Free PDF How to Measure Anything: Finding the Value of Intangibles in Business, by Douglas W. Hubbard
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How to Measure Anything: Finding the Value of Intangibles in Business, by Douglas W. Hubbard
Free PDF How to Measure Anything: Finding the Value of Intangibles in Business, by Douglas W. Hubbard
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Amazon.com Review
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 How can we measure the population of fish in a lake? And how is that like measuring unsatisfied customers who didn’t complain or measuring security breaches that were not detected? How can we isolate the effect advertising has on sales when a vast amount of unknowns also affect sales? How did an 11-year old girl use a simple measurement to debunk a popular practice in medicine? How did intelligence analysts in WWII estimate the monthly German tank production by an analysis of serial numbers of captured tanks? How do we measure quality, risk or innovation? How do we know what to measure in the first place? The answers are easier than you might think. The idea that some things are utterly immeasurable is based on just three common misconceptions. As I explained in How to Measure Anything, the three misconceptions can be overcome and powerful measurement methods can be applied to resolve just about any problem. The misconceptions are Concept, Object and Method (you can remember them as .com). The concept of measurement refers to the meaning the word “measurement†is assumed to have. Some things are thought to be immeasurable only because it is believed that measurement must be some exact value. But the more pragmatic scientific approach to the term measurement is to treat it as quantified uncertainty reduction based on observation. If you have a wide range of possible values for the percentage of customers who would prefer a new product, all you really need is a reduction in that uncertainty to make a better bet about a new product. The second misconception about measurement is the objective of measurement itself. If “strategic alignmentâ€, or “employee empowerment†seem immeasurable, it is only because they are – initially – ambiguous. But if they are important to a business, then they must have observable consequences. They must have some impact on some decision (otherwise they wouldn’t need to be measured at all). And so they must be detectable in some manner, directly or indirectly. The third misconception is about methods. Obscure but well-developed methods already exist for more types of measurement problems than most managers realize. Controlled experiments, variations on random sampling methods, and some very simple but non-obvious methods can be used in many practical business situations. While many measurements feel daunting at first, the fact is that we often have more data than we think, we need less data than we think, and getting more data through observation is simpler than we think. And, above all else, no matter how challenging a measurement problem appears, we should assume that we are not the first to measure something like it. Any measurement problem you encounter will very likely already have a practical solution. You only need to know about it. Once these imaginary obstacles have been overcome, there are practical measurement solutions that can be applied to any uncertain decision. We can quantify any uncertainty and then compute the value of reducing that uncertainty by measurement. Where the value of information about a measurement is very high, my book explains how to employ sampling, controlled experiments, and even more methods in a way that makes it approachable for any manager.
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Review
"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 and 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 "Now, performance measures can be defined for even the most difficult problems. 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 from How to Measure Anything, First Edition "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...Hubbard helps us by showing us that when we seek metrics to solve problems, we are really trying to know something 'better than we know it now,' to put something into context, to find insight to help us get our jobs done, to be more successful, to discover things, or to build things. 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, Ph.D., M.D., Chief Technology Officer at 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." —Strategic Finance "Hubbard has made a career of finding ways to measure things that other folks thought were immeasurable. Quality? The value of telecommuting? The risk of IT project failure? 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." —ComputerWorld, August 2007 "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 & defense, healthcare, politics, etc." —Ricardo Valerdi, PhD, Lecturer, MIT "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.
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Product details
Hardcover: 320 pages
Publisher: Wiley; 2 edition (April 12, 2010)
Language: English
ISBN-10: 0470539399
ISBN-13: 978-0470539392
Product Dimensions:
6.3 x 1.1 x 9.3 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
157 customer reviews
Amazon Best Sellers Rank:
#98,489 in Books (See Top 100 in Books)
This was a required text for a class for my Masters of Science in Organizational Leadership. I was terrified-I thought this was a statistics course. I had nothing to fear. This guy is a genius. The formulas he has developed to measure things that you would assume are impossible to measure are amazing. You don't need to know math-the equations are in the excel spreadsheets you download! Think of something you don't think you could measure. You can measure it, and this book shows you how. Very interesting and understandable, even for a math dummy like me.
My Ph.D. coursework revolved around advanced quantitative methods, and therefore I did consider many real-world things to be immeasurable. Indeed, lots of practical cases involve small sample sizes, a large number of factors that can influence the outcome, and many other sources of uncertainty. Such problems are not very amenable to advanced quantitative methods. They may appear absolutely hopeless, but not if you switch to the decision making framework described in this book.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.
Highly recommended.Most capital investments are poorly analyzed. Doug Hubbard provides remedies for the common shortcomings: o Typical cost-benefit analyses use single-value best estimates for inputs; these ignore or inadequately address risk and uncertainty. Also, often, important benefits are omitted because they are "intangibles" such as "improved customer service." Remedies in the book: Capture expert judgments as probability distributions; then solve the forecast model with Monte Carlo simulation. Decompose and explicitly represent former-intangibles in measurable units. o Multi-criteria scoring approaches often feel good yet have little theoretical foundation. They are entirely subjective and have not been shown to improve decision-making. Remedy: clarify the business (or other) objective and craft a quantified decision policy accordingly. Judge and/or model inputs in meaningful, quantitative terms.Everything important to a decision should be in a forecasting model. And everything in those models is either structural or quantified.For me, three primary themes emerge in the book:1. Calibration. Hubbard asserts that everything can be quantified--and he enjoys challenging individuals and groups to find any exception. Most people, even if expert in their field, are biased when making judgments. Hubbard shows ways to "calibrate" these experts (including the reader) with perhaps a half-day of practice. Most readers (as did I) will find that they initially fare poorly on the engaging calibration exercises in the book. We suffer from overconfidence and other cognitive biases. With feedback and practice, most people can quickly improve at assessing probabilities and probability distributions, and confidence ranges.2. What needs to be measured further? The most interesting calculation in the book is a form of sensitivity analysis. Which variables are most important to measure further? Hubbard calculates the value of perfect information for each variable with a straightforward expected opportunity loss calculation. Though an analysis may have dozens of identified variables with uncertainty, in his experience typically only 1 to 3 variables are worthy of further measurement.3. Deliberately seek information about the most-important risks and uncertainties where the additional time and cost are justified. This usually means obtaining more data with targeted investigation. Hubbard offers these encouraging maxims: o It has been done before o You have more data than you think o You need less data than you think o It is more economical than you thinkAdditional highlights of the book include: o Abundant war story examples. Much of Hubbard's work has been in the information technology (IT) sector. I especially enjoyed the case about forecasting fuel consumption for the U.S. Marine Corps. o Demystifying parameters that the reader might initially think as "intangibles." For example, "Improved Customer Service" might be measured in terms of percent of customers re-ordering; percent of returns; average delivery time, etc. o Example calculations, all done with Microsoft® Excel. He hosts a website, [...], where the reader can download example calculation worksheets, additional calibration quizzes, papers, articles, and reader comments.The first edition is excellent. Upon learning of a second edition, I immediately bought the updated book for a fresh re-read. This was again a good investment of time and money. The new edition is updated, expanded (about 15%), and more crisp. The editing and layout are again excellent. Several references are now embedded to his other best-selling and companion book, The Failure of Risk Management.
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