How to Measure Anything: Finding the Value of Intangibles in Business

by Douglas W. Hubbard

Measurement is about supporting decisions, and there are even “micro-decisions” to be made within measurements themselves.

In some ways expert intuition is irreplaceable but it has its limits and decision makers at all levels must know when they are better off just “doing the math.

don’t confuse the proposition that anything can be measured with everything should be measured.

The concept of measurement as “uncertainty reduction” and not necessarily the elimination of uncertainty is a central theme of this book.

there is another key concept of measurement that would surprise most people: A measurement doesn’t have to be about a quantity in the way that we normally think of it.

The commonplace notion that presumes measurements are exact quantities ignores the usefulness of simply reducing uncertainty, especially if eliminating uncertainty is not feasible (as is usually the case). And not all measurements even need to be about a conventional quantity.

When I talk about measurement as “uncertainty reduction” I imply that there is some prior state of uncertainty to be reduced. And since this uncertainty can change as a result of observations, we treat uncertainty as a feature of the observer, not necessarily the thing being observed.

Once managers figure out what they mean and why it matters, the issue in question starts to look a lot more measurable.

I then follow up by asking “What do you mean by <fill in the blank>?” and “Why do you care?”

there are two methods that seem to help with the particularly hard-to-define problems. I use what I call a “clarification chain” or, if that doesn’t work, perhaps a type of thought experiment. The clarification chain is just a short series of connections that should bring us from thinking of something as an intangible to thinking of it as tangible.

First,

Second,

final step

Rule of Five There is a 93.75% chance that the median of a population is between the smallest and largest values in any random sample of five from that population.

The Single Sample Majority Rule (i.e., The Urn of Mystery Rule) Given maximum uncertainty about a population proportion—such that you believe the proportion could be anything between 0% and 100% with all values being equally likely—there is a 75% chance that a single randomly selected sample is from the majority of the population.

In the context of management, if a measurement matters at all, it is because it must have some conceivable effect on decisions and behavior. If managers can’t identify a decision that could be affected by a proposed measurement and how it could change those decisions, then the measurement simply has no value.