#metrics #productivity #management #control


Measuring productivity of a team can be accomplished by many ways: the business outcome, the output, etc. This measurement is valuable contextually: when comparing a number from the month before, given the conditions of the tests, etc. It is very hard, given that usually there is no control group, which makes the exercise significant mostly for setting direction, or interpreting data collaboratively (with, as opposed to them), but not to set a target.

Measuring the productivity of an individual is significantly harder. Most of the metrics are biases one way or another. Outcome is biased by the card the individual got distributed (e.g. accounts they work on). Output is biased by the metric itself (e.g. number of lines of code don't mean anything: coding style can double things, and then more lines might mean poorer code). At best this can be used to signal a potential issue that will need some qualitative study. And then you're at risk of Goodhart's law.

Most of the time, better not use productivity metrics and instead rely on gemba.

Measure results instead of time spent[2]. This also means negative results: when something has extra complexity or is not possible, it still generates valuable information[1] and should not be discarded as incompetence.