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

Imagine for a second you’ve won the lottery, and are now driving a new $75,000 BMW M3.  Would you put low-octane gasoline and bargain motor oil into it?  Unlikely, since the value and usefulness of what you have would likely be degraded significantly by doing so.

Yet companies do this all the time — put low-quality data into a highly-tuned decision-making machine — often without knowing it.  The quality of raw unprocessed 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 we 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.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

It’s here—the shiny (really!) new version 3.2 Knowledge Value Chain Workbook.  Our thanks to the TKA team and partners who made this happen.  Workbook designer John (”The Great”) Fantini and printer Craig (”On-Time”) Pace of Spectragraphic Inc. did yeoman service in getting this edition ready.  Mike (”Pool Boy”) Powell made extensive editorial comments, as did KVC “users” Kathy Walsh of Purdue Pharma and Stephen Lippman of Merrill Lynch.

For those of you with version 3.1, most of the changes are editorial, there is little new content this time (hence its “dot” number).

We have now shipped the book worldwide—most recently to Estonia and Korea—and welcome any feedback from those of you who have it or are using it.

 To order, click here.

Sharp readers of the KVC Workbook (Version 3.1) have spotted several typos:

• Page 3, right column, 1st paragraph – change “necessary” to “necessarily”
• Page 5, 1st line – change “rather” to “father”
• Page 5, right column, 2nd paragraph – after “more”, insert “than”
• Page 6, right column, 3^rd paragraph, 6^th line – change “the” to “to”
• Page 7, 1^st paragraph – change first “who” to “which”, and “their” to “its”
• Page 10, 4^th paragraph – after “of” insert “us”
• Page 12, 2nd paragraph – change the second “do” to “conduct”
• Page 23, 2^nd paragraph – after “routinely” drop “and”
• Page 28, 2nd paragraph, 2^nd line – drop “we”
• Page 30, 2^nd line – change “know” to “known”
• Page 32, 2^nd paragraph, last line – after “$50,000″ insert “car”
• Page 40, 4^th paragraph – after “company” insert “has taken”
• Page 42, last paragraph – change “some” to “an”
• Page 44, 3rd paragraph – drop the second “that”
• Page 46, 1st paragraph – change “diagnosis” to “diagnose”
• Page 62, 3rd paragraph – change “an” to “and”
• Page 66, 2nd paragraph – drop second “you”
• Page 74, 1st paragraph – after “way” insert “as”
• Page 74, 3rd paragraph – change the second “your” to “you”
• Page 79, slide fourth bullet – after “possible” insert “to”

Thanks, you know who you are!

 All of these (and more) changes are made in the new Version 3.2.

The Knowledge Agency has moved!  After a successful five-year run on Fifth Avenue, we’ve moved west to the Hudson River waterfront.

Effective November 1, 2007, TKA’s  address is:

The Knowledge Agency®

548 West 28^th Street

New York, NY 10001, USA

That’s in the Hudson Yards area the New York Times recently profiled as one of the fastest-developing business centers in New York.  Several major art galleries, publishing, and technology firms have already relocated from midtown to here.  Soon Ogilvy and Mather, Morgan Stanley, News Corporation, and Condé Nast are said to be moving their headquarters here.  We have some interesting neighbors!

We’re also in the process of modernizing  and upgrading our phone and Internet systems based on fiber optic T1 technology.  Phone numbers stay the same.

September 6, 2007, NEW YORK—The Knowledge Agency® (TKA) today announced the availability of The Knowledge Value Chain® (KVC) Workbook, Version 3.1.  “This marks the first time we’ve made our workbook available separately from our clinics and workshops,” said Tim Powell, TKA Managing Director and developer of the KVC model.  “In order to get the full benefits of the KVC, you need to participate in a KVC Clinic or public workshop.  However, the Workbook itself provides a good deal of value, and you can use it to begin to implement some of the KVC principles yourself.”

First introduced in 1996, the KVC model has been taught in universities, business clinics, and workshops worldwide since that time.  Version 3.1 introduces the KVC Scorecard^TM, a self-scoring evaluation system for the effectiveness of intelligence and knowledge processes and products.

The KVC Workbook is available only directly from TKA.  For a limited time, we are offering introductory pricing of US$100 plus shipping.  To reserve your copy, use the “Contact” button at the top of the page.  We’ll contact you back to complete the order.  We accept American Express and bank wires.

Also announced was an upgrade path for owners of earlier versions of the workbook.  Version 3.0 owners may upgrade to Version 3.1 for US$65.  Owners of earlier versions should contact TKA for pricing on those upgrades.

September 4, 2007, NEW YORK—The Knowledge Agency® (TKA) today announced the availability of The Knowledge Value Chain® (KVC) Clinic^TM, version 3.1.  TKA Managing Director Tim Powell said, “The KVC Clinic gives us the chance to work directly with companies to bring the benefits of the KVC model into companies to solve their intelligence and knowledge challenges.”

First introduced in 1996, the KVC model has been taught in universities, clinics, and workshops worldwide since that time.  Version 3.1 introduces the KVC Scorecard^TM, a checklist and self-scoring evaluation system for the effectiveness of intelligence and knowledge processes and products.

Contact TKA for clinic pricing and availability at +1.212.243.1200.

The Knowledge Value Chain and The Knowledge Agency are registered U.S. trademarks of the TW Powell Co. Inc.

When I started my own business in 1991, my late father showed a more keen interest in my career than he had previously. He had gone into business for himself late in his own career (as a business writer), and I always found his insights helpful. I always gave him whatever papers and books on intelligence I had written, and he always made an effort to read them, and respond “intelligently”.

But one day, he confirmed what I already suspected by asking, “Tim, I’m still not really sure what you, your company, and your colleagues do for a living. Can you explain it in ways that the rest of us can understand?”

Read the rest of this entry »