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Last month a friend asked me "what's the best resource you've read thus far on human decision-making?" 1/
After thinking about it, I decided to recommend her one of the first such resources I ever encountered, seven years ago: Doug Hubbard (@hubbardaie)'s How to Measure Anything. 2/ amazon.com/How-Measure-An…
While I don't agree with every word of it, How to Measure Anything is quite possibly the most important and useful book I've ever read on any topic. Here's why. 3/
Its central argument is that we have been thinking about #measurement all wrong. Measurement is not an act of observation disconnected from any larger plan, but rather *an optimization strategy for reducing uncertainty about decisions.* 4/
This is a radical reframing of the purpose of measurement that would revolutionize organizational #decisionmaking if applied at scale. It carries a number of MAJOR philosophical and practical ramifications. 5/
First it positions the decision-making process not just as a nice bonus for #research, #evaluation, #forecasting, and #datascience projects if things go really well -- but as THE WHOLE POINT OF DOING THEM AT ALL. 6/
Which in turn implies that the common phrase "data-driven decision-making" has it backwards. Instead, we should be practicing decision-driven data-making! Because #data has literally no value to us if it can't help us make a decision. 7/
Second, it encourages a way of thinking about the world that makes perfect sense as soon as you stop to consider it, yet is completely foreign to most organizational cultures. 8/
Look again at that definition of measurement: it's about "reducing uncertainty." In other words, uncertainty exists on a continuum. Admitting you're unsure about things is not the end of the conversation. You can have more or less uncertainty, and less = better. 9/
In reality, decisions are ALWAYS about managing uncertainty. You can't know the outcomes in advance. So the value in data is not in the single number it gives you, but rather how it helps you get *less uncertain* about the outcomes of your decisions. 10/
Third, Hubbard argues that if something matters, it must leave some kind of observable trace. Therefore, *everything* (that matters) is measurable, even things that most people would consider to be beyond the realm of quantification. 11/ 
Hubbard gives lots of examples of how these so-called "intangibles" can be quantified in the book. ⬇️⬇️12/
Throughout the book, Hubbard relentlessly cheerleads for the idea that measurement is easier than you think. He points out that our #intuition naturally models decisions all the time - as long as we can understand a situation, we are already modeling it in our heads. 13/
He further deduces that most of the value in measurement comes from the first observations; contrary to conventional wisdom, the more data you have, the LESS useful additional data will be (because it doesn't reduce your uncertainty as much). 14/
This means Hubbard's method can be used even in extremely information-poor environments, and in fact thrives in those contexts. 15/
How to Measure Anything is packed to the gills with tools and techniques to help readers realize the potential of these insights--including templates and spreadsheets you can download from howtomeasureanything.com. It's GREAT for autodidacts in that respect. 16/
Ultimately, these all fit like jigsaw puzzle pieces into Hubbard's Applied Information Economics technique, which is a universal methodology for making any decision. 17/
I don't mean to oversell. How to Measure Anything isn't perfect. One critique: by default we make decisions intuitively, so the decision that a different approach is needed has to be intuitive as well. The book doesn't say much about how or when to make that happen. 18/
And while the book does a great job explaining complex ideas in simple terms, it could benefit from better editing. The latest edition (3rd) has a ton of digressions that distract from its core points. I actually prefer the 2nd edition because of this. 19/
Even so, it amazes me that How to Measure Anything isn't more widely known in the social sector. 20/
It has "Business" in the subtitle, but if anything, its lessons are even more relevant to realms where stakeholders must balance diverse and hard-to-quantify goals while ensuring they make thoughtful choices. That's most of #philanthropy, #government and #impinv right there! 21/
Learning to approach #management and life challenges with a probabilistic mindset is incredibly empowering. Among other things, it relieves the stress and embarrassment that comes from having one's predictions revealed to be wrong--because we're all wrong sometimes. 22/
It is also a recipe for far more cost-effective and impactful use of resources whenever any analytical process, including research and evaluation, is called for. 23/
Bottom line: I believe if foundations, donors, impact investors, government agencies, and major nonprofits applied these principles on a more routine basis, the world would be a radically different--and hopefully better--place. /fin
