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Reliance on measurement from lack of trust in our own intuition is turning us into meatbots.
The desire to measure is strongest when things are not working. [Not to say we can’t implement a measurement system to “prove” what is obvious to all]. Why isn’t it working – what’s wrong? And the fixers get out their measuring tapes, consultants are hired, processes are put in place, and eventually we learn what we already knew in the first place
Why? Trust, or lack of it. If we would allow ourselves to access – and trust — our intuition, we know “what’s wrong“ most of the time. Here are a few of my favorite rationales for measurement interventions:
- We’re afraid, and with good reason. Trusting our intuition has been beaten out of us by the entire education system.
- CYA (cover your ass). If we put in protection against risks, we can’t be blamed when it blows up – this is particularly challenging in today’s “gotcha” climate.
- In many instances, the key to “climbing the ladder” is being expert at avoiding decisions and commitments – developing measurements is a great way to postpone.
- We need to convince “them.” One of the most mysterious entities in the universe, these them have been thwarting progress everywhere. My favorite, in a pitch to the executive management team of a Fortune 500 company, including the CEO, was that they would never go for our pitch. Incredulously, who is they??? ah, the Board of Directors…and after that???
So, we measure because we don’t trust, and…..we become meatbots! As software/AI/machine learning improves it will take over more of this safe, rule-based, and formulaic thinking. Falling back on policy, procedure, rules, metrics and measure is classic meatbot thinking. In a sense, these fall-backs are substitutes for thinking, reason, nuance, creativity and the ability to see what’s special about a circumstance – the kind of thinking will be most difficult to automate.
In closing, let’s remind ourselves of Donella Meadows “Places to Intervene in a System” which put metrics/measurements at the top – easiest to do, but least overall impact. — Andy Hines
[…] I think a key piece of this is the growing support for relying on algorithm, data, and analytics for decision-making. Because we don’t trust ourselves, we rely on machine logic, which is seen as neutral, unbiased and fair (though that is questionable). My own view is that we should indeed use data to help us decide – not abdicate our decision-making responsibilities. Or, as I’ve put, don’t be a meatbot. […]