I'm doing this because Commoncog's essays over the past two years have been pretty much about a) business expertise, or b) about methods to accelerate the acquisition of that expertise.
But that business expertise has a shape, and I wanted to make that shape explicit.
Basically experts in business deeply understand 'supply', 'demand' and 'capital' in their respective industries, and grok how changes in one leg of the triad affects the others.
The thread where I originally get into this is still pinned on my profile:
As a minor follow up to this … if you internalise the triad in business, it’s really striking how high the bar is for expertise.
I know exceedingly few people who are good at all three legs of the triad, and are able to reason about changes between the three legs.
Why? Partly this is due to age: relatively few people I know are familiar with and able to navigate the capital cycle for their industry (because they’ve not been in business long enough).
Whereas if you read biographies the triad is more stark.
Conversely, the triad really does NOT resonate with business novices.
I find that it takes a little sophistication (or hard-won experience) before someone goes “oh yeah, DiBello’s claims seem right.”
In other news I should probably make friends with older people in business (or make friends with operators in industries with extremely short capital cycles!) 😅
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The field of Statistical Process Control has this interesting idea that ‘management is prediction’ — i.e., in order to be a good operator, you need to be able to predict (within limits) the business outcomes of your actions.
The implications that flow from this are quite useful.
First, most business operators run their businesses according to ‘superstition’ — meaning they believe some set of things will happen due to their actions, and then they take those actions, but they don’t really KNOW if there’s a causal relationship.
This describes me too, btw.
W. Edwards Deming then has a follow up idea: he says that “there is no truth in business, only ‘knowledge’”
And ‘knowledge’ is defined as “models or theories that allow you to predict better”.
Whenever you see an SPC practitioner talk about ‘knowledge’, this is what they mean.
My article on Goodhart’s Law was on the Hacker News front page last night.
I thought this was a good comment (just about every other comment was bad).
The Goodhart’s Law piece may be found here (and the comment refers to the lengthy case study of the Amazon Weekly Business Review in action): commoncog.com/goodharts-law-…
Again, bear in mind that the HN comment is one person’s opinion, but I find it plausible.
Caveats: I’ll need multiple corroborating accounts that say similar things to increase my confidence in this take; if true, I’m interested to know when the decay started.
I’ve been thinking about Amazon’s “Are Right, A Lot” Leadership Principle again. It’s probably the most controversial of the Leadership Principles, and one I’ve spent the most time thinking about (and asking ex-Amazonians about).
The first thing that usually gets pointed out is that it sits between two other principles that intentionally balance it out:
I once asked an ex Amazon exec why the S-team were willing to wait 2 years before Prime’s metrics confirmed their initial hypothesis (Prime was existentially risky), and he said, basically “Jeff had a history of being right a lot, so we gave him the benefit of the doubt.”
This week’s Commoncog essay is about How to Become Data Driven, at least according to Statistical Process Control.
Their answer to that question is, perhaps, surprising: SPC practitioners believe the beginning of becoming data driven is … (drum rolls) understanding variation.
Wait, understanding variation?
Yes, understanding variation.
Deming and his colleagues (who started this whole SPC thing) argue that you cannot use data to make decisions if you don’t know what is routine variation or exceptional variation.
One is signal, the other is noise.
That sounds like common sense, but give it a minute — SPC practitioners take this single idea and then built an entire approach to data-driven decision making on top of It.
The ideas build on the core insight that you really ONLY want to investigate exceptional variation.
One thing that doesn’t come through as viscerally in the deliberate practice literature is how much doing DP is basically dealing, psychologically, with repeated defeat.
Stupid errors, dumb errors, failed attempts at executing sub-skills, exercises that zoom in on the worst parts of your game, thus constantly putting you in situations where you’re designed to fail.
The term used in the research literature is ‘desirable difficulties’. Seems nice in the abstract. Not so nice in practice.
It’s surprisingly difficult to communicate the core ideas of statistical process control.
I’ve been banging my head on this @commoncog piece for the good part of three weeks now, and I’m trying to capture the intuition without making it trite or getting into the maths
I think part of the difficulty is that so much of SPC’s ideas sound boring and trivial when stated up front, and only show their power through worked examples.
For instance: “understanding variation is the beginning of becoming data driven.”
And: “management is prediction.”
Another one: “the role of the businessperson is to seek knowledge, not truth. There is no such thing as truth in business. There is only knowledge, and knowledge gives you the ability to make predictions.”