I just finished reading How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard. It discusses fascinating concepts about measurement and observability, but they are tendrils that you must follow among mentions of Excel, statistical formulas, and somewhat dry consulting anecdotes. For those of you that might want to focus mainly on the concepts rather than the literal statistics and formulas behind implementing his framework, I wanted to share the concepts that resonated with me. If you want to read a more thorough summary, I recommend the summary on Less Wrong, also titled How to Measure Anything.

The premise of the book is that people undertake many business decisions and large projects with the idea that success of the decisions or projects can’t be measured, and thus they aren’t measured. It seems a large waste of money and effort if you can’t measure the success of such projects and decisions, and so he developed a consulting business and a framework, Applied Information Economics (AIE)to prove that you can measure such things. Near the end of his book on page 267, he summarizes his philosophy as six main points:

1. If it’s really that important, it’s something you can define. If it’s something you think exists at all, then it’s something that you’ve already observed somehow.

2. If it’s something important and something uncertain, then you have a cost of being wrong and a chance of being wrong.

3. You can quantify your current uncertainty with calibrated estimates.

4. You can compute the value of additional information by knowing the “threshold” of the measurement where it begins to make a difference compared to your existing uncertainty.

5. Once you know what it’s worth to measure something, you can put the measurement effort in context and decide on the effort it should take.

6. Knowing just a few methods for random sampling, controlled experiments, or even just improving on the judgment of experts can lead to a significant reduction in uncertainty.

To restate those points:

1. Define what you want to know. Consider ways that you or others have measured similar problems. What you want to know might be easier to see than you thought.
2. It’s valuable to measure things that you aren’t certain about if they are important to be certain about.
3. Make estimates about what you think will happen, and calibrate those estimates to understand just how uncertain you are about outcomes.
4. Determine a level of certainty that will help you feel more confident about a decision. Additionally, determine how much information will be needed to get you there.
5. Determine how much effort it might take to gather that information.
6. Understand that it probably takes less effort than you think to reduce uncertainty.

The crux of the book revolves around restating measurement from “answer a specific question” to “reduce uncertainty based on what you know today”.

Measure to reduce uncertainty #

Before reading this book, I thought about data analysis as a way to find an answer to a question. I’d go in with a question, I’d find data, and thanks to that data, I’d magically know the answer. However, that approach only works with specifically-defined questions and perfect data. If I want to know “how many views did a specific documentation topic get last week” I can answer that straightforwardly with website metrics.

However, if I want to know “Was the guidance about how to perform a task more useful after I rewrote it?” there was really no way to know the answer to that question. Or so I thought.

Hubbard’s book makes the crucial distinction that data doesn’t need to exist to directly answer that question. It merely needs to make you more certain of the likely answer. You can make a guess about whether or not it was useful, carefully calibrating your guess based on your knowledge of similar scenarios, and then perform data analysis or measurement to improve the accuracy of your guess. If you’re not very certain of the answer, it doesn’t take much data or measurement to make you more certain, and thus increase your confidence in an outcome. However, the more certain you are, the more measurement you need to perform to increase your certainty.

Start by decomposing the problem #

If you think what you want to measure isn’t measurable, Hubbard encourages you to think again, and decompose the problem. To use my example, and #1 on his list, I want to measure whether or not a documentation topic was more useful after I rewrote it. As he points out with his first point, the problem is likely more observable than I might think at first.

“Decompose the measurement so that it can be estimated from other measurements. Some of these elements may be easier to measure and sometimes the decomposition itself will have reduced uncertainty.”

I can decompose the question that I’m trying to answer, and consider how I might measure usefulness of a topic. Maybe something is more useful if it is viewed more often, or if people are sharing the link to the topic more frequently, or if there are qualitative comments in surveys or forums that refer to it. I can think about how I might tell someone that a topic is useful, what factors of the topic and information about it I might point to. Does it come up first when you search for a specific customer question? Maybe then search rankings for relevant keywords are an observable metric that could help me measure utility of a topic.

You can also perform extra research to think of ways to measure something.

“Consider your findings from secondary research: Look at how others measured similar issues. Even if their specific findings don’t relate to your measurement problem, is there anything you can salvage from the methods they used?”

Is it business critical to measure this? #

Before I invest a lot of time and energy performing measurements, I want to make sure (to Hubbard’s second point in his list) that the question I am attempting to answer, what I am trying to measure, is important enough to merit measurement. This is also tied to points four, five, and six: does the importance of the knowledge outweigh the difficulty of the measurement? It often does, especially because (to his sixth point), the measurement is often easier to obtain than it might seem at first.

Estimate what you think you’ll measure #

To Hubbard’s third point, a calibrated estimate is important when you do a measurement. I need to be able to estimate what “success” might look like, and what reasonable bounds of success I might expect are.

Make estimates about what you think will happen, and calibrate those estimates to understand just how uncertain you are about outcomes.

To continue with my question about a rewritten topic’s usefulness, let’s say that I’ve determined that added page views, elevated search rankings, and link shares on social media will mean the project is a success. I’d then want to estimate what number of each of those measurements might be meaningful.

To use page views as an example for estimation, If page views increase by 1%, it might not be meaningful. But maybe 5% is a meaningful increase? I can use that as a lower bound for my estimate. I can also think about a likely upper bound. A 1000% increase would be unreasonable, but maybe I could hope that page views would double, and I’d see a 100% increase in page views! I can use that as an upper bound. By considering and dismissing the 1% and 1000% numbers, I’m also doing some calibration of my estimates—essentially gut checking them with my expertise and existing knowledge. The summary of How to Measure Anything that I linked in the first paragraph addresses calibration of estimates in more detail, as does the book itself!

After I’ve settled on a range of measurement outcomes, I can assess how confident I am that this might happen. Hubbard calls this a Confidence Interval. I might only be 60% certain that page views will increase by at least 5% but they won’t increase more than 100%. This gives me a lot of uncertainty to reduce when I start measuring page views.

One way to start reducing my uncertainty about these percentage increases might be to look at the past page views of this topic, to try to understand what regular fluctuation in page views might be over time. I can look at the past 3 months, week by week, and might discover that 5% is too low to be meaningful, and a more reasonable signifier of success would be a 10% or higher increase in page views.

Estimating gives me a number that I am attempting to reduce uncertainty about, and performing that initial historical measurement can already help me reduce some uncertainty. Now I can be 100% certain that a successful change to the topic should show more than 5% page views on a week-to-week basis, and maybe am 80% certain that a successful change would show 10% or more page views.

When doing this, keep in mind another point of Hubbards:

“a persistent misconception is that unless a measurement meets an arbitrary standard….it has no value….what really makes a measurement of high value is a lot of uncertainty combined with a high cost of being wrong.”

If you’re choosing to undertake a large-scale project that will cost quite a bit if you get it wrong, you likely want to know in advance how to measure the success of that project. This point also underscores his continued emphasis on reducing uncertainty.

For my (admittedly mild) example, it isn’t valuable for me to declare that I can’t learn anything from page view data unless 3 months have passed. I can likely reduce uncertainty enough with two weeks of data to learn something valuable, especially if my uncertainty level is in relatively low (in this example, in the 40-70% range).

Measure just enough, not a lot #

Hubbard talks about the notion of a 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.

Knowing the median value of a population can go a long way in reducing uncertainty. Even if you can only get a seemingly-tiny sample of data, this rule of five makes it clear that even that small sample can be incredibly valuable for reducing uncertainty about a likely value. You don’t have to know all of something to know something important about it.

Do something with what you’ve learned #

After you perform measurements or do some data analysis and reduce your uncertainty, then it’s time to do something with what you’ve learned. Given my example, maybe my rewrite increased page views of the topic by 20%, something I’m now fairly certain is a significant degree, and it is now higher in the search results. I’ve now sufficiently reduced my uncertainty about whether or not the changes made this topic more useful, and I can now rewrite similar topics to use a similar content pattern with confidence. Or at least, more confidence than I had before.

Overall summary #

My super abbreviated summary of the book would then be to do the following:

1. Start by decomposing the problem
2. Ask is it business critical to measure this?
3. Estimate what you think you’ll measure
4. Measure just enough, not a lot
5. Do something with what you’ve learned

I recommend the book (with judicious skimming), especially if you need some conceptual discussion to help you unravel how best to measure a specific problem. As I read the book, I took numerous notes about how I might be able to measure something like support case deflection with documentation, or how to prioritize new features for product development (or documentation). I also considered how customers might better be able to identify valuable data sources for measuring security posture or other events in their data if they followed many of the practices outlined in this book.