How to Measure Anything: Finding the Value of Intangibles in Business

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Preliminary measurement method designs: Focusing on the few variables with highest information value, the AIE analyst chooses measurement methods that should reduce uncertainty.

How to Measure Anything Book | Douglas Hubbard

The most important questions of life are indeed, for the most part, really only problems of probability. —Pierre Simon Laplace, Théorie Analytique des Probabilités, 1812” In fact, the more valuable predictive factor was whether or not the combat vehicle had been in a specific area before . It turns out that vehicle commanders, when maneuvering in an uncertain area (i.e. landmarks, routes, and conditions in that area they had never encountered before), tend to keep their engines running for a variety of reasons. That burns fuel. Modeling the world mathematically is as uniquely a human trait as language or art, but you would rarely find anyone complaining of being “reduced to a poem” or “reduced to a painting.” One major obstacle to better quantitative analysis is a profound misconception of measurement. For that, we can blame science, or at least how science is portrayed to the public at large. To most people, measurement should result in an exact value, like the precise amount of liquid in a beaker, or a specific number of this, that, or the other.Or, even easier, make use of the 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.” Understanding how to measure uncertainty is key to measuring risk. Understanding risk in a quantitative sense is key to understanding how to compute the value of information. Understanding the value of information tells us what to measure and about how much effort we should put into measuring it.”

Measuring - BBC Teach Measuring - BBC Teach

What if you want to figure out the cause of something that has many possible causes? One method is to perform a controlled experiment, and compare the outcomes of a test group to a control group. Hubbard discusses this in his book (and yes, he’s a Bayesian, and a skeptic of p-value hypothesis testing). For this summary, I’ll instead mention another method for isolating causes: regression modeling. Hubbard explains: Other chapters discuss other measurement methods, for example prediction markets, Rasch models, methods for measuring preferences and happiness, methods for improving the subjective judgments of experts, and many others.  If you have the list of desired things ready, there should be an ETA on the work time necessary for each desired thing as well as confidence on that estimate. Confidence varies with past data and expected competence, e.g. how easily you believe you can debug the feature if you begin to draft it. Or such. Then you have a set of estimates for each implementable feature. A measurement is an observation that quantitatively reduces uncertainty. Measurements might not yield precise, certain judgments, but they do reduce your uncertainty. In most cases, we’ll estimate the values in a population by measuring the values in a small sample from that population. And for reasons discussed in chapter 7, a very small sample can often offer large reductions in uncertainty.

Amount of material produced (in propositions or subsections of proof) that, if correct, will actually be part of my answer in the end. Scientists distinguish two types of measurement error: systemic and random. Random errors are random variations from one observation to the next. They can’t be individually predicted, but they fall into patterns that can be accounted for with the laws of probability. Systemic errors, in contrast, are consistent. For example, the sales staff may routinely overestimate the next quarter’s revenue by 50% (on average). And in the end I'm going to have to make difficult calls, like how desirable it is for us to have weird chunks of code that look strange by consume noticeably fewer resources. By 1999, I had completed the… Applied Information Economics analysis on about 20 major [IT] investments… Each of these business cases had 40 to 80 variables, such as initial development costs, adoption rate, productivity improvement, revenue growth, and so on. For each of these business cases, I ran a macro in Excel that computed the information value for each variable… [and] I began to see this pattern: * The vast majority of variables had an information value of zero… * The variables that had high information values were routinely those that the client had never measured… * The variables that clients [spent] the most time measuring were usually those with a very low (even zero) information value… …since then, I’ve applied this same test to another 40 projects, and… [I’ve] noticed the same phenomena arise in projects relating to research and development, military logistics, the environment, venture capital, and facilities expansion.