all day on Monday, October 29, 2012 there were Twitter feeds (especially from @EricHolthaus) about where Sandy currently was, and what the effect on tidal levels in New York Harbor was likely to be.  In hindsight, these alerts were amazingly accurate.

Read the rest of this entry »

I was nearly killed during the drafting of this post.  I hope that some of you out there in Blogland will benefit from my experience.

Part I:  Organic Intelligence

West Greenwich Village, New York City—Monday October 29, 2012 4:00pm ET.  As I draft this, I’m in the middle of readying our downtown, Hudson River-fronting New York apartment for Hurricane Sandy, which by most accounts could be the biggest storm here in, well, recorded history.  Big enough that it could bring over ten feet of storm surge onto our street—and into our duplex apartment, the lower floor of which is partly below ground.

What goes through my mind?  I have to prepare to:  (1) make sure I physically survive (my wife is safely out of town, and my kids are both grown men living in safer places), (2) make sure our cats survive, and (3) make sure our most-prized possessions are out of harm’s way to the extent possible. This is the value hierarchy that guides my actions, sometimes coming down to would I rather save this (camera equipment) or that (books).

How fast do I need to prepare?  That depends on many factors—how fast the storm is moving, when and where it will hit land, how it coincides with tides and the moon cycles, its barometric pressure (which is historically low, lower pressure tending to create a semi-vacuum that essentially sucks the water upwards)…lots of factor all changing at once, and all interacting with each other rapidly and unpredictably.

Kind of like real life in the business world.  While I could be chastised for remaking everything into a metaphor for organizational intelligence, in fact that’s the way I most often view the world.

My intelligence stew

I take in lots of information—ranging from satellite photos being streamed on the Internet, to hallway conversations with building neighbors, and everything in between.  My ‘life partner’ Ellen, though back in Atlanta taking care of her mother, is emailing and calling our neighbors to see what they have heard and what they are doing about it.  It’s all a very kinetic, intuitive, non-deliberate process.  I’m ‘playing by sense of smell’.

I weave it all into a story, a real-time dynamic picture of my situation going forward.  Its choices and payoff tables are very organic, given the ‘value proposition’ I mentioned above.  While not (yet) life-and-death, it’s maybe one rung down on that ladder.

It’s more than a picture, it’s a model of how the various causal factors could interact—many of which I can only monitor and react to, but over which I have little control.  For example, it is rumored that Con Edison has decided to preemptively turn off the power—which so far has not happened, but I’m keeping my batteries charged just in case.

I consider all factors within my sphere of awareness. The most obvious of these, direct observation, is too often overlooked.  I just got in from visiting the river across the street, and am encouraged that now (just after low tide), the river is significantly lower, and not nearly as angry-looking, as it was at high tide this morning.  The wind is picking up, though, and it’s raining moderately.

High tide tonight at 9pm will be the real test, when the full moon will magnify the tide, and the storm is predicted to hit land (though farther south of here.)

Read the rest of this entry »

There’s so much talk around here about ‘value’ that I’ve been accused of being obsessed by it.  I plead no contest.

Particularly in these stressed economic times in which we seem mired for the foreseeable future, the ‘quest for value’ is a search that most of us pursue, in both our personal and professional lives, almost continually.

But what is value?  Most of us realize it’s not the same as ‘cost’ or price.  It’s what you get for that cost — the ‘bang for the buck’.  Economists would say it’s the benefit/cost ratio.

My two bucks not to Chuck

I like wine.  One of my hobbies is discovering really good wines that sell for less than $10 — but taste like they’re ‘worth’ several times that.  To me, it wouldn’t be nearly as much fun discovering a great bottle for $100 — too easy — besides which my Scottish heritage would never stand for it.

I’ll never forget our trip to Languedoc-Roussillon in the south of France, where we got delicious local wines in the supermarket — for $2 a bottle. That cured me forever of ‘wine price’ snobbery — but made me forever a ‘wine value’ snob.

‘Value’ implies getting more (of something) for a given amount of money.  A value investor is one who likes to ‘buy low, sell high’.  A value stock is one that seems underpriced relative to its fundamentals and earnings potential.

Thinking like an MBA

‘Value creation’ is the fundamental keystone of our competitive economy — and one of the genuine Mysteries of the Universe.  For me one of the best things about business school was the opportunity to think and talk about value for two intense years — both in an abstract theoretical sense, and in very applied sense as it relates to creating value in live casebook situations.

You learn not to take value for granted, even to have a certain reverence for it — that it’s transient, not to be treated carelessly — it can come and go.  Much like other living organisms, products, business models, companies, even whole industries have life cycles — they’re born, they grow, they thrive, they ebb, they die.

Read the rest of this entry »

When I first moved to New York in the mid-70s, I was working downtown for New York State’s Emergency Financial Control Board.  Yes, Virginia, we’ve had financial crises before, and that was a pretty bad one.  My job was conducting ‘financial intelligence’ about the city subway system.

A guy I worked with shared my interest in music, and told me about this great new record store just down the street — J&R.  It was for years my first stop when I wanted to find something unusual.  As many of you know, in the interim they expanded from that one store to now occupy many of the storefronts opposite City Hall.  They now carry cameras, computers, TV and other electronics, small appliances, and on and on.

‘This could be the last time’

These days, whenever I’m downtown, I’ve taken to stopping in — because I’m always afraid it will be the last time.  Tower Records, Virgin, Sam Goody’s, and HMV, formerly the other biggies in town, have long since left the market for vinyl records and CDs — because obviously the markets for these products are increasingly niche-based, and the major (legal) markets for recorded music are for MP3 downloads and streaming à la Pandora and Spotify.  I still buy CDs to get the best quality sound, but do it almost exclusively now from Amazon.

Even while they were around, records changed a lot.  My first memories of buying music are of going to the record store with my dad and having the clerk take out the record you were interested in so you could listen to it in a listening booth before committing to it.

Read the rest of this entry »

When Alice tumbled down the rabbit hole, she entered a world (“Wonderland”) reminiscent of her own—but in which everything seemed upside-down, and nothing worked as expected.

After spending over a year researching the economics of the health care industry, I’ve concluded that health care is its own economic Wonderland.  If you were given the hypothetical task of designing an economic system in which the laws of ‘economic physics’ had been miraculously suspended, you could not do a better job than the current US healthcare system.

As a result, health care costs continue to escalate considerably faster than most other household expenses.  Our $2.6 trillion in 2010 health care spending represented nearly 18% of GDP, a proportion that has doubled during the past 30 years.

Economic fundamentals

Most of my knowledge of economics comes, not from academics, but from working as a researcher and adviser to businesses over a period of 40 years.   As a result, I always err on the side of empiricism (i.e., what is) and pragmatism (i.e., how to improve it).

For reference I always come back to capital fundamentalism—the basics of supply, demand, and market exchange.  A ‘pure’ economic exchange works something like this:  you own a product or service that I want to acquire.  You name your price (or we negotiate and agree on a price), I decide whether it’s ‘worth it’ or not, then I decide whether I want to buy, and (if appropriate) in what quantity.  In the purest form, I am also the consumer or user of the product or service, so I can make this value choice first-hand.

Then we ‘do the deal’. I pay you and acquire the product or service, and everyone’s immediate economic needs and goals are presumably satisfied.  We all live happily ever after.  Or if not, and I’m not happy with the result you provided relative to the price I paid you, I buy from another provider next time.

Read the rest of this entry »