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

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In other words, the quantitative method you use to make measurements and decisions only has to beat the alternative. Any empirical method you incorporate into your process can improve it if it provides more practical and accurate insight than what you were doing before. Just FYI, I think Hubbard knows this and wrote "A measurement is an observation that quantitatively reduces uncertainty" because he was trying to simplify and avoid clunky sentences. E.g. on p. 146 he writes: For whomever reads this that is as innumerate as I am and is confused about the example simulation with the excel formula "=norminv(rand(), 15, (20–10)/3.29)", I hope my explanation below helps (and is correct!). One of the strengths of the book is its practicality. The author provides numerous examples and case studies to illustrate the concepts he introduces, making it easy to understand and apply the ideas to real-world situations. So, I agree that you accomplished these desired things. However, before you accomplished them, how accurately did you know how much time they would take, or how useful they would be?

How to Measure Anything Quotes by Douglas W. Hubbard - Goodreads How to Measure Anything Quotes by Douglas W. Hubbard - Goodreads

After following the above steps, Hubbard writes, “the measurement instrument should be almost completely formed in your mind.” But if you still can’t come up with a way to measure the target variable, here are some additional tips:

We also found that the Marines were measuring variables that provided a lot less value. More on that later. If a measurement matters at all, it is because it must have some conceivable effect on decisions and behaviour. If we can't identify a decision that could be affected by a proposed measurement and how it could change those decisions, then the measurement simply has no value” That might work in an academic setting, but doesn't work in a real-life business setting where you're not going to tie up two programmers (or two teams, more likely) reimplementing the same stuff just to satisfy your curiosity.

How to Measure Anything - Wiley Online Library

If your goal is gauging the effectiveness of this or that approach (agile vs. waterfall? mandated code formatting style or no? single or pair programming? what compensation structure? etc.), then it's slightly less trivial, but you can use some "fuzzy" metrics: for instance, classify "desired things" into categories (feature, bug fix, compatibility fix, etc.), and measure those per unit time. He also asks people to look more closely at each bound (upper and lower) on their estimated range. A 90% CI “means there is a 5% chance the true value could be greater than the upper bound, and a 5% chance it could be less than the lower bound. This means the estimators must be 95% sure that the true value is less than the upper bound. If they are not that certain, they should increase the upper bound… A similar test is applied to the lower bound.”

The bottom line is simple: Measurement is a process, not an outcome. It doesn’t have to be perfect, just better than what you use now. Perfection, after all, is the perfect enemy of progress. 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.

How to Measure Anything: Finding the Value of Explaining ‘How to Measure Anything: Finding the Value of

Anything can be measured. If a thing can be observed in any way at all, it lends itself to some type of measurement method. No matter how “fuzzy” the measurement is, it’s still a measurement if it tells you more than you knew before. And those very things most likely to be seen as immeasurable are, virtually always, solved by relatively simple measurement methods.The fish example was a special case of a common problem: population proportion sampling. Often, we want to know what proportion of a population has a particular trait. How many registered voters in California are Democrats? What percentage of your customers prefer a new product design over the old one? Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods It seems quite unclear what's meant by "the difference between EOL before and after a measurement" (EOL of which option? is this in expectation?). 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.

Measuring - BBC Teach Measuring - BBC Teach

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:I like the coin example. In my experience the situation with clear choice is typical in small businesses. It often isn't worth honing the valuation models for projects very long when it is very improbably that the presumed second best choice would turn out to be the best. One possibility is that there are a very large number of things they could measure, most of which have low information value. If they chose randomly we might expect to see an effect like this, and never notice all the low information possibilities they chose not to measure. Each scientific discipline has its own specialized measurement methods. Hubbard’s book describes measurement methods that are often useful for reducing our uncertainty about the “softer” topics often encountered by decision-makers in business. If you followed the first three steps, then you’ve defined a variable you want to measure in terms of the decision it affects and how you observe it, you’ve quantified your uncertainty about it, and you’ve calculated the value of gaining additional information about it. Now it’s time to reduce your uncertainty about the variable – that is, to measure it.