{"product_id":"how-to-measure-anything-in-project-management-isbn-9781394239818","title":"How to Measure Anything in Project Management","description":"\u003cp\u003e\u003cb\u003eUncover common project management myths to improve project success\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHow to Measure Anything in Project Management\u003c\/i\u003e explains why popular methods for measurement in project management are flawed and describes how to conduct measurements that better inform decisions, reduce project risks, and improve the chance of project success. The authors argue that anything that matters to project management at all is measurable and that these measurements address many of the problems in project management. The authors leverage an exclusive survey on the state-of-the-art of measuring projects, new case studies of things that are seemingly hard to measure and a database, collected by Oxford Global Projects, of thousands of projects in software development, construction, energy, and many other fields, including some of the biggest projects in history. The book is accompanied by a set of useful spreadsheet-based \"power tools\" that support the more technical aspects of quantifying project risk, forecasting outcomes, and conducting seemingly difficult measurements. In this book, readers will learn:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWhy many of the methods they have been taught to use are little more than a type of “analysis placebo”\u003c\/li\u003e \u003cli\u003eWhy many popular methods lead to extreme overconfidence in estimates\u003c\/li\u003e \u003cli\u003eHow some of the most important measurements a project could conduct are currently rarely used\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eHow to Measure Anything in Project Management\u003c\/i\u003e earns a well-deserved spot on the bookshelves of managers, executives, auditors, controllers, and consultants seeking to improve project performance through superior measurement methodology.\u003c\/p\u003e \u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxi\u003c\/p\u003e \u003cp\u003eAbout the Authors xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 A World-scale Risk and a World-scale Opportunity 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Size of Projects 2\u003c\/p\u003e \u003cp\u003eThe Size of Project Problems 4\u003c\/p\u003e \u003cp\u003eEfforts to Fix Projects: The Emergence of Project Management 5\u003c\/p\u003e \u003cp\u003eA Path Forward: The Meta Project 8\u003c\/p\u003e \u003cp\u003eNotes 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 A Measurement Primer for Project Management 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Concept of Measurement 14\u003c\/p\u003e \u003cp\u003eA Definition of Measurement 15\u003c\/p\u003e \u003cp\u003eMeasurement and Probabilities for Practical Decision-making 16\u003c\/p\u003e \u003cp\u003eAre Scales Really Measurements? 18\u003c\/p\u003e \u003cp\u003eThe Object of Measurement 21\u003c\/p\u003e \u003cp\u003eWhat Do You See When You See More of It? 21\u003c\/p\u003e \u003cp\u003eWhy Do You Care? 23\u003c\/p\u003e \u003cp\u003eThe Methods of Measurement 25\u003c\/p\u003e \u003cp\u003eStatistical Significance: What’s the Significance? 26\u003c\/p\u003e \u003cp\u003eSmall Samples Tell You More Than You Think 28\u003c\/p\u003e \u003cp\u003eOther Sources of Measurement Aversion 30\u003c\/p\u003e \u003cp\u003eThe Cost Objection 30\u003c\/p\u003e \u003cp\u003eMeasurements Change What Is Being Measured 31\u003c\/p\u003e \u003cp\u003eStatistics Can Prove Anything 32\u003c\/p\u003e \u003cp\u003eEthical Objections to Measurement 33\u003c\/p\u003e \u003cp\u003eNotes 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 How We Know What Works 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSkepticism for Project Managers 36\u003c\/p\u003e \u003cp\u003eThe Analysis Placebo 36\u003c\/p\u003e \u003cp\u003eThe Problem of Feedback and Learning 38\u003c\/p\u003e \u003cp\u003eHow to Test Methods 40\u003c\/p\u003e \u003cp\u003eControlled Experiments and Component Testing 40\u003c\/p\u003e \u003cp\u003eEvaluating Sources 41\u003c\/p\u003e \u003cp\u003eThe Performance of Quantitative Methods 43\u003c\/p\u003e \u003cp\u003eExperts Versus Algorithms 43\u003c\/p\u003e \u003cp\u003eThe Exsupero Ursus Fallacy: Algorithm Aversion 44\u003c\/p\u003e \u003cp\u003ePotential Reasons for Exsupero Ursus 45\u003c\/p\u003e \u003cp\u003eImproving the Human Expert 47\u003c\/p\u003e \u003cp\u003eCalibrating the Expert 48\u003c\/p\u003e \u003cp\u003eThe Expert Consistency Component 49\u003c\/p\u003e \u003cp\u003eCollaboration on Estimates 50\u003c\/p\u003e \u003cp\u003eThe Decomposition Component 52\u003c\/p\u003e \u003cp\u003eA Summary of Research on Other Project Planning and Management Methods 54\u003c\/p\u003e \u003cp\u003eReference Class Forecasting 54\u003c\/p\u003e \u003cp\u003eVarious Project Management Methods 55\u003c\/p\u003e \u003cp\u003eThe Performance of Monte Carlo Simulations 58\u003c\/p\u003e \u003cp\u003eNotes 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The Project Decision Model: The Reason for Measurements 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTwo Types of Project Measurements 64\u003c\/p\u003e \u003cp\u003eProto-purpose Discovery Measurements 64\u003c\/p\u003e \u003cp\u003eDecision-driven Measurements 66\u003c\/p\u003e \u003cp\u003eUnproductive Incentives vs. Measurements 69\u003c\/p\u003e \u003cp\u003eDecisions Before: Thinking Slow 70\u003c\/p\u003e \u003cp\u003eExploration vs. Exploitation 71\u003c\/p\u003e \u003cp\u003eTracking the Outside World 73\u003c\/p\u003e \u003cp\u003eChoosing How to Run the Project 74\u003c\/p\u003e \u003cp\u003eHow Models Indicate What to Measure 77\u003c\/p\u003e \u003cp\u003eThe Expected Value of Information: A Simple Introduction 77\u003c\/p\u003e \u003cp\u003eThe Measurement Inversion: Measuring the Wrong Things 79\u003c\/p\u003e \u003cp\u003eThe Value of Imperfect Measurements 80\u003c\/p\u003e \u003cp\u003eAn Aspirational Model 82\u003c\/p\u003e \u003cp\u003eThe Rise of Digital Twins 83\u003c\/p\u003e \u003cp\u003eDigital Twins in Project Management 84\u003c\/p\u003e \u003cp\u003eA Practical Path Forward 87\u003c\/p\u003e \u003cp\u003eNotes 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Project Uncertainty and Risk: A Primer 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Concepts and Definitions 92\u003c\/p\u003e \u003cp\u003eUncertainty as a Probability Distribution 93\u003c\/p\u003e \u003cp\u003eRisk: A Special Case of Uncertainty 96\u003c\/p\u003e \u003cp\u003eThe Problem with Current Methods 98\u003c\/p\u003e \u003cp\u003eWhy Risk “Scores” Don’t Work 99\u003c\/p\u003e \u003cp\u003eHow the Risk Matrix Makes Scores Worse 101\u003c\/p\u003e \u003cp\u003eA Quantitative Risk Model: Starting Very Simple 105\u003c\/p\u003e \u003cp\u003eThe One-for-One Substitution 106\u003c\/p\u003e \u003cp\u003eMonte Carlo Mechanics: A Brief Introduction 108\u003c\/p\u003e \u003cp\u003eSupporting Decisions 111\u003c\/p\u003e \u003cp\u003eA Return on Mitigation 112\u003c\/p\u003e \u003cp\u003eHow Much Risk Do You Tolerate? 113\u003c\/p\u003e \u003cp\u003eRisk Versus Return: The Powerful Theory of Utility 115\u003c\/p\u003e \u003cp\u003eSimple Tools for Measuring Uncertainty and Risk 117\u003c\/p\u003e \u003cp\u003eA First Estimate of a Discrete Probability 118\u003c\/p\u003e \u003cp\u003eA First Estimate of a Continuous Probability 119\u003c\/p\u003e \u003cp\u003eFinal Clarifications 120\u003c\/p\u003e \u003cp\u003eCase Examples for What Probability Means 121\u003c\/p\u003e \u003cp\u003eUncertainty Versus Risk Versus Opportunity 123\u003c\/p\u003e \u003cp\u003eEpistemic Versus Aleatory Uncertainty 124\u003c\/p\u003e \u003cp\u003eEven More Ordinal Scales 125\u003c\/p\u003e \u003cp\u003eRisk as Governance or Compliance 125\u003c\/p\u003e \u003cp\u003eThe Problem of “Black Swans” 126\u003c\/p\u003e \u003cp\u003eSome Items That Aren’t Really Risks 127\u003c\/p\u003e \u003cp\u003eMore Improvements to Come 128\u003c\/p\u003e \u003cp\u003eNotes 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Calibrated Subjective Probability Estimates 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Subjective Probability 132\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCalibration Exercise 135\u003c\/p\u003e \u003cp\u003eThe Calibration Exercises 136\u003c\/p\u003e \u003cp\u003eEvaluating Performance and Typical Results 137\u003c\/p\u003e \u003cp\u003eImproving Calibration 140\u003c\/p\u003e \u003cp\u003eThe Equivalent Bet 141\u003c\/p\u003e \u003cp\u003eMore Techniques 142\u003c\/p\u003e \u003cp\u003eMore Advanced Calibration Topics to Come 144\u003c\/p\u003e \u003cp\u003eThe Effects of Calibration 146\u003c\/p\u003e \u003cp\u003eConceptual Obstacles to Calibration 149\u003c\/p\u003e \u003cp\u003eConflating Uncertainty with Knowing Nothing 149\u003c\/p\u003e \u003cp\u003eHypotheses That Contradict the Data 152\u003c\/p\u003e \u003cp\u003eObjections Based on the Philosophical Debate in Statistics 153\u003c\/p\u003e \u003cp\u003eNotes 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Cost and Schedule Measurements 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Big Plan Versus Iteration: Meta-measurements of Common Estimation Methods 158\u003c\/p\u003e \u003cp\u003eTop-down Estimations: Reference Class Forecasting 162\u003c\/p\u003e \u003cp\u003eBottom-up Forecasting with Monte Carlo 165\u003c\/p\u003e \u003cp\u003eA Deterministic View of Tasks 165\u003c\/p\u003e \u003cp\u003eProbability Distributions for Project Tasks 167\u003c\/p\u003e \u003cp\u003eCorrelations 168\u003c\/p\u003e \u003cp\u003eMultiple Prerequisites and Stochastic Critical Paths 170\u003c\/p\u003e \u003cp\u003eParade of Trades 171\u003c\/p\u003e \u003cp\u003eComparing Top Down and Bottom Up: Case Examples 174\u003c\/p\u003e \u003cp\u003eThe Swedish Nuclear Waste Program 175\u003c\/p\u003e \u003cp\u003eHigh-speed Rail 176\u003c\/p\u003e \u003cp\u003eHow to Improve the Models 181\u003c\/p\u003e \u003cp\u003eThe Granularity of the Monte Carlo Model 182\u003c\/p\u003e \u003cp\u003eDistributions and Biases 182\u003c\/p\u003e \u003cp\u003eCorrelations 183\u003c\/p\u003e \u003cp\u003eImproving the RCF with Monte Carlo 184\u003c\/p\u003e \u003cp\u003eNotes 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Betting on Benefits 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeta-measurements of Benefits 189\u003c\/p\u003e \u003cp\u003eHow Much Should Benefits Be to Justify a Project? 190\u003c\/p\u003e \u003cp\u003eWhy This May Be Optimistic 192\u003c\/p\u003e \u003cp\u003eWhy Measuring Benefits Is Rare 195\u003c\/p\u003e \u003cp\u003eFermi Decompositions for Benefits 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Fermi 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Example Decompositions 199\u003c\/p\u003e \u003cp\u003eMonetizing Benefits 201\u003c\/p\u003e \u003cp\u003eForecasts of Monetary Impacts 201\u003c\/p\u003e \u003cp\u003ePreferences 202\u003c\/p\u003e \u003cp\u003eQuantifying Preferences 203\u003c\/p\u003e \u003cp\u003eThe Use of Scores and Multiple Objectives 205\u003c\/p\u003e \u003cp\u003eAn Example of Challenging Benefit Measurement: Biodiversity 206\u003c\/p\u003e \u003cp\u003eMeasuring What Matters in Projects 206\u003c\/p\u003e \u003cp\u003eA (Slightly) More Realistic Information Value Calculation 207\u003c\/p\u003e \u003cp\u003eThe High Information Values for Projects 209\u003c\/p\u003e \u003cp\u003eGetting Started on Measuring What Matters 211\u003c\/p\u003e \u003cp\u003eConsidering Risk and Return 213\u003c\/p\u003e \u003cp\u003eA Risk Neutral Decision-maker for Projects 214\u003c\/p\u003e \u003cp\u003eAdding Utility Theory to Projects 215\u003c\/p\u003e \u003cp\u003eSome Alternatives within Utility Math 217\u003c\/p\u003e \u003cp\u003eAre Executives Too Risk Averse for Projects? 219\u003c\/p\u003e \u003cp\u003eA Framework and Its Consequences 221\u003c\/p\u003e \u003cp\u003eFindings from Quantitative Analysis of Past Projects 223\u003c\/p\u003e \u003cp\u003eHow and When, Not Just Whether 223\u003c\/p\u003e \u003cp\u003eBenefits Are Not Just for Project Approval Decisions 224\u003c\/p\u003e \u003cp\u003eNotes 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Measuring Progress 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Progress Problem 227\u003c\/p\u003e \u003cp\u003eSimple Progress, Simple Interventions 228\u003c\/p\u003e \u003cp\u003eEarned Value Management 229\u003c\/p\u003e \u003cp\u003eEVM Basics 230\u003c\/p\u003e \u003cp\u003eThe XRL Example 231\u003c\/p\u003e \u003cp\u003eRecovery vs. Performance 233\u003c\/p\u003e \u003cp\u003eForecasting with EVM 235\u003c\/p\u003e \u003cp\u003eProgress in Information Projects 237\u003c\/p\u003e \u003cp\u003eWaterfall 237\u003c\/p\u003e \u003cp\u003eAgile and Measurement in Other Software Development Methods 237\u003c\/p\u003e \u003cp\u003eSummarizing Software Metric Difficulties 239\u003c\/p\u003e \u003cp\u003eFour Stories and Lessons 240\u003c\/p\u003e \u003cp\u003eInterfaces in a Global Bank Transformation 240\u003c\/p\u003e \u003cp\u003eAn Energy Project Front End 241\u003c\/p\u003e \u003cp\u003eConstruction Constraints 243\u003c\/p\u003e \u003cp\u003eTesting as Software Checkpoints 245\u003c\/p\u003e \u003cp\u003eLessons 246\u003c\/p\u003e \u003cp\u003eThe Remaining Project Simulation 247\u003c\/p\u003e \u003cp\u003eConditional Reference Class Forecasting (CRCF) 247\u003c\/p\u003e \u003cp\u003eThe Bottom-up Simulation for the Remaining Project 251\u003c\/p\u003e \u003cp\u003eFurther Considerations for the RPA 252\u003c\/p\u003e \u003cp\u003eNotes 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 More Measurement Methods Made Easy 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntuition for the Habitually Scientific 258\u003c\/p\u003e \u003cp\u003eA Jelly Bean Example 258\u003c\/p\u003e \u003cp\u003eA Little Probability Theory 260\u003c\/p\u003e \u003cp\u003eConsequences of Probability Theory 262\u003c\/p\u003e \u003cp\u003eMyths Exposed by Probability Theory 262\u003c\/p\u003e \u003cp\u003eSignificant Points About Statistical Significance 265\u003c\/p\u003e \u003cp\u003eBasic Sampling Methods 266\u003c\/p\u003e \u003cp\u003eThe “Mathless” Table for Medians 269\u003c\/p\u003e \u003cp\u003eEstimating a Population Proportion 270\u003c\/p\u003e \u003cp\u003eProject Cancellation Rates as a Function of Duration 274\u003c\/p\u003e \u003cp\u003eMeasuring Population Size 274\u003c\/p\u003e \u003cp\u003eMeasuring Some Things by Knowing Other Things 276\u003c\/p\u003e \u003cp\u003eControlled Experiments 277\u003c\/p\u003e \u003cp\u003eRegression 277\u003c\/p\u003e \u003cp\u003eMore Advanced Methods of Regression and Classification 283\u003c\/p\u003e \u003cp\u003eEstimating the Whole Distribution 285\u003c\/p\u003e \u003cp\u003eSummarizing Methods 289\u003c\/p\u003e \u003cp\u003eBrainstorming a Measurement Approach 289\u003c\/p\u003e \u003cp\u003eData Gathering Methods 291\u003c\/p\u003e \u003cp\u003eA Review of Methods in This Chapter 292\u003c\/p\u003e \u003cp\u003eNotes on Surveys 293\u003c\/p\u003e \u003cp\u003eNotes 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 The Meta-project: Implementing Better Project Measurements 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStart with the End in Mind: The Continuous Improvement Process 299\u003c\/p\u003e \u003cp\u003eMeasure What Matters 299\u003c\/p\u003e \u003cp\u003e(Real) Skepticism and Meta-measurements 301\u003c\/p\u003e \u003cp\u003eMeasuring and Forecasting the Outside World 302\u003c\/p\u003e \u003cp\u003eAI: The Most Important Project Ecosystem Measurement? 304\u003c\/p\u003e \u003cp\u003eMore Thinking, Fewer Projects, Bigger Wins 307\u003c\/p\u003e \u003cp\u003eStart Your Meta-project 307\u003c\/p\u003e \u003cp\u003eExamples of Meta-projects Deliverables: Continuous Improvement 308\u003c\/p\u003e \u003cp\u003eDevelop an Initial Team 309\u003c\/p\u003e \u003cp\u003eAssess the Current State of the Project Portfolio 310\u003c\/p\u003e \u003cp\u003eConsiderations for the Meta-project Plan 312\u003c\/p\u003e \u003cp\u003eThe Pilot Project 312\u003c\/p\u003e \u003cp\u003eScaling to the Final Deliverable 314\u003c\/p\u003e \u003cp\u003eOrganizational Challenges 315\u003c\/p\u003e \u003cp\u003eResistance to Change 315\u003c\/p\u003e \u003cp\u003eAddressing Organizational Objections to Measurement 316\u003c\/p\u003e \u003cp\u003eThe Politics of Measurement 318\u003c\/p\u003e \u003cp\u003eNotes 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 A Call to Action for the Industry 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCall for Action for Project Software Vendors 321\u003c\/p\u003e \u003cp\u003ePut Decisions at the Center 322\u003c\/p\u003e \u003cp\u003eDeal in Uncertainties 324\u003c\/p\u003e \u003cp\u003eBuild the User-buyer-builder Federation 325\u003c\/p\u003e \u003cp\u003eBe the Vendor That Measures Its Performance 325\u003c\/p\u003e \u003cp\u003eBe Forward-looking 326\u003c\/p\u003e \u003cp\u003eCall for Action for the Standard-setting Bodies 327\u003c\/p\u003e \u003cp\u003eCall to Action for Consultants, Researchers, and Advisory Firms 329\u003c\/p\u003e \u003cp\u003eBig Future Projects 331\u003c\/p\u003e \u003cp\u003eA Mars Mission 331\u003c\/p\u003e \u003cp\u003eStopping Hurricanes 332\u003c\/p\u003e \u003cp\u003eThe Meta-Project 333\u003c\/p\u003e \u003cp\u003eNotes 333\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix 1 Analysis of Survey Responses on Project Management Practices 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction and data overview 335\u003c\/p\u003e \u003cp\u003eSuccess Metrics: Cost and Schedule Overrun Ratios 337\u003c\/p\u003e \u003cp\u003eOverview of Project Management Practices Reported in the Survey 339\u003c\/p\u003e \u003cp\u003eProject Management Methodologies 339\u003c\/p\u003e \u003cp\u003eCost and Schedule Estimation Methods 339\u003c\/p\u003e \u003cp\u003eUncertainty and Risk Assessment Tools 340\u003c\/p\u003e \u003cp\u003eCertifications 341\u003c\/p\u003e \u003cp\u003eResults 341\u003c\/p\u003e \u003cp\u003eProject Management Methodologies 341\u003c\/p\u003e \u003cp\u003eCost and Schedule Estimation Methods 343\u003c\/p\u003e \u003cp\u003eUncertainty and Risk Assessment Tools 343\u003c\/p\u003e \u003cp\u003eCertifications 343\u003c\/p\u003e \u003cp\u003eInterpreting the (Mostly) Statistically Insignificant Results 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix 2 Reference Class Data on Project Cost, Schedule, and Benefit Overruns 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRelevance of the Data and Reference Class Forecasting 346\u003c\/p\u003e \u003cp\u003eUsing Historical Data to Improve Estimates – An Example 347\u003c\/p\u003e \u003cp\u003eNotes 351\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix 3 Selected Distributions 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUniform 354\u003c\/p\u003e \u003cp\u003eBeta 355\u003c\/p\u003e \u003cp\u003eBeta PERT 356\u003c\/p\u003e \u003cp\u003eTriangular 357\u003c\/p\u003e \u003cp\u003eBinary 358\u003c\/p\u003e \u003cp\u003eNormal 359\u003c\/p\u003e \u003cp\u003eLognormal 360\u003c\/p\u003e \u003cp\u003ePower Law 361\u003c\/p\u003e \u003cp\u003eTruncated Power Law 362\u003c\/p\u003e \u003cp\u003eQuantile-parameterized 363\u003c\/p\u003e \u003cp\u003eGamma Poisson 365\u003c\/p\u003e \u003cp\u003eStochastic Information Packet 366\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix 4 Chapter 6 Calibration Question Answers 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnswers to Confidence Interval Questions 369\u003c\/p\u003e \u003cp\u003eAnswers to True\/False Questions 371\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix 5 Measuring Biodiversity 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Benefits of Biodiversity 373\u003c\/p\u003e \u003cp\u003eMeasuring Biodiversity 375\u003c\/p\u003e \u003cp\u003eNotes 376\u003cbr\u003e\u003cbr\u003e Index 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDOUGLAS W. HUBBARD\u003c\/b\u003e has 35 years’ experience as a management consultant with a focus on the application of quantitative methods in decision making. He is the founder and president of Hubbard Decision Research and the creator of the “Applied Information Economics” method. He is also the author of the original \u003ci\u003eHow to Measure Anything: Finding the Value of Intangibles in Business\u003c\/i\u003e as well as other books in measurement, risk analysis and decision making.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eALEXANDER BUDZIER, PHD,\u003c\/b\u003e is a Fellow at the University of Oxford’s Saïd Business School. He specializes in IT, infrastructure, energy, mega-events and change.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eANDREAS BANG LEED\u003c\/b\u003e is the Head of Data Science at Oxford Global Projects. He specializes in data-driven project planning and risk analysis for some of the world’s most ambitious mega-projects.\u003c\/p\u003e  \u003cp\u003eDecades of data show that, despite the development of many new tools and methods, projects still too often fail to meet objectives. Leading a successful project requires more than haphazard and idiosyncratic monitoring and measurement of KPIs and OKRs. You’ll need to know what metrics require your attention and how to accurately track them. But many popular project management measuring techniques not only fail to keep project leaders informed, but also contribute to overconfidence in your odds of success. \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eHow to Measure Anything in Project Management,\u003c\/i\u003e a team of veteran management experts delivers an incisive and practical new approach to decision making and risk management in project management. The authors convincingly argue for the proposition that everything that matters to project management is measurable, and that these measurements address many of the perennial problems in project management. \u003c\/p\u003e\u003cp\u003eThe book leverages a broad and robust suite of research and practical examples demonstrating that—among other things—many of the measurement methods currently relied on by project managers amount to little more than a type of “analysis placebo.” It also offers a collection of hands-on, spreadsheet-based “power tools” that support the more technical aspects of quantifying project risk, forecasting outcomes, and conducting complex measurements. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eHow to Measure Anything in Project Management\u003c\/i\u003e relies on the expansive Oxford Global Projects Database, containing thousands of studied projects conducted in a wide variety of disciplines, to bring you the most important measurements you should be making on every project you lead. \u003c\/p\u003e\u003cp\u003ePerfect for executives, managers, entrepreneurs, founders, and other business leaders, this book will also prove invaluable to project managers, auditors, controllers, statisticians, consultants, and other practicing professionals interested in improving their decision making and risk management in the context of project management.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for HOW TO MEASURE ANYTHING IN PROJECT MANAGEMENT\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e“Truly successful projects are not those that just meet predetermined metrics or outputs. Project professionals need to pursue the value critical stakeholders believe they’ve received from their efforts. \u003ci\u003eHow to Measure Anything in Project Management\u003c\/i\u003e reiterates this critical nuance and provides a roadmap for those seeking to fuse data with perceptions. It reshapes how organizations make decisions, deliver end-to-end value, and build lasting resilience.”\u003cbr\u003e \u003cb\u003e—PIERRE LE MANH,\u003c\/b\u003e President and CEO, Project Management Institute (PMI) \u003c\/p\u003e\u003cp\u003e“A bold and timely book that redefines how we think about project success. By proving that anything that matters can be measured, the authors equip project professionals with tools to make smarter, evidence-informed decisions. This is a must-read for anyone serious about creating a world in which all projects succeed.”\u003cbr\u003e \u003cb\u003e—PROFESSOR ADAM BODDISON OBE,\u003c\/b\u003e CEO, Association for Project  Management (APM) \u003c\/p\u003e\u003cp\u003e“As someone who has dedicated a career to advancing project management, I believe this book is one of the most important contributions to the field in recent years. And it comes at the right time, as AI, data science, and systems thinking converge to reshape how decisions are made.”\u003cbr\u003e \u003cb\u003e—RICARDO VIANA VARGAS,\u003c\/b\u003e PMI Fellow and former Chair of the Board, Project  Management Institute (PMI) \u003c\/p\u003e\u003cp\u003e“To control projects, we need to measure what is important and not only what is easy to measure. This book lives up to its title. It provides practical guidance and useful tools to measure what matters in projects.”\u003cbr\u003e \u003cb\u003e—TOMAS CARLSSON,\u003c\/b\u003e President and CEO, NCC\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989381398757,"sku":"NP9781394239818","price":60.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394239818.jpg?v=1761783890","url":"https:\/\/k12savings.com\/products\/how-to-measure-anything-in-project-management-isbn-9781394239818","provider":"K12savings","version":"1.0","type":"link"}