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

by Douglas W. Hubbard

Book Overview

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...

• Format: Hardcover
• Language: English
• ISBN: 0470539399
• ISBN13: 9780470539392
• Release Date: April 2010
• Publisher: John Wiley & Sons
• Length: 304 Pages
• Weight: 1.18 lbs.
• Dimensions: 1.2" x 6.4" x 9.3"

Customer Reviews

More specifically, how to measure anything that is especially important, including intangibles

"I wrote this book to correct a myth that permeates many organizations today: that certain things can't be measured." Douglas Hubbard goes on to note that he has made a career out of measuring the sorts of things many thought were immeasurable. Intangibles, for example, "that appear to be completely intractable to be measured...in a way that is economically justified." Hubbard notes that there are several common misconceptions about intangibles. He offers what he characterizes as a "universal approach," Applied Information Economics (AIE), to measure an intangible, providing with that explanation some "interesting methods for particular problems." He duly recognizes that only what is most important (tangible or intangible) should be measured; also, that what is currently most important may not retain that importance; and, that information needs change, sometimes significantly and unexpectedly. That said, basic questions must constantly be asked and answered: 1. What are our most important information needs? Why? 2. How best to obtain and then verify that information? 3. What will we then do with that information? 4. How can we then measure (accurately, consistently, and sufficiently) the impact of actions taken based on that information? To his credit, Hubbard makes every effort to provide information, explanations, and recommendations that are (in his words) as "simple as can be"; nonetheless, some of the material may prove daunting, at least it did to me. I appreciate the inclusion of dozens of real-world examples that illustrate key points. Hubbard also makes effective use of other reader-friendly devices, such as checklists inserted throughout his narrative. In his own words, here is how he organizes his material: In Section One (Chapters 1-3), he "makes the case that everything is measurable and offers some examples that should inspire readers to attempt measurements even when it seems impossible." In Section Two (Chapters 4-7), he "begins to get into more specific substance about how to measure things - specifically uncertainty, risk - and the value of information." In Section Three (Chapters 8-10), he "deals with how to reduce uncertainty by various methods of observation including random sampling and controlled experiments." And then in Section Four (Chapters 11-14), Hubbard offers "an eclectic collection of interesting measurement solutions and case examples." Many readers will appreciate having the Appendix (Pages 269-278) which provides both the questions and answers for various calibration tests, including "Calibration Survey for Binary: B" that also includes percentages to indicate degree of confidence that the respondent is correct. Earlier, I suggested that this is by no means an "easy read." It isn't. Nor will this book respond directly to every executive's immediate needs and objectives. However, it will generously reward those who need assistance with finding and measuring the intangibles in business if they abs

Quantifying Soft Knowledge

Perhaps the most frequent question from decision analysis team members is, "How do we get the inputs?" In most evaluations, there are several key variables about which we know little. Consider oil price, for example. We have abundant historical data, yet forecasting future prices is a daunting challenge. Doug Hubbard has written an entire book about capturing quantitative judgments. His approach differs from the usual decision analysis process. In a conventional analysis, we assume that that a subject matter expert (SME) can be identified for each key variable. Then, a skilled interviewer carefully elicits the SME's judgment through an interview process. Hubbard takes a different approach. People familiar with the type project are assembled and given calibration training. Becoming calibrated might take perhaps a half-day of practice exercises and feedback. Basically, being "calibrated" means that one can consistently provide judgments of 90% confidence intervals that avoid the "overconfidence" bias. The book provides several example quizzes for the reader to self-assess. Even though I was well-aware of the overconfidence bias, I still performed poorly on the self-assessment tests (history was never my strong subject!). Of course, the questions for a technical group would be crafted from topics within the area of interest. Whether (a) expert in the quiz subject matter or not and (b) being told in advance that people tend to be overconfident about the quality of their knowledge doesn't seem to affect the overconfident bias. Practice and feedback are the antidotes. Hubbard's training and consulting examples are engaging. It has been years since I've devoured a technical book so thoroughly. While the reader will pick-and-choose methods of most interest, the "measurement" topic is well-covered. The book contains many shortcuts and heuristics for rapid problem-solving. Many people never attempt to quantify intangibles. Yet, most people with some modest training are able to provide credible judgments in quantitative form. A sampling of topics includes: * Modeling and Monte Carlo simulation * Designing experiments for measurement * Decomposition * Heuristics for obtaining simple statistics * Value of perfect information, for screening which variables are worthwhile measuring * Bayes' rule (because we almost always have some prior information about the subject of the observation) * Cognitive biases How to Measure Anything is well-written and carefully edited. The companion Web site, [..], offers additional calibration questions, several calculation spreadsheets, and additional information. Persons reading this book will be the better for it.

Reducing the Uncertainty in Intangible Business Value . . .

Hubbard explains how to "find the value of intangibles in business." An excellent book and one which should be on every manager's book shelf. Hubbard has made what can be a deadly dull subject interesting and accessible. I found several examples for measuring exactly what I needed and always felt I could not measure. This book is a must read for leaders including the Master Six Sigma Blackbelt on your staff. Finding the value of intangibles in business has always been a challenge. How to Measure Anything is full of practical ideas for getting to a measurement. Measurement: reducing the uncertainty. As long as we are not willing to accept a best guess, or educated estimate, or range of possibilities for a difficult to measure item we will not move forward. Our decisions will be flawed. Hubbard put forth these four assumptions which I found to be most useful when thinking about measuring: 1. Your problem is not as unique as you think 2. You have more data than you think 3. You need less data than you think 4. There is a useful measurement that is much simpler than you think. Numbers can be used to confuse people; especially the gullible ones lacking basic skills with numbers. Therefore we, as leaders, must be committed to making sure the whole organization is data driven and understands the way we can reduce uncertainty through the straight forward techniques Hubbard explains. As he states, "The fact is that the preference for ignorance over even marginal reductions in ignorance is never the moral high ground." Hubbard gives us a very useful check list for a Universal Approach to Measurement: 1. What are you trying to measure? What is the real meaning of the alleged "intangible?" 2. Why do you care -- what's the decision and where is the "threshold?" 3. How much do you know now -- what ranges or probabilities represent your uncertainty about this? 4. What is the value of the information? What are the consequences of being wrong and the chance of being wrong, and what, if any, measurement effort would be justified? 5. Within a cost justified by the information value, which observations would confirm or eliminate different possibilities? For each possible scenario, what is the simplest thing we should see if that scenario were true? 6. How do you conduct the measurement that accounts for various types of avoidable errors (again, where the cost is less than the value of the information)? I especially enjoy the approach Hubbard takes to quantify the cost of making measurement based on the value of the information obtained. Too often, I have seen projects founder on either inaction to get data which would be of great value and little cost or, perhaps, the exact opposite -- spending great amounts of time and money to obtain relatively useless information. To emphasize: After reading Hubbard's excellent book on `How to Measure Anything,' I was able to immediately solve several measurement challenges for my CEO and Business Owner colleagu

Clear explanation making the complex simple.

Douglas Hubbard covers a broad landscape but does exactly what the title claims; it provides a guide to measuring anything. Hubbard builds from simple concepts to show the practical yet intuitively simple application of some rather advanced statistical techniques. The author's skill is in communicating complex ideas in an easy to follow and motivational flow that builds in a series of seemingly obvious steps. The book is both philosophical and practical. If one read no more than the first three chapters one's view of the world would be changed forever. Yet the later chapters cover many extremely simple illustrations of some complex statistical concepts. I particularly enjoyed his discussion of the value of information (chapter 7), Bayesian Statistics (chapter 10), and some advanced concepts such as measuring value via observable trade-offs and using prediction markets. No one reading just a portion of this book would walk away without a new insight. This book would be extremely useful to students in an MBA program or to those pursuing an advanced degree in one of the social sciences. It would provide a valued motivational reference to anyone studying Computer Science, Economics, or Applied Statistics. Anyone teaching or mentoring students in these disciplines might want to review this book for inclusion in their curriculum. The book also has considerable potential at helping someone working the area of Data Warehousing and Business Intelligence. For example, Steve and Nancy Williams have written a great book titled "The Profit Impact of Business Intelligence". In it they explain the case for managing BI projects as a portfolio of risky investments. They talk of the need to measure the business value of a BI project and to coordinate changes in information flow, workflow, and decision structure so as to maximize that value. Hubbard's book offers ways that business value, cultural change, and process impact can be measured, and therefore managed. The Williams' book talks of the need for Decision Engineering. Hubbard's book gives one the understanding of what a Decision Engineering group would do on a routine basis. The two books together would be of high benefit to any manager trying to develop a "value management" culture. The Profit Impact of Business Intelligence The field of Human Capital Management and Workforce Analytics is receiving a lot of attention today. Two books that are particularly helpful in understanding how analytics can help Human Resource managers better support (and in fact drive) organizational performance are "The HR Scorecard" (by Brian E. Becker, Mark A. Huselid, and Dave Ulrich) and "The Workforce Scorecard" (by Mark A. Huselid, Brian E. Becker, and Richard W. Beatty). These books provide a comprehensive list of elements that could be included in a workforce analytics program from recruiting and retention to compensation and talent development. Hubbard's book helps the HR executive identify the critical metrics, what

Taming uncertainty, risk, and those pesky soft benefits.

Excellent, practical treatment of a critical issue in managing technology investments--namely, how can we reliably assess IT project ROI? This question lies at the heart of virtually every strategic IT decision. Yet, answering it is often viewed as being either too difficult to pull off or simply not worth the effort. As a result, organizations are left with project portfolios that are underperforming and wasting resources that could otherwise be applied to lower risk and higher return efforts. Doug's straightforward treatment of this important topic directly addresses what our clients view as their biggest obstacles to achieving better returns on IT investments: * Intangibles, esp. potential benefit streams * Lack of proper instrumentation and measurement infrastructure * Difficulty in applying the proper financial and statistical concepts The examples, spreadsheet templates, and download links provide almost any IT organization with the basic tools and know-how to quickly assess what to measure, how to measure, and then how to turn that information into a powerful decision-making tool for your IT organization. The material in this book offers a compelling blueprint for IT governance. This book is accessible to everyone.