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

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Observer bias: the very act of observation can affect what you observe. E.g. in one study, researchers found that worker productivity improved no matter what they changed about the workplace. The workers seem to have been responding merely to the fact that they were being observed in some way. 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 Workbook: Finding the Value of

In a business case, the economic value of measuring a variable is usually inversely proportional to how much measurement attention it usually gets. 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. Another example: I took statistics on how my friends played games that involved bidding, such as Liar's Poker. I found that they typically would bid too much. Therefore a measurement of how many times someone had the winning bid was a high predictor of how they would perform in the game-people who bid high would typically lose. What if you already make measurements? Let’s say you collect data and have some system – even an empirical, statistics-based quantitative method – to make measurements. Your effectiveness may be severely hampered by measuring things that, at the end of the day, don’t really matter. Much of the value we’ve generated for our clients has come through measuring variables that the client thought were either irrelevant or too difficult to measure. But often, these variables provide far more value than anyone thought!This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. 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.

How to Measure Anything in Cybersecurity Risk | Wiley How to Measure Anything in Cybersecurity Risk | Wiley

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." To run an MC simulation we need not just the 90% CI for each variable but also the shape of each distribution. In many cases, the normal distribution will work just fine, and we’ll use it for all the variables in this simplified illustration. (Hubbard’s book shows you how to work with other distributions). If it’s something important and something uncertain, you have a cost of being wrong and a chance of being wrong.

Work through the consequences. If the value is surprisingly high, or surprisingly low, what would you expect to see? Hubbard then zooms out to a big-picture view of measurement, and recommends the “instinctive Bayesian approach”:

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

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.Once you know what it’s worth to measure something, you can put the measurement effort in context and decide on the effort it should take. 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. This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable, " including customer satisfaction, organizational flexibility, technology risk, and technology ROI. Given a particular observation, it may seem more obvious to frame a measurement by asking the question “What can I conclude from this observation?” or, in probabilistic terms, “What is the probability X is true, given my observation?” But Bayes showed us that we could, instead, start with the question, “What is the probability of this observation if X were true?” Adds even more intuitive explanations of powerful measurement methods and shows how they can be applied to areas such as risk management and customer satisfaction