Think of this as KJR’s pledge week.

No, I’m not asking for donations. I’m asking for your time and attention before you let yourself read this week’s missive.

Specifically, when I decide what to write about each week I’m doing too much guessing based on too little information. I’m asking you to let me know what you’d like me to cover this year in KJR, and, almost as important, what I should be writing about so your non-KJR-subscribing colleagues would find it more compelling.

One more thing: My ManagementSpeak inventory is running low. After 23 years of one a week it’s entirely possible there just isn’t all that much more bafflegab to translate. So instead, if you don’t hear something that deserves the ManagementSpeak treatment, send me your favorite quotes instead.

I will ask you to apply one filter on these: As with all things KJR I’m looking for what’s off the beaten path — dictums that haven’t yet been widely discovered but deserve to be read by a discerning audience.

Okay, that’s enough Pledge Minute. Back to this week’s KJR.


The private sector has discovered data. Where a decade ago, business leaders were encouraged to trust their guts, they’re now encouraged to trust their data scientists.

It’s role reversal. Back then, governmental policy-making was heavily data-driven. As Michael Lewis (no relation) explains in The Fifth Risk, an extensive, intensive, and essential responsibility of many cabinet-level agencies is the collection of highly valuable data and managing the databases that contain them.

Just in time for businesses to invest heavily in data collection, management, analytics, and interpretation, the federal government is shifting much of its policy-making to a more instinctive approach, and in doing so is shifting budget and resources elsewhere.

Here at the Keep the Joint Running Institute, the Joints in question are organizations of all sizes and shapes; our charter is how to keep them running. As a general rule we (and that’s a royal we) stay away from political matters. Politicized matters? In bounds whenever they’re relevant.

And so (you were wondering when a point might emerge, weren’t you?) as your organization, for all the right reasons, embraces data-driven decision-making, here are a few cautionary notes you and your colleagues might find helpful:

Culture before tools: If you’re a longtime subscriber you’re familiar with the idea that when trying to institutionalize data-driven decision-making, a “culture of honest inquiry” is a prerequisite for success. In case you aren’t, the principle (but not its achievement) is simple: Everyone involved wants to discover the right answer to each question, not to prove themselves right.

Solving for the number: A culture of honest inquiry is what enlightened leaders strive for. While still on the journey, though, be on the lookout for someone using these new, powerful analytical tools to manipulate filters, choices of statistical techniques, and thresholds to support their pre-determined preferred result — for ammunition, not illumination.

GIGO: “Garbage In, Garbage Out” was widely recognized back when IT was known as the Data Processing department. That Big Data lets organizations collect and manipulate bigger piles of garbage than before changes nothing: Before you release your data lakes into the watershed, make sure your data scientists assess data quality and provide appropriate cautions as to their use.

Ease vs Importance: When it comes to data, some attributes are easier to measure than others. Even professional researchers can fall prey to this fallacy — that hard to measure means it doesn’t matter — without the blind spot ever quite reaching the threshold of consciousness.

Interpolation is safer than extrapolation: Imagine a regression analysis that yields a statistically significant correlation. And imagine that, in your dataset, the lowest value of x is $20 and the highest value is $200. Predicting the outcome of spending $40 is a pretty safe bet.

Predicting the outcome of spending $10 or $300? Not safe at all. Straight lines don’t stay straight forever. They usually bend. You just know where the line doesn’t bend — you have no idea where it does, and in what direction.

Machine guts need skepticism, too: Machine learning depends on neural networks. It’s the nature of neural networks that they can’t explain their reasoning — mostly, they’re just very sophisticated correlation finders. They’re useful in that they can plow through a lot more data than their human counterparts. But they’re still correlations, which mean they don’t imply causation.

But of course, to us the unwary, they do.

Courage: Take a timid business — more accurately, a business made timid by business leaders who consider avoiding risks to be the pinnacle of business priorities. Now add data and analytics to the mix.

What human data scientists and their AI machine-learning brethren do is spot potentially useful patterns in the data. These patterns will sometimes suggest profitable actions.

When all is said and done, when a pattern like this, along with the potentially profitable actions, are put in front of a timid business leader, much more will be said than done.

It’s unfortunate but not uncommon: Taking action is inherently unsafe, an insight that’s true as far as it goes.

What it misses: Playing it safe is usually even more of a risk, as competitors constantly search for ways to take your customers away from you.

Play it too safe and not only won’t you take customers away from them. You’ll fail to give your own customers a reason to stay.

I’ll start this week’s column where last week’s finished: “When we lose something or someone because they, like the kiwi, aren’t able to adapt, we’re as much the losers as they are.”

To which one Commenter replied, “Then how do you move into the future? Children miss their teddy bears when they grow up. Would you recommend keeping the factory line that makes buggy whips? There’s only room for so much stuff, whether it’s an economy or an ecosystem.”

And so, a question: Is a mainframe computer in any way comparable to a teddy bear? A kiwi? A tuna?

When I wrote about how sad it would be were the kiwi to lose its battle with extinction, I was thinking in terms of its intangible value for our sensibilities. I might just as easily, and just as accurately talked about the impact of the unescapable dominance of electric vehicles on drivers who love the sound of a muscle car revving its engine.

Or the impact of machine learning on the value we derive from the chess grandmaster archetype.

Or, let’s spin ourselves into the unavoidable future in which robotic football players replace their current-day human counterparts. Will Packer fans be as loyal and enthusiastic rooting for PX-6783-93-c as they now root for Aaron Rodgers?

That something is inevitable in no way makes it desirable.

But the kiwi’s plight, and that of the tuna, informs us in other ways more directly applicable to organizational strategy, tactics, and operations.

What’s important for business professionals to understand about the kiwi is that it’s a very well-designed bird. Well designed, that is, for an environment in which seeds, grubs, and worms are plentiful for them to eat, while nothing wants to eat them. In this situation, strong beaks and talons would be wasted — high-energy predation is pointless. The same is true for growing wings and the other adaptations necessary for flight.

If you’re in a parallel business situation — your company enjoys, say, a monopoly — there isn’t much point in developing the capabilities businesses develop to make them more competitive.

Contrast the kiwi with the tuna. Until very recently (in the context of evolutionary timescales), size gave tuna significant advantages: they could eat bigger prey; they could swim faster letting them catch bigger prey and escape from bigger predators.

As humans have become the most important tuna predator, size — because it’s what human commercial tuna piscators prize most — has become a disadvantage.

A business parallel? Try this: In a stable marketplace, businesses should grow. Size confers increased throughput and economies of scale throughout, whether the subject is raw materials, manufacturing, or distribution.

But in an unstable marketplace, a corporate behemoth can find itself suffering from too much capacity, while at the same time lacking what it needs to rapidly adapt to changing circumstances: research-and-development facilities to develop the products and services customers now want; the marketing capabilities needed to sell to unfamiliar markets; and skill at intentional business change so the whole organization knows how to operate in the new situation.

This is, by the way, where many Digital advocates get into trouble: Digital strategies, business capabilities, and underlying technologies are presented as universal requirements, or as panacea-level solutions all businesses must adopt or else die.

But there are no universal requirements or panaceas. As pointed out in the KJR Manifesto, there are no best practices, only practices that fit best.

The reverse also matters: Success can be an organization’s worst enemy, because success can blind business leaders to emerging threats, not to mention opportunities.

We can imagine members of the Kiwi Evolution Planning Committee debating priorities shortly after the first Rattus norvegicus appeared on New Zealand’s shores. Would its members have recognized the severity of the threat, or minimized it so as to rationalize the importance of respecting budgetary “realities”?

Would they have been willing to abandon the kiwi way of life — of flightlessness, grubbing in the underbrush for food, growing feathers that look like hair, and lacking any anatomical features useful for defense?

Or would they have told each other that rats would never make it in New Zealand because their teeth were too sharp, their pace too fast, and fecundity way too high compared to kiwi best practices?

Because we like kiwis more than we like rats (we all do, don’t we?) we’d all surely hope the KEPC would have chosen an evolutionary path that would have given them the upper hand … uh … wing.

And we’d forgive them if, as they figured it all out, they paused to regret having to lose some of what makes a kiwi a kiwi in the first place.