Book review: How to measure anything


Few books can fill the category MUST READ. ”How to measure anything:” by Douglas W.Hubbard is on of these without doubt.

Here I want to share some interesting points I’ve found in it, without pretending to offer a solid link among them. But first some (personal) considerations.

Nowadays it seems that mathematical skills are very strong just after the graduation (of course I’m talking about ICT fields), then after some years of “coding” those skills are not practiced so much… and they literally dry up. It’s not uncommon to find a middle project manager (coming from coders/analysts) with economics, mathematical, statistical knowledge not well “established” or, better said, not practiced.

In some ways this book reflect this status quo and offer a solid knowledge about measurement, risk calculation and decision making from a managerial point of view. Studying and implementing CMMI I can find the same really needs expressed in PSP and TSP approaches.

Ok, now let see the book content.

Some pillars:

  • what we generally label as “intangible” can - actually - be measured
  • first we have to define the problem
  • to measure == to reduce the uncertainty

Literally everything can be measured, even the most “intangible” ones. Three wonderful examples:

  • Eratosthenes “measured” the Earth circumference 200 years BC
  • Enrico Fermi was famous also for his joke about the estimation of piano tuners in Chicago
  • Emily - the younger scientist publisher at 10 yo - dismissed the real efficacy of a “therapeutic touch”

Some clear definitions:

  • the concept of measurement: definition of measure
  • the object of measurement: what we want to measure
  • the measurement methods: procedures and empirical observations

Very practical - and an effective “mantra” throughout the whole text - is the definition of measurement:

a set of observations that reduces the uncertainty where the result is quantitatively expressed

A clarification chain can help us:

  1. if it matters then is observable
  2. if it’s observable then it is by means of a quantity (or a set of possible values)
  3. if it’s observable by a set of possible values then it is measurable

Four useful things to remember when faced with a measurement issue:

  1. your problem is not so unique
  2. you have more data than you need
  3. you need less data than you believe
  4. there is a useful measurement easier than what you really believe

First you have to clarifying your measurement problem; here are some questions:

  1. What is the decision we have to support?
  2. What actually is the objective of our measurement?
  3. Why such a objective is so important for our decision^
  4. What we know now about the objective?
  5. What is the value of further measuring it?

Why an information is valuable for our business?

  • it reduces the uncertainty of a decision that has economic consequences
  • it influences people, with economic consequences
  • it have value by itself

The cost of a wrong decision is equivalent to the difference between the decision and the best possible alternative (= the one we take in presence of perfect information).

Decomposition and reality sampling (spot, serial, clustered) can being really useful tools.

An interesting point is made “against” human judgement. As human beings we are not perfect, rather, we are really biased “brains”. Take into account this considerations when faced with “expert field” estimations.

Finally the author summarize his universal measurement method: the Applied Information Economics.

More info about the book can be found on the companion website.


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