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

Product Information

Nothing is impossible to measure. We’ve measured concepts that people thought were immeasurable, like customer/employee satisfaction, brand value and customer experience, reputation risk from a data breach, the chances and impact of a famine, and even how a director or actor impacts the box office performance of a movie. If you think something is immeasurable, it’s because you’re thinking about it the wrong way. Unfortunately, in real life underlying processes tend to be unstable. For a trivial example of a known-to-not-be-stable process consider weather. Let's say I live outside of tropics and I measure air temperature over, say, 60 days. Will my temperature estimates provide a good forecast for the next month? No, they won't because the year has seasons and my "population" of days changes with time.

How to Measure Anything Book | Douglas Hubbard

Information can affect people’s behavior (e.g. common knowledge of germs affects sanitation behavior). Number of things (namely propositions, definitions, rules/instructions) I’ve written down that seem likely to be useful reference later. Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfactionThis isn’t to say that the variables you’re measuring now are “bad.” What we’re saying is that uncertainty about how “good” or “bad” a variable is (i.e. how much value they have for the predictive power of the model) is one of the biggest sources of error in a model. In other words, if you don’t know how valuable a variable is, you may be making a measurement you shouldn’t – or may be missing out on making a measurement you should. And if the distribution is symmetrical, then the mathless table gives us a 90% CI for the mean as well as for the median. The second form of the question is useful because the answer is often more straightforward and it leads to the answer to the other question. It also forces us to think about the likelihood of different observations given a particular hypothesis and what that means for interpreting an observation. It is not too bold a statement to say that a software development project is one of the riskiest investments a business makes. For example, the chance of a large software project being canceled increases with project duration. In the 1990s, those projects that exceeded two years of elapsed calendar time in development had a default rate that exceeded the worst rated junk bonds (something over 25%).”

How to Measure Anything: Finding the - Yumpu Read Book [PDF] How to Measure Anything: Finding the - Yumpu

We also found that the Marines were measuring variables that provided a lot less value. More on that later. Updated decision model: The AIE analyst updates the decision model based on the results of the measurements.

Customer reviews

When measuring risk, we don’t just want to know the “average” risk or benefit. We want to know the probability of a huge loss, the probability of a small loss, the probability of a huge savings, and so on. That’s what Monte Carlo can tell us. Sometimes you’ll want to start by decomposing an uncertain variable into several parts to identify which observables you can most easily measure. For example, rather than directly estimating the cost of a large construction project, you could break it into parts and estimate the cost of each part of the project. If we use regression modeling with historical data, we may not need to conduct a controlled experiment. Perhaps, for example, it is difficult to tie an IT project to an increase in sales, but we might have lots of data about how something else affects sales, such as faster time to market of new products. If we know that faster time to market is possible by automating certain tasks, that this IT investment eliminates certain tasks, and those tasks are on the critical path in the time-to-market, we can make the connection. For example] if, hypothetically, we know that only 20% of the population will continue to shop at our store, then we can determine the chance [that] exactly 15 out of 20 would say so… [The details are explained in the book.] Then we can invert the problem with Bayes’ theorem to compute the chance that only 20% of the population will continue to shop there given [that] 15 out of 20 said so in a random sample. We would find that chance to be very nearly zero…

7 Simple Principles for Measuring Anything - Hubbard Decision

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.” Wow, this is really exciting. I thought at first, "Man, quantifying my progress on math research sounds really difficult. I don't know how to make it more than a measure of how happy I feel about what I've done." In most cases, we want to compute the VoI for a range of values rather than a binary succeed/fail. So let’s tweak the advertising campaign example and say that a calibrated marketing expert’s 90% CI for sales resulting from the campaign was from 100,000 units to 1 million units. The risk is that we don’t sell enough units from this campaign to break even. Applying the same question to theft produces the result that if I steal your car and I get more utility out of having your car than you lose by not having it + the utility that you lose from psychological harm due to theft, insurance premiums rising, etc., I can internalize the cost and still come out ahead, so this sort of theft is not in oversupply.

The Rule of Five has another advantage over the t-statistic: it works for any distribution of values in the population, including ones with slow convergence or no convergence at all! It can do this because it gives us a confidence interval for the median rather than the mean, and it’s the mean that is far more affected by outliers. 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”

How to Measure Anything by Douglas W. Hubbard | Perlego [PDF] How to Measure Anything by Douglas W. Hubbard | Perlego

The speed of the convergence is a function of what your underlying distribution is. If it's normal (Gaussian), your mean estimate will converge at the same speed regardless of how high or low the variance of the distribution is. If it's, say, a Cauchy distribution then the mean estimate will never converge.An MC simulation uses a computer to randomly generate thousands of possible values for each variable, based on the ranges we’ve estimated. The computer then calculates the outcome (in this case, the annual savings) for each generated combination of values, and we’re able to see how often different kinds of outcomes occur. Hubbard’s book includes two case studies in which Hubbard describes how he led two fairly different clients (the EPA and U.S. Marine Corps) through each phase of the AIE process. Then, he closes the book with the following summary: