Absolutely anything can be measured. It’s just a matter of how you frame the query – and you don’t need to be a statistician or a maths whiz to get it right.

In essence, a measurement is simply an observation that quantitatively reduces uncertainty. Everything that matters has observable consequences (otherwise they wouldn’t matter).

The first step of Applied Information Economics is to define your problem and identify the variables that would make your decision easier. This helps to quantify the uncertainty.

It’s a common misconception that the greater the uncertainty, the more data you need to overcome it. When you know next to nothing, almost any information will help.

Asking specific questions based on what’s observable can turn intangibles into tangibles. Start with: “What do we mean by X? Why does it matter? What do we see when X is taken away?”

Let’s say you want to figure out the value of having IT security. That may sound vague and immeasurable, but once you break down why you would care about having it (the threat of attacks), its value can be measured: preventing lost productivity, fraud, and legal trouble.

All uncertain variables have an expected value of information (EVI). This is calculated by figuring out the point where it begins to make a difference compared to the existing uncertainty.

In every industry, most businesses fall into the “measurement inversion” trap: what gets measured and what needs to be measured turn out to be two completely different things.

Relevant variables that you have the least information about are often the ones you need to prioritize. But all too often, managers instead simply focus on what’s easy to measure.

If the outcome of your decision is highly uncertain and will have significant consequences, any variable that reduces that uncertainty is valuable. Once you’ve found one, it’s time to measure it.