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

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Because initial measurements often tell you quite a lot, and also change the value of continued measurement, Hubbard often aims for spending 10% of the EVPI on a measurement, and sometimes as little as 2% (especially for very large projects). 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. 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%).” The difference between EOL before and after a measurement is called the “expected value of information” (EVI). Initial calibrated estimates: First, the experts undergo calibration training. Then, they fill in the values (as 90% CIs or other probability distributions) for the variables in the decision model.

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Final value of information analysis: The AIE analyst runs a VoI analysis on each variable again. As long as this analysis shows information value much greater than the cost of measurement for some variables, measurement and VoI analysis continues in multiple iterations. Usually, though, only one or two iterations are needed before the VoI analysis shows that no further measurements are justified.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… 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.

How to Measure Anything: Finding the Value of Intangibles in

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. It concerns me. Because business is already full of dubious metrics that actually do harm. For instance, in programming, source lines of code (SLOC) per month is one metric that is used to gauge 'programmer productivity', but has come under extreme and rightful skepticism. This 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.If you are an experienced manager, you’ve heard of the latter type of “intangibles” in your own organization—things that presumably defy measurement of any type. The presumption of immeasurability is, in fact, so strong that no attempt is even made to make any observation that might tell you something about the alleged immeasurable that you might be surprised to learn. Here are a few examples: Success is a function of persistence and doggedness and the willingness to work hard for twenty-two minutes to make sense of something that most people would give up on after thirty seconds.” Define a decision problem and the relevant variables. (Start with the decision you need to make, then figure out which variables would make your decision easier if you had better estimates of their values.)