How To Measure Anything[1] by Douglas Hubbard.
The basic gist of the book goes something like this: in the real world (especially in a business setting) there are many things which are hard to measure directly, but which we may care about. Take, for example, "employee morale" which matters because it may affect, say, retention, or product quality. Hubbard suggests that we can measure (many|most|all|??) of these things by using a combination of "calibrated probability assessments"[2], awareness of nth order effects, and Monte Carlo simulation.
Basically, "if something matters, it's because it affects something that can be measured". So you identify the causal chain from "thing" to "measurable thing", have people who are trained in "calibrated probability assessment" estimate the weights of the effects in the causal chain, then build a mathematical model, and use a Monte Carlo simulation to work out how inputs to the system affect the outputs.
Of course it's not perfect, since estimation is always touchy, even using the calibration stuff. And you could still commit an error like leaving an important variable out of the model completely, or sampling from the wrong distribution when doing your simulation. But generally speaking, done with care, this is a way to measure the "unmeasurable" with a level of rigor that's better than just flat out guessing, or ignoring the issue altogether.
[1]: https://www.amazon.com/How-Measure-Anything-Intangibles-Busi...
[2]: https://en.wikipedia.org/wiki/Calibrated_probability_assessm...