March 27th, 2016
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.
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.
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.)
But then curiosity sets in, and our client thinks of other related things she is interested in. “While you are finding out the MRK, it would be useful to also find out X, Y, and Z.” I call this “expanding the cognitive perimeter”, but most consultants know it as “scope creep”. Researchers on a fixed-budget scope of work dread this because it causes them to expend unplanned (and often non-chargeable) time and effort.
So we have two parts to Column B, let’s call them Column B1 (“Need to Know”, or MRK) and Column B2 (“Nice to Know.”) B2 is especially dynamic and iterative; as new data appears, rather than quenching an information need, it typically spawns collateral needs.
One more thing
Pretty cool, right? Sure enough, except that (to quote the late, great Steve Jobs) there’s one more thing: Column C.
In Column C are things that we don’t know that we don’t know. (Usually attributed to Donald Rumsfeld, but we hear rumors he got it from Ernest Hemingway.) Since we don’t know that we don’t know these things, they can’t rightfully be in either Columns A or B. The best thing to do here is to be aware that these things will almost invariably come up, and to keep an “open box” in which to identify them as they arise.
We have backed into a consideration of second-order knowledge — knowledge about knowledge, or “meta-knowledge.”
There is also a corresponding Column D that contains things we do know, but don’t know that we know. This includes items at a personal level (“tacit knowledge”) as well as items that the “organizational we” (i.e., someone within our organization) knows, but that we ourselves do not.
Oh, and one MORE thing
As I was polishing this post for release, I happened to see the movie The Big Short. It tells the story of the 2006-07 US mortgage market bubble and subsequent meltdown (pretty accurately from my readings on this and related subjects at the time and since then).
A quote from Mark Twain appeared at the beginning of the film for which I had to “stop the presses”, it is so completely germane. It captures in a nutshell what I believe is the single most intractable cognitive problem in business strategy, in investing (where it applies in the film), and even in natural science:
“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” Mark Twain
“Everyone knows that…”
Sam Clemens really nailed it, as he so often did. For how many thousands of year did all educated people “know” that the world is flat? For how many centuries did they “know” that the sun revolves around the earth? Even a child can see the sun rising in the morning and setting in the evening.
More recently, “Everyone knows that housing prices always go up.” “Everyone knows that capital markets are efficient.” These two previously-hidden assumptions were dragged into the light and took a beating in 2008-09, but now may be silently creeping back into some of our actions. “Everyone knows that…cars run on gasoline…people will never buy eyeglasses or shoes over the Internet…content must be professionally edited before people will want to read it.”
And so on, you get the idea. Hidden assumptions are the progenitors of myths, dogmas, biases, orthodoxies, and other bad mental habits — the things that block our attainment of “true knowledge”. They are insidious in that, being hidden, they are never examined — let alone, challenged or rigorously tested.
So the knowledge gaps we need to address are a little more complex than our original two-column “TV detective” chart, as shown above. We now have a Column A1 (things we know that also are verifiably true) and a Column A2 (things we “know for sure that just ain’t so.”)
Columns C and D, our second-order knowledge (“meta-knowledge”), perhaps fit best as another dimension — the “order-ness” of knowledge, first or second, as shown. So we have now arrived at every consultant’s dream destination: the bi-dimensional analytic matrix — this one describing options and opportunities in the research process.
What are the hidden assumptions behind your research processes? What are the things that you don’t know that you don’t know? Start by taking an inventory and making a list. Not so easy, as these by definition are hidden — and therefore hard to capture and manage.