Why this picture, you may ask? Because success measurement is like climbing the steps of the Peterskirche in München. Or because I didn’t want to use a generic data illustration… Anyway, here are two things that will help you on your way.

Metrics serve decisions, not the other way around

Measure what matters by Jon Doerr is probably the most famous book explaining OKRs (Objective and Key Results). But there was one thing that confused me: The Key results described as examples, sounded much like to-do-lists, rather then measurements of success.

Luckily my friend Antonia sent me the article Measure What Matters got 2 things wrong, which highlighted the difference between output- and outcome-driven Key results. And recommended to use the latter. Good Key results and KPIs are focused on outcome.

In Nobody needs metrics — what we need are better decisions, Helge Tennø uses decision-based as a synonym to outcome-based. He suggests that when you want to find good, outcome-based metrics, don’t start with what can be measured. Instead, think about the most important decisions you could make and then figure out what combination of metrics can lead to those decisions.

Proper use of metrics involve regular reviews

Emily F. Gorcenski’s book review of The Tyranny of Metrics, by Jerry Z. Muller taught me new terms that I will definitely use more often to sound smart. First one is “Taylorism”, so named for engineer Frederick Winslow Taylor, that stands for the attempt to add mathematical rigor to management theory.

Gorcenski sees Taylorism as one of the reasons for bad OKRs like, “derive five insights per quarter” for a data science team. In her eyes, the art of designing effective OKRs is often missing from the discussion and OKRs are often only used so someone can brag about being goal-driven.

The other reason for bad OKRs she gives is Campbell’s law. That one describes that “the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures […]”. Gorcenski’s states that Campbell’s law can result in the tendency to cherry pick outcomes that lead to better metrics.

So is success measurement flawed in its core? Should we stop doing it? No, but Gorcenski says you need to do one thing, to not suffer from Taylorism or Campbell’s law: Implement a feedback cycle, that regularly reviews the applicability and efficacy of the metrics used in your success measurement.

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