A while ago, Doug Hubbard introduced me to the concept of measurement inversion. According to him the more something matters the less we tend to measure it and the less we know about it.
Things that really matter are often highly imprecise and thus there is a great opportunity to learn a whole lot more. Whereas, the things that do not really matter can often be easily measured and as a result have been studied to death.
Measure what’s actually important
For example, a CEO’s actions can impact the company much more than those of an entry level employee, but there is a lot more effort in tracking that employee’s time than the CEO’s.
Businesses tend to focus on tracking and managing costs, while the better predictability of revenue is by far more important.
This applies to just about anything. The topics that really matter, like
- love, relationships, community, friendship,caring,
- motivation, ability to learn,
- customer loyalty and leadership
are inherently hard to quantify and can be damaged by the measurement process itself.
However, we would gain a lot more insight by spending the time to try to have a better idea of the degrees within each of these variables, rather than trying to insure that:
- the attendance is perfect,
- number of orders is processed in a particular amount of time, or
- scrap rate has reached a certain part per million count.
Thus, a large part of an organizational challenge is to stop measuring all the wrong things, either:
- things that do not really matter, or
- things that do not tend to vary much, or
- things that can be measured less frequently, or
to a lower level of precision and still not affect how we make decisions.
We don’t need the level of perfect precision
Measurement has to become divorced from precise numbers, after all, most things are not very precise at all. Nor do we need the level of perfect precision, or for that matter can afford it.
Introduction of tools such as Monte Carlo simulation and SPC charting in the calculation of the variables that are used as KPI in the business setting is the ticket to having more meaningful data that facilitates better decisions.
Among those KPIs one that is worth at least occasionally considering is the benefit from improvement in decision making as a result of availability of the measurement data for each variable at a level of granularity and accuracy that is currently available. This may help us fight the measurement inversion.