- Hardcover: 432 pages
- Publisher: Wiley; 3 edition (March 17, 2014)
- Language: English
- ISBN-10: 1118539273
- ISBN-13: 978-1118539279
- Product Dimensions: 6.2 x 1.6 x 9.1 inches
- Shipping Weight: 1.5 pounds (View shipping rates and policies)
- Average Customer Review: 4.6 out of 5 stars See all reviews (60 customer reviews)
- Amazon Best Sellers Rank: #19,619 in Books (See Top 100 in Books)
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How to Measure Anything: Finding the Value of Intangibles in Business 3rd Edition
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From the Inside Flap
Anything can be measured. This bold assertion is the key to solving many problems in business and life in general. The myth that certain things can’t be measured is a significant drain on our nation’s economy, public welfare, the environment, and even national security. In fact, the chances are good that some part of your life or your professional responsibilities is greatly harmed by a lack of measurement – by you, your firm, or even your government. Regardless of your role in business, understanding the power of measurement will make you, those around you, and your organization more efficient and productive.
Using simple concepts to illustrate the hands-on application of advanced statistical techniques, How to Measure Anything, Third Edition reveals the power of measurement in our understanding of business and the world at large. This insightful and engaging book shows you how to measure those things in your business that you may have previously considered immeasurable, including: customer satisfaction, organizational flexibility, technology ROI, and technology risk. Offering examples that will get you to attempt measurements—even when it seems impossible—this book provides you with the underlying knowledge and the necessary steps for measuring anything, especially uncertainty and risk. This revised third edition provides even deeper insights into the fascinating practice of measuring intangibles, with a special emphasis on risk management and customer satisfaction. New and updated chapters also include:
- A philosophical discussion of different approaches to probabilities, including what is known as the “Bayesian” vs. “frequentist” interpretations of probability
- Information compiled from other popular works and compelling articles from Douglas W. Hubbard
- Enlightening new examples of where seemingly impossible measurements were resolved with surprisingly simple methods
- More measurement myths and other perceived obstacles to measurement debunked
A complete and updated resource with real-world case studies and an easy-to-follow format, How to Measure Anything, Third Edition illustrates how author Douglas Hubbard—creator of Applied Information Economics—has successfully applied his approach across various industries. You’ll learn how any problem, no matter how difficult, ill-defined, or uncertain, can lend itself to measurement using proven methods. Straightforward and accessible, this is the resource you’ll turn to again and again to measure the seemingly immeasurable.
From the Back Cover
Praise for the second edition of How to Measure Anything: Finding the Value of “Intangibles” in Business
“How to Measure Anything was already my favorite book (just ahead of Hubbard’s second book, The Failure of Risk Management) and one I actively promote to my students and colleagues. But the Second Edition, improving on the already exquisite first edition, is an achievement of its own. As a physicist and economist, I applied these techniques in several fields for several years. For the first time, somebody wrote together all these concerns on one canvas that is at the same time accessible to a broad audience and applicable by specialists. This book is a must for students and experts in the field of analysis (in general) and decision-making.”
—Dr. JOHAN BRAET, University of Antwerp, Faculty of Applied Economics, Risk Management and Innovation
“Doug Hubbard’s book is a marvelous tutorial on how to define sound metrics to justify and manage complex programs. It is a must-read for anyone concerned about mitigating the risks involved with capital planning, investment decisions, and program management.”
—JIM FLYZIK, former Government CIO, White House Technology Advisor and CIO magazine Hall of Fame Inductee
Praise for the first edition—The bestselling Business Math book two years in a row!
“I love this book. Douglas Hubbard helps us create a path to know the answer to almost any question, in business, in science, or in life . . . How to Measure Anything provides just the tools most of us need to measure anything better, to gain that insight, to make progress, and to succeed.”
—PETER TIPPETT, PhD, MD, Chief Technology Officer, CyberTrust, and inventor of the first antivirus software
“Interestingly written and full of case studies and rich examples, Hubbard’s book is a valuable resource for those who routinely make decisions involving uncertainty. This book is readable and quite entertaining, and even those who consider themselves averse to statistics may find it highly approachable.”
“This book is remarkable in its range of measurement applications and its clarity of style. A must-read for every professional who has ever exclaimed, ‘Sure, that concept is important, but can we measure it?’”
—Dr. JACK STENNER, cofounder and CEO of MetaMetrics, Inc.
“Hubbard has made a career of finding ways to measure things that other folks thought were immeasurable. Quality? The value of telecommuting? The benefits of greater IT security? Public image? He says it can be done—and without breaking the bank . . . If you’d like to fare better in the project-approval wars, take a look at this book.”
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Top Customer Reviews
There is, however, an important sense in which this book is deceptive. I fully agree with the author that careful quantitative analysis backed by empirical research and rigorous validation can improve decisions. However he does not emphasize the difficulty of doing this in large organizations. Most of his examples are unusual combinations of circumstances, and had his credibility as world-famous, outside, paid expert (okay, in the early examples he wasn’t so famous). And while the decisions are clearly improvements at the margin, they are a long way from global optima. I still believe the effort is worthwhile, partly because improvements at the margin can add up to the difference between organizational success and failure, and partly because of the positive effect of the example. But anyone reading this book and expecting to fix the world tomorrow will find that truth is very disruptive, and there are strong forces opposing disruption.
One specific issue is calibration. If you ask people for confidence intervals on things they don’t know, like Fermi’s famous number of piano tuners in Chicago, they tend to either underestimate their knowledge (“I have no idea”) or overestimate it (“90% confidence the answer is between 280 and 300”). The author is correct that most people can get calibrated with a little practice.
But this is calibration on average, and average is not what you need. Consider the problem of setting a point spread for a football game. It’s easy to calibrate it perfectly, set it to zero points and one team will win exactly half the time. Or set it to minus 3 points for every home team, and half the home teams will cover. If you used these to accept bets from the public, you’d get killed. The hard part of calibration is not just finding a rule that gives the right average results, but using all of your knowledge and still getting the right average results.
There is a psychological issue as well. The calibration training has no stakes other than pride. Calibrating with no stakes is like poker for play money. The game is a lot harder with the rent money on the table, facing an expert player with an equal stake. When people move from training to estimating important business parameters all kinds of behavioral biases will kick in, not to mention conflicted interests and even calculated deception. Mike Tyson may not be as smart as the author, but he was right that “everyone has a plan until they get hit.”
With an expert like the author to guide the process, I think these and other issues can be surmounted. But without that, I suspect most attempts to apply these ideas will collapse. Putting it another way, if you plan to use the material in this book in a large organization, be aware that knowing how is only the first easy step on a long and difficult journey. It’s worth doing, but mainly for the viral spread of ideas like quantification, measurement, validation and rationality; not because you have much chance of making significant direct improvements on decisions.
Even doing this yourself, without the constraints and conflicts of a large organization, it’s hard. In my experience, when you start thinking about anything interesting, you realize you’ve started with the wrong question. When you get the right question, you realize you’ve got the wrong data. The process of collecting the right data changes your fundamental view of the issue. Once you have the right view, you realize that your original application was wrong. If you can get through this process honestly, which is very difficult given psychological and social pressures to be consistent, you may have a valuable plan. Doing this with backers or partners can be much harder, and doing it in an established organization nearly impossible.
So I recommend this book, with the caveat that this stuff is harder than it sounds.
The opening of How to Measure Anything focuses on trying to both dispel of statistical myths that surround the topic, as well as try to specify the language and concepts that we need to approach measurement of things not clearly quantified. A major idea is focusing on uncertainty and how we can reduce it in order to make better decisions. Combining this with relatively sophisticated statistical tools such as Monte Carlo modelling, Hubbard outlines an approach that even those who avoided stats class in college can follow. The middle and back end of the book contain enough meat for the quantitatively inclined to dig into, but also is approachable enough for those with less experience. As Hubbard mentions it is about learning when and how to use the tool, not necessarily how to tool works; you don't need to build a car from the ground up in order to drive to work.
If we are providing a service “which values cannot easily be measured” - maybe we should think again about what we are trying to achieve. Some kind of observable consequence must be present if they matter at all? Right?
Measuring things just because they are easy to measure is pretty useless. What are the decisions you want to support? Will this measurement help this?
One thought experiment to find what is important to measure is “if we didn’t do this - how would you notice the difference?”