Most CEOs suck at Precision vs Accuracy??

Most people, regardless of education, use the terms precision and accuracy interchangeably, but they are fundamentally different concepts - with huge implications for decision making in business.

Setting aside the semantics - turns out most leaders are vastly overemphasize precision, when accuracy is actually what matters.

Firstly - a few important definitions:

• Precision refers to the level of detail and refinement in a measurement or calculation. It's about how close repeated measurements are to each other, regardless of their closeness to the true value. For example, hitting the bullseye on a dartboard with consistent shots demonstrates precision.
• Accuracy, on the other hand, refers to how close a measurement or calculation is to the true or desired value. It's about correctness and reliability. Using our dartboard analogy, accuracy means hitting the bullseye consistently, even if the shots are clustered in different areas of the target.
• A Stochastic System: Any process wherein external variables result in significant unmodel-able randomness for an individual outcome. To use a rather macabre example, you can drive perfectly and yet get into a car accident because of someone else's driving (interestingly, they could also be driving perfectly too and yet still cause an accident)

Now, let's jump into why business executives screw this up: everyone likes precision without realizing it:

• A) Doesn't matter
• B) The real world is fundamentally stochastic - so precision is actually impossible to achieve, no matter how good your model!

This issue is originates in the training and background of many executives, who tend to be high-IQ people who spent much of their career building complex excel models. So if that's what you were trained to do through the early and mid-stages of your career, then that's what you do when you're a senior leader. To a hammer, everything looks like a nail. ??

Hence, executives spend considerable effort in building detailed models of for business decisions, often spending hundreds of person-hours of their own and subordinates time modelling the outcome, with dozens or hundreds of assumptions.

For example, I was recently asked by an investment firm to build a model by country x by product by month - a model requiring approximately 500 inputs, nearly all of which were made up. When I pushed back saying that I could achieve a more accurate forecast of the business with 2 inputs and 4 lines, I was rebuffed, since my model was too simple.

Precision is stupid and pointless - the real world of business is too stochastic to predict precisely, but it can be predicted accurately.

At IdeaScale, we take a different approach. We prioritize accuracy over precision.

Instead of striving for exhaustive precision in our analyses, we focus on quickly gaining a solid understanding of the situation at hand. We make rough assumptions (sometimes entirely based on gut feel), conduct quick and dirty analyses, and embrace a fail-fast mentality. Our assumption-laiden bets tend to be small with quick feedback cycles, enabling us to determine whether our assumptions were accurate and then scale up rapidly. If the assumptions were inaccurate, we move on, having lost very little and wasted no time on precise analysis.

This approach allows us to make decisions swiftly - usually accurate, but never precise. We understand that in the face of uncertainty, it's more important to be directionally accurate than precisely wrong. By embracing this mindset, we empower ourselves to move fast, iterate rapidly, and capitalize on opportunities as they arise.

In the next article, I'll go through how IdeaScale goes even further in making GREAT decisions by leveraging the power of "the theory of real options".

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