You Can Measure Anything

I recently finished reading  How to Measure Anything - Finding the Value of Intangibles in Business - by Douglas W. Hubbard. This book was recommended to me by our management team as we work through our annual planning exercise. Below I wanted to share key learnings from this book:

1) On The Clarification Chain:

1. If it matters at all, it is detectable/observable. 2. If it is detectable, it can be detected as an amount (or range of possible amounts). 3. If it can be detected as a range of possible amounts, it can be measured.

2) On Definitions:

Uncertainty: The lack of complete certainty, that is, the existence of more than one possibility. The "true" outcome/state/result/value is not known. Measurement of Uncertainty: A set of probabilities assigned to a set of possibilities. For example: "There is a 60% chance this market will more than double in five years, a 30% chance it will grow at a slower rate, and a 10% chance the market will shrink in the same period." Risk: A state of uncertainty where some of the possibilities involve loss, catastrophe, or other undesirable outcome. Measurement of Risk: A set of possibilities each with quantified probabilities and quantified losses. For example: "We believe there is a 40% chance the proposed oil well will be dry with a loss of $12 million in exploratory drilling costs."

3) On The Measurement Inversion:

In a decision model with a large number of uncertain variables, the economic value of measuring a variable is usually inversely proportional to how much measurement attention it typically gets.

4) On Quick Glossary of Error:

Systemic error/bias: An inherent tendency of a measurement process to favor a particular outcome; a consistent bias. Random error: An error that is not predictable for individual observations; not consistent or dependent on known variables (although such errors follow the rules of probability in large groups). Accuracy: A characteristic of a measurement having a low systemic error—that is, not consistently over- or underestimating a value. In some fields this is used synonymously with "validity." Precision: A characteristic of a measurement having a low random error; highly consistent results even if they are far from the true value. In some fields of research, the terms "reliability" and "consistency" will be used in the same way.

5) On A Prior-Knowledge Paradox:

1. All conventional statistics assume (a) the observer had no prior information about the range of possible values for the subject of the observation, and (b) the observer does have prior knowledge that the distribution of the population is not one of the "inconvenient" ones. 2. The first above assumption is almost never true in the real world and the second is not true more often than we might think.

A highly recommended read in the areas of business, management and valuation.