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

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Decision optimization: The final business decision recommendation is made (this is rarely a simple “yes/no” answer). 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. Initial research: Interviews and secondary research to get familiar on the nature of the decision problem.

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

Identified metrics procedures: Procedures are put in place to measure some variables (e.g. about project progress or external factors) continually. Suppose you enter this formula on cell A1 in Excel. To generate (say) 10,000 values for the maintenance savings value, just (1) copy the contents of cell A1, (2) enter “A1:A10000” in the cell range field to select cells A1 through A10000, and (3) paste the formula into all those cells.We must also distinguish precision and accuracy. A “precise” measurement tool has low random error. E.g. if a bathroom scale gives the exact same displayed weight every time we set a particular book on it, then the scale has high precision. An “accurate” measurement tool has low systemic error. The bathroom scale, while precise, might be inaccurate if the weight displayed is systemically biased in one direction – say, eight pounds too heavy. A measurement tool can also have low precision but good accuracy, if it gives inconsistent measurements but they average to the true value. What’s the really simple question that makes the rest of the measurement moot? First see if you can detect any change in research quality before trying to measure it more comprehensively. For many decisions, one decision is required if a value is above some threshold, and another decision is required if that value is below the threshold. For such decisions, you don’t care as much about a measurement that reduces uncertainty in general as you do about a measurement that tells you which decision to make based on the threshold. Hubbard gives an example:

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

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.Updated decision model: The AIE analyst updates the decision model based on the results of the measurements. Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction 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. Make a decision and act on it. (When you’ve done as much uncertainty reduction as is economically justified, it’s time to act!)

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

Hubbard says a few things in support of this approach. First, he points to some studies (e.g. El-Gamal & Grether (1995)) showing that people often reason in roughly-Bayesian ways. Next, he says that in his experience, people become better intuitive Bayesians when they (1) are made aware of the base rate fallacy, and when they (2) are better calibrated.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. 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.