What will it be, a “single version of the truth” or unabated proliferation of ad hoc data? It’s a chronic dilemma, and its resolution is crucial to big-box business intelligence. Frank Buytendijk’s new book, his second one, offers a way out of this pickle.

In Dealing with Dilemmas: Where Business Analytics Fall Short (John Wiley & Sons; 2010), Buytendijk — pronounced BOW-ten-dek, according to a Dutch friend of mine — argues that the usual this-or-that, you-or-me, and now-or-then dilemmas may not be the tough choices that they seem to be.

I had known Frank Buytendijk from his two TDWI keynotes, both of which broke down old fences. Then I got to the part of the book where he takes on Michael Porter — author of the essay “What is Strategy?,” in which he defines strategy partly by what a business doesn’t do. Southwest Airlines, for one well known example, offers no reserved seats or meals. But without that distraction, it can fly you on time at a reasonable price.

Porter’s theory, I used to say, is comparable to cropping a photo: emphasize one aspect by trimming others. Then you know what the message is for once and for all, or maybe you have to interpret it, but what you need is all there. Buytendijk’s theory may be more like making a movie. The movie, too, requires the artist to decide on emphasis and exclusion, but in a movie the story plays out over time and through multiple spaces. The movie, too, has a message. But a movie goes this way and that way as it winds toward the end — like a business as it winds through its environment toward a goal.

Of course, a movie requires the artist to think harder. A movie takes shape much more slowly. I’ve only imagined Buytendijk’s principles in practice, but I think that what he prescribes is far more involved the usual strategy formulation.

Buytendijk, in fact, has fun ridiculing strategy-formation executive campouts. Inspiration may strike while they “sing songs around the campfire.” The marshmallows and scotch taste good, but the thinking doesn’t stick, the assumptions are forgotten, and the organization is left to live on slogans.

What’s required, he writes, is deeper understanding of the theory behind the business and the nature of the dilemmas that decision makers face.

While the goal (that we chose) remains intact, and the assumptions remain in place as long as they match reality, we can travel toward our goal, assessing whether options that we create and opportunities that we see fit into the framework. If so, we capitalize on them; if not, we let them go. And the moment assumptions change, we can immediately see which activities do not lead us to the goal anymore, or which activities are lacking in making it to the goal. Choices do not turn into dilemmas.

Choices don’t turn into dilemmas the way “single version of the truth” versus spreadmarts has. The Big Brother version of decision support might have been devised in a campfire sing-along — far away from those who still had work to do.

What would Buytendijk do with that problem? I think he would classify it as a “you-or-me” type. It involves one group against another, usually IT soldiers charged with enforcing a policy against rebel cells armed with spreadmarts. But if either one had a decisive victory, it might spell trouble for the organization.

He prescribes three steps: First, examine your motives. What are you really trying to do? What’s the goal? And so on. Next, communicate. Do not fall for that old slogan of ham-fisted managers, “If you’re not part of the solution, you’re part of the problem.” The solution may be found in conversation. “By being part of the solution from the start,” he writes, “the only angle you will see is your own … Acknowledging there are multiple sides to the story, even if you do not agree, is the key to reconciliation.” Finally, reconcile and synthesize. Opposites — such as love and hate, Tea Bagger and Berkeley liberal, IT soldier and spreadmart rebel — may actually be more alike than you think.

I see what he means when, at the end, he confesses to not understanding the old saying that “you can’t have your cake and eat it too.” It’s stupid, something a burned out school teacher uses to keep order. Aren’t we smarter than that? And if there’s hope for cake, there’s hope that the “single version” and the spreadmarts can live together under one roof.

Business is ultimately not technical but social, is it not? Appropriately, this book is deeply humane and intelligent — expressed in a warm, conversational voice. That alone distinguishes it from most other business books. It eases you through difficult new ways of thinking, through what I think many readers will find is new and unfamiliar territory. Decision makers who are willing to put in the effort to understand it and put it into practice, I think, will find it worthwhile.

Dealing with Dilemmas may never become a mainline classic of the kind Porter wrote. But it will certainly be a favorite among a smart and adventurous few.

Imagine someone writing a book about data analysis without even mentioning software.

“To penetrate the heart and soul of the problem of improving analysis,” writes Richard J. Heuer Jr. in Psychology of Intelligence Analysis, “it is necessary to better understand, influence, and guide the mental processes of analysts themselves.” It’s the mind that does the heavy lifting.

Though Heuer writes for CIA-type analysts, similarities to business are clear. The biggest one is that it’s always the mind that does the heavy lifting.

The two kinds of data analysts share many other aspects of their work, such as their unconscious biases. For example, they all tend to give undue importance to vivid evidence over abstract evidence.

Both also endure the after-the-fact know-it-all, the type that announces, “That analysis told me nothing new,” after confirming evidence arises. Heuer describes an experiment that suggests such beliefs are usually delusional.

Analysts’ memories, too, undermine their work, such as when asked to remember what led them to their conclusions. Also, overseers tend to regard events as more foreseeable than they actually were.

Missing data, another problem, is “out of sight, out of mind.” Two groups of experienced auto mechanics were asked for a diagnosis of a car that wouldn’t start. They were offered a fault tree with several major branches — weak battery, starter-system defects, etc. — with several themes left out and summarized as “other causes.” One group’s fault tree included all major branches. But the second group’s fault tree had several branches missing — and selected the summarized “other causes” far less often than would be expected if the summarized reasons had been listed along with all the others.

Heuer offers a few solutions. The main one seems to be his “Analysis of Competing Hypotheses.” He outlines it in eight steps.

Heuer writes the way I’d expect a CIA veteran to write: formally. Even where he has a colorful tidbit — such as any of the load of stories mentioned in the footnotes — he opts for gray.

Such drab phrasing ignores the principle he gives in Chapter 10 under “the vividness criterion.” More vividness would have made a more readable book.

The impact of information on the human mind is only imperfectly related to its true value as evidence. Specifically, information that is vivid, concrete, and personal has a greater impact on our thinking than pallid, abstract information that may actually have substantially greater value as evidence.

If there’s one thing the book reminds the reader of over and over, it’s that analysts are only human.

On tools, Heuer lists only what he considers the basics: decomposition and externalization. Vendors will have to do what they can with those clues. Unlike so much software, though, Psychology of Intelligence Analysis is free.

What a radical idea: break business intelligence down by the types of work to be done — financial intelligence, human-resources intelligence, risk intelligence, etc. — instead of by the technology — data warehousing, data integration, dashboards, etc.

“If I put my feet in the shoes of a business person listening to someone pitching ‘business intelligence,’ what I hear is another IT silver bullet,” eLearningCurve education director Dave Wells said recently.

If you’re talking to a finance officer, for example, about forecasting cash flows or predictive analytics based on the economy, you might call be describing “financial intelligence.” If you’re talking to an HR manager about retention, recruiting and pay scales, you might be talking about “human-resources intelligence.”

Imagine: BI terms that mean something — exactly what he’s trying to promote. Radical, but smart.

Last week, SAS Institute did what few other rearrangers of jargon have done: They got attention — in the way fashion hounds do. They’d like to replace “business intelligence” with “business analytics.” Why? Because “business intelligence is not “where the future is.”

Last year’s styles are never next year’s. Sooner or later, all clothes, cars and business terms become unfashionable. It’s not because anything really changed.

Neil Raden calls SAS’s move “all fluff,” and it is. Others I’ve heard from think so too, such as the eminent industry analyst I talked to weeks ago about another attempt at jargon-making. “What a bunch of b—” he said. “We have so many buzzwords.”

Some of this bloggery about this goes off track, though, when opinions touch on tools and expectations of jargon. Neil, for example, scoffs at “business analytics,” SAS’s term for the day, because “it doesn’t tell me anything.”

Let’s think. What does “dog” tell us? Who needs to say “domesticated canine”? “Dog” does tell us something because we’ve agreed on what it means. Words work like that.

One commenter wrote that “business intelligence” doesn’t imply any business value. Must it? If a shepherd mentions his “dogs,” must he correct himself and say “work dog”? Even then, someone might complain that that doesn’t say what kind of work.

Poor, poor “business intelligence.” We keep using it for now like a still-good jacket of last year’s color. But, ah, the weather’s changing.