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How to Measure Anything: Finding the Value of Intangibles in Business Hardcover – April 12, 2010
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Now updated with new research and even more intuitive explanations, a demystifying explanation of how managers can inform themselves to make less risky, more profitable business decisions
This insightful and eloquent book will show you how to measure those things in your own business that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI.
- 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
- Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods
- Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas
- Offers practical methods for measuring a variety of "intangibles"
- Adds recent research, especially in regards to methods that seem like measurement, but are in fact a kind of "placebo effect" for management – and explains how to tell effective methods from management mythology
Written by recognized expert Douglas Hubbard-creator of Applied Information Economics-How to Measure Anything, Second Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.
How Everything Can Be Measured
Amazon-exclusive content from author Douglas Hubbard
"I use this book as a primary reference for my measurement class at MIT. The students love it because it provides practical advice that can be applied to a variety of scenarios, from aerospace and defense, healthcare, politics, etc." ---Ricardo Valerdi, Ph.D., Lecturer, MIT --This text refers to the MP3 CD edition.
Top customer reviews
One simple idea is that, as long as the experiment reduces the costly uncertainty by the amount that is larger than the cost of research, it is worth performing. For instance, many experiments that may appear meaningless to a "classical" statistician because of their small sample size can be well justified once the benefit of uncertainty reduction is taken into account. The flipside is that if a study is well funded, well designed and replicated from the statistical viewpoint, it can still be useless. If our goal is to reduce the uncertainty to below a pre-specified threshold, then the "statistically" large sample size may still be unable to do the job. If the study is too expensive compared to the value of reduced uncertainty, it is not worth doing either. Another negative scenario is when we invest resources into measuring a variable that would make a small contribution to the uncertainty of the final outcome even if we knew the exact distribution of the variable.
The book provides numerous examples that would be hard to crack for a "classical" statistician, and yet they are very amenable to the Applied Information Economics method developed by Mr. Hubbard. I can highly recommend it to all who "do not believe in statistics", as well as to the young quantitative analysts who want to expand the set of applications they can handle successfully.
There have been a couple of reviews stating that the book doesn't offer practical ways to measure intangibles. One of the lessons from this book is that by their very nature, intangibles often have to be measured indirectly by observing other variables and then discovering a correlation. Statistical analysis can handle the latter, but choosing an appropriate set of other variables can be very challenging, often requiring clever outside-the-box thinking.
Fundamentally, this book is about method, process, first principles - ideas about measurement and information applied in a business context - and not so much about their technological implementation; however, it's interesting to see how the former fares as the latter progresses. If the concepts remain unscathed or are reinforced, one can conclude that they continue to be valid and useful.
Since the original 2007 publication date, "big data," "analytics," and "data science" have become everyday business terms. In the chapter, "Illusion of Intangibles," the author lists four useful measurement assumptions. The second is, "You have more data than you think." Big data in the business context is based on the notion that businesses collect and store mountains of data. So you do have more data than you think, a LOT more. Often however, much of it is recorded for other purposes and seems on the surface to have little value otherwise. However, this book suggests we challenge this assumption. If all this data could be somehow collected and analyzed, it's possible that there could be ways to extract the latent information hidden inside.
There are lots of recent examples of this kind of analysis. One is the 2009 Google Flu Trends' prediction of the advance of the H1N1 pandemic in 2009. Here is a wonderful example of what the author refers to as a Fermi problem: (cleverly) using what you do know to measure indirectly what you are looking for. One of the most valuable aspects of this analysis - aside that it was essentially free - was that it made predictions in near real-time. The notion that it's possible to track the activity of a pandemic by analyzing search terms entered in a web browser is quite remarkable - or it was at the time. It's actually common-place now. It's also indicative that the concepts discussed in the book are not only valid, relevant, and useful, but are possibly even more so now given the access to data and computation that drive big data, analytics, and data science in business.
Many readers will find Mr. Hubbard's discussion on estimation and confidence intervals useful. This is an area where the statisticians and math types obfuscate the ideas for everyone else; Mr. Hubbard blows past that cloud of smoke to present very straightforward ideas of how you can estimate a 90% confidence interval on something of interest. The discussion on "calibrating estimators"; where estimators means humans, is interesting if not totally convincing. Nonetheless the exercises provide insight into how to check for bias in estimates and improve your own abilities.
In these days of big data, many who read his messages on the value of measurement (which can be zero, in which cases there is no point to measure) may resonate with those who ask "what are we doing with all this". I suspect Mr. Hubbard would shrink big data down considerably after he threw out all the worthless measurements.