Name: The Decision Intelligence Handbook (L. Y. PrattN. E. Malcolm) (z-library.sk, 1lib.sk, z-lib.sk)
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L. Y. Pratt & N. E. Malcolm Foreword by John Elkington The Decision Intelligence Handbook Practical Steps for Evidence-Based Decisions in a Complex World

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AI “This book builds on Dr. Pratt’s first book, Link, stepping organizations through the collaborative—and very human—process of ‘designing decisions.’ I recommend it to anyone involved in business decision making at any level, as a means of elevating the process and delivering real, measurable, and strategic value.” —Howard Dresner Founder and Chief Research Officer at Dresner Advisory Services, author, and “father” of business intelligence The Decision Intelligence Handbook Twitter: @oreillymedia linkedin.com/company/oreilly-media youtube.com/oreillymedia Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data. With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. This handbook addresses three widespread data-driven decision-making problems: • How can decision makers use data and technology to ensure desired outcomes? • How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? • How can organizational decision makers assess and improve their decisions over time? Dr. L. Y. Pratt is the inventor of AI transfer learning. With 35 years of applied AI experience, Pratt also boasts a global reputation as a technology leader and creator of decision intelligence. As Dr. Pratt’s colleague for the last five years, N. E. Malcolm has applied 35+ years of enterprise software expertise to co-developing best practices for delivering decision intelligence projects. US $65.99 CAN $82.99 ISBN: 978-1-098-13965-0

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Praise for The Decision Intelligence Handbook This book builds on Dr. Pratt’s first book, Link, stepping organizations through the collaborative—and very human—process of “designing decisions.” I recommend it to anyone involved in business decision making at any level, as a means of elevating the process and delivering real, measurable, and strategic value. —Howard Dresner, founder and Chief Research Officer at Dresner Advisory Services; author of The Performance Management Revolution and Profiles in Performance; and “father” of business intelligence Decision intelligence is an incredibly important concept. It is critical for decision makers to understand the implications of the decisions they make. Causal diagramming and the other methods outlined in this book are powerful techniques that should be introduced into every company interested in improving its ability to react effectively to change. —Allan Frank, president at Think New Visions, LLC; former CTO for the city of Philadelphia and CapGemini I am thrilled to pioneer a new approach to decision making in the complex world of healthcare workforce management. Our innovative use of decision intelligence foundation models is helping hospitals address the ongoing staffing crisis and provide better patient care by capturing the best practices to make and monitor talent acquisition, enablement, and retention decisions. This book is an invaluable resource for anyone looking to make better decisions to improve healthcare operations. —Lorenzo Martinelli, cofounder and chairperson of C-Plan.IT Artificial intelligence today is exploding, driven by large language models (LLMs) like ChatGPT. I think of decision intelligence (DI) as the “killer app” for LLMs because DI connects this advanced technology to business outcomes with an unprecedented amount of knowledge and power. This book tells you how to harness that power. —Teasha Cable, decision intelligence entrepreneur; cofounder and CEO of CModel.io

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L. Y. Pratt and N. E. Malcolm Foreword by John Elkington The Decision Intelligence Handbook Practical Steps for Evidence-Based Decisions in a Complex World Boston Farnham Sebastopol TokyoBeijing

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978-1-098-13965-0 [LSI] The Decision Intelligence Handbook by L. Y. Pratt and N. E. Malcolm Copyright © 2023 Quantellia, L.L.C. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisition Editor: Michelle Smith Development Editor: Sarah Grey Production Editor: Clare Laylock Copyeditor: nSight, Inc. Proofreader: Piper Editorial Consulting, LLC Indexer: Sue Klefstad Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea June 2023: First Edition Revision History for the First Edition 2023-06-21: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098139650 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. The Decision Intelligence Handbook, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Do You Need Decision Intelligence? 2 What DI Does for You and Your Organization 3 From Data to Decisions 3 The Decision Complexity Ceiling 5 What Is DI? 8 Build Your First CDD, Right Now! 9 DI Is About Action-to-Outcome Decisions 10 DI Is About Human-in-the-Loop Decisions 12 Why Data-Driven Decision Makers Need DI 14 Where DI Comes From 16 What DI Is Not 18 The DI Maturity Model 20 The Shifting Meaning of “Decision Intelligence” 22 Who Is Doing DI Today? 22 The Nine DI Processes 22 Conclusion 25 2. Decision Requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Decision Requirements (Phase A): Overview 29 Process A1: The Decision Objective Statement 31 Decision Objective Statement: Net-Zero Emissions Program Use Case 32 Interlude: Convening the Decision Team 33 Making Decisions at Multiple Organizational Levels 34 v

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Balancing Information, Authority, and Responsibility 35 Process A2: Decision Framing 36 Decision Verification 36 Why Decision Framing Is Important 37 Formal Process Description: Process A2, Decision Framing 38 When the Decision Frame Is Wrong 40 Verifying and Framing: Net-Zero Emissions Program Use Case 41 Try It Yourself: Decision Requirements for a Telecom Use Case 44 Framing the Decision 46 Asking About Outcomes and Goals 47 Completing the Decision Framing Worksheet 48 Conclusion 49 3. Decision Modeling: The Decision Design Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 The Decision Modeling Phase: Overview 53 Decision Elements 55 Decisions and Time 62 Process B1: Decision Design 63 Formal Process Description: Process B1: Decision Design 64 Divergent and Convergent Thinking 68 The CDD Elicitation Meeting 69 Divergent Thinking: Eliciting Outcomes and Objectives 70 Capturing Out-of-Context Decision Elements in a “Parking Lot” 71 Convergent Thinking: Refining (“Cleaning Up”) Outcomes and Objectives 71 Divergent Thinking: Eliciting Levers 74 Convergent Thinking: Refining Levers 74 Divergent Thinking: Eliciting Intermediates (Causal Chains) 76 Convergent Thinking: Refining Intermediates 78 Divergent Thinking: Eliciting Externals 79 Convergent Thinking: Refining Externals 80 Convergent Thinking: Refining Decision Elements 80 Refining and Expanding Your CDD 81 Levels of Modeling Detail 82 How Do You Know When to Stop Decision Modeling? 83 Decision Design Use Case: Governmental Net-Zero Project 85 Try It Yourself: Decision Design for a Telecom Use Case 89 Brainstorming Outcomes 90 Brainstorming Levers 91 Wiring Known Dependencies Together 92 Initial Refinement 94 Iterating On and Publishing the CDD 102 Conclusion 103 vi | Table of Contents

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4. Decision Modeling: The Decision Asset Investigation Process. . . . . . . . . . . . . . . . . . . . . 105 Deciding to Go Digital 107 Introduction to Process B2: Decision Asset Investigation 107 From Simple to Sophisticated Assets 108 Documenting Decision Assets 109 Formal Process Description: Process B2: Decision Asset Investigation 111 Decision Asset Investigation: A Sweet Potato CDD Drives Data Gathering and Research 113 Data for Externals 115 The Puzzle Toy Use Case: The Decision Asset Conversation 117 Try It Yourself: Decision Asset Investigation for the Telecom Use Case 122 Conclusion 126 5. Decision Reasoning: The Decision Simulation Process. . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Decision Reasoning: Phase Overview 128 Decision Simulation Process Overview 129 Why Simulate Your CDD? 131 Deciding Whether to Automate Your Decision Simulation 134 Developing the Simulation Iteratively 136 Formal Process Description: Decision Simulation 137 Creating a Decision Simulation 139 Simplifying the CDD 139 Lever Choices 140 Externals and Assumptions 140 Intermediates and Outcomes 141 Decision Simulation Fidelity 142 Running the Simulation “Backward”: Decision Models for Optimization 143 Technology Options 144 Simulation Report: Identifying Patterns and Feedback Loops 145 Net-Zero Emissions Use Case: Simulating the Decision Model 149 Visualizing Emergent Patterns in the Puzzle Toy Use Case Simulation 151 Try It Yourself: Decision Simulation for the Telecom Use Case 155 Deciding on a CDD to Simulate 156 Designing CDD Visual Elements 156 Coding the Dependencies 158 Testing Your Simulation 160 Conclusion 162 6. Decision Reasoning: The Decision Assessment Process. . . . . . . . . . . . . . . . . . . . . . . . . . 163 Introduction to Process C2, Decision Assessment 166 Formal Process Description: Process C2, Decision Assessment 167 Decision Assessment Lenses 170 Table of Contents | vii

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Baseline 171 Accuracy of the Model, and of Its Parts 172 Bias 173 Sensitivity 174 Uncertainty and Risk 176 Provenance 178 Fidelity 179 Consensus 179 Timing 180 Try It Yourself: Decision Assessment for the Telecom Use Case 180 Conclusion 185 7. Decision Action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Decision Action Phase Overview 188 Process D1: Decision Monitoring 189 Formal Process Description: Process D1, Decision Monitoring 190 When Is a Monitored Value Wrong Enough to Matter? 192 What to Monitor 193 When Do You Need to Revisit Your Decision? 194 Decision Monitoring Records 195 Silos, Whack-a-Mole, and the Measurement Effect 195 Case Study: Network Upgrade: When the Decision Is Already Off the Rails 197 Try It Yourself: Decision Monitoring for the Unlimited Usage Plan Use Case 200 Conclusion 203 8. Decision Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 The Decision Review Phase: Overview 206 Process E1: Decision Artifacts Retention 207 Legal, Regulatory, and Policy Issues 209 When Should Decision Artifacts Retention Begin and End? 210 Formal Process Description: Process E1, Decision Artifacts Retention 210 Try It Yourself: Decision Artifacts Retention for the Telecom Use Case 212 Process E2: Decision Retrospective 214 One-Time Versus Repeated Decisions 214 Measuring Decision Quality 215 Formal Process Description: Process E2, Decision Retrospective 217 Continuous Decision-Process Improvement 220 Conclusion 222 Appendix: Framework for How Data Informs Decisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 viii | Table of Contents

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Foreword When this book’s authors first asked me to write this foreword, I answered with a soft “no.” Although I have often spoken of the need for system change in service of sustainabil‐ ity objectives, and am often told I am a system thinker, it doesn’t feel that way. If you recall Robert Pirsig’s long-ago book, Zen and the Art of Motorcycle Maintenance, he distinguished between “romantics”—people who love riding a Harley until it breaks, then kick it as they stalk off down the road—and “mechanics,” who hunker down and try to fix the machine. I warned Pratt and Malcolm that I am more of a romantic than a mechanic, at a time when we need more mechanics. Romantics, I believe, will always exist and will often be needed, but true systemic change—whether toward sustainability or simply to adapt to the complexities facing modern enterprises—increasingly needs mechanical talent. And that, bluntly, has never been me. Undeterred, the authors doubled down, explaining that I’d hit on exactly the point of decision intelligence and of this book. Indeed, if we are to effectively galvanize ourselves to meet the level of change and disruption now required, we will need the perspectives of both the mechanic and the creative, working together. We live in an age where two forces are colliding. First, as often happens during scientific and technological revolutions, increased specialization is creating barriers between disciplines: the exponential innovator struggles to communicate with the economist, the politician, the investor, or the executive. Each inhabits their own island of expertise, protected by bastions of jargon. But nature—and emerging eco‐ nomic realities—rarely respect such artificial separations. The second force comes from a new class of cross-disciplinary problems: climate dis‐ ruption drives migration and conflict; conflict affects the production and distribution of food; food scarcities drive poverty; poverty impacts the wider economy and dents ix

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tax yields, and so on, as these dynamics hamper our ability to tackle the climate crisis. Around we go, in complex causal chains, whorls, loops. None of these problems can be solved in isolation. And such dynamics cascade from the realm of sustainability down to everyday business and policy decisions, whether you’re a sweet potato grower or a government official creating a net-zero carbon emissions policy (both examples well covered in this book). Every decision maker now faces an ever more complex and turbulent world. Throughout my career, I’ve observed that creative minds like mine help bridge the gaps between our perceptions and emergent realities. We need people who can use lateral thinking to spot these connections, to understand increasingly nested systems, and to locate today’s crucial tipping points and tomorrow’s leverage points. I need no persuading that technologies—including earth observation, big data, and AI—are crucial in helping us to spot and solve new classes of risk and opportunity. I have visited organizations like NASA, the Woods Hole Oceanographic Institution, and DeepMind to get a sense of where we may be headed. But, full disclosure, much of the time I struggle to understand what these people are talking about. Their brains seem to work many times faster than mine–it’s like drinking from a fire hose. Nor am I alone here. Leaders worldwide are trying to become more “evidence-based” and “data-driven,” but many seem to operate more from the analytical right side of their brains than the creative left side. So, the focus needs to expand from high-profile leaders to their teams and organizational cultures. We need to be less obsessed with what individuals and corporations and brands are doing and more focused on how we can create market dynamics that ensure all market actors move in the right direction—with the necessary urgency. There is no way we can pull our economies back within our planet’s limits without the help of big data, expert systems, and AI, but their contribution will only bend the relevant curves if we can blend the minds and skills of romantics and mechanics, today and tomorrow. I found, in the following pages, that if you have the patience and are a nontechnical creative like me, you can learn how to work with technical people more effectively. This holds true whether you’re a business leader or you’re creating policy and shaping market incentives. I hope you’ll find that here, too. If, on the other hand, you’re a technical person who feels the frustration of communi‐ cating effectively with nontechnical (or less technical) people, you can bridge the gap from the other side. This book contains a step-by-step recipe for doing so. I conclude that it should be on the reading lists of all business schools and on the desks (not just shelves) of legislators and leaders worldwide. x | Foreword

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I suspect that few people will read this book cover-to-cover in a single sitting. But you will be well-served even if the book hovers on your desk or shelf and you read just a chapter a month. It is dense with ideas, but leavened with exercises that help us see our decisions through new lenses, in ways that can help us all to communicate better with colleagues. Finally, and perhaps most importantly, the authors show us all how to invite a new kind of “colleague” into our thinking, conversations, and work. Decades ago I got a well-known financial-world cartoonist to draw me a picture of a boardroom table, also now featuring a fish in a business suit (symbolizing the natural world), a woman from the Global South (symbolizing the ever-expanding social agenda) and a robot (symbolizing accelerating technological progress). Today, the robots are already here. It’s time to welcome them in and work out what we can do with them that we can’t do without. — John Elkington, cofounder of Environmental Data Services, SustainAbility, and Volans Ventures; aka the “Godfather of Sustainability”; and author of Green Swans: The Coming Boom in Regenerative Capitalism Foreword | xi

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Preface The past few decades have brought astonishing improvements in data science, business intelligence, and artificial intelligence (AI). In response, organizations are increasingly determined to weave these technologies into the fabric of their everyday decisions. Indeed, a 2019 survey conducted by global consulting firm McKinsey found that better decision making can benefit a typical Fortune 500 company by as much as $250 million per year. This reflects a big opportunity to improve organizational outcomes, even as it reflects the dismal state of organizational decision making today. But we’re far from achieving this nirvana. In Fortune magazine, Alan Murray and Jackson Fordyce write, “Business leaders are so overwhelmed with data they’re strug‐ gling to function.” And today, many “data-driven” and “evidence-based” initiatives are falling short. The reason is, simply, that decision making is not really about data: it’s about achieving an organization’s outcomes, with data as a key ingredient, but still secondary to business outcomes. This incorrect focus on the data itself leads to data and AI work that isn’t well aligned with many organizations’ outcomes and desired goals. Smart organizations are moving, instead, to “outcome-driven” decision making, with data and technology working “under the hood” to supercharge their choices. Along the way, a new discipline has emerged to help them, called decision intelligence (DI). DI brings AI (including generative AI technologies like ChatGPT), data, human expertise, research, and more into an integrated framework that answers two ques‐ tions: “If I take this action today, in this context, what will be the outcome?” and “What is the best action to take today to maximize the likelihood that I’ll reach my goals?” DI is about ensuring that decision makers can use the most powerful technologies, and that decision-making systems present information in a way that feels natural and intuitive. DI moves organizations beyond simply using historical data, which xiii

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1 L. Y. Pratt, Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World (Bingley, UK: Emerald Publishing, 2019). provides information and insights about the present and past, to answering questions about the future. Hi, we’re the authors of this book, N. E. Malcolm and L. Y. Pratt. Pratt co-invented DI (with Mark Zangari) in 2010. Since then, working with our team, we’ve helped DI to grow into a field so promising that Forbes asks whether it’s “the new AI.” The Gartner Group predicts that more than a third of large organizations will be using DI by the time this book is published; market-research firm MarketsandMarkets projects that DI will grow to a $22 billion market by 2027; and Chinese behemoth Alibaba names DI second on its list of top technology trends for 2023 (just after generative AI). One of us—coauthor Pratt—wrote the first book on DI: Link: How Decision Intelli‐ gence Connects Data, Actions, and Outcomes for a Better World.1 We’ve now built and delivered dozens of DI solutions for large and small commercial organizations, start‐ ups, and the public sector. DI projects—ours and those of other DI practitioners— have saved and generated many hundreds of millions of dollars for organizations worldwide, in addition to social and other nonfinancial benefits. About This Book This book is a practical, “roll up your sleeves” guide to how you can do DI, within your own organization or as a consultant or Decision Intelligence Service Provider (DISP) or Decision Intelligence Infrastructure Provider (DIIP) for others. It’s organ‐ ized around a collection of nine DI “best practice” processes. We’ll walk you through each one, starting with how to decide if DI is right for your situation. We’ll show you how to go about designing a decision. By the time you work through the book, you’ll have a continuously improvable decision asset that is connected to data, AI, and more in a way that will drive competitive differentiation and success through better decision making. But before we dive into the gnarly details, we feel it’s important for you to understand that you can start doing DI today. Seriously, we’re talking about 20 minutes from now: the time that it takes to get to the section called “Build Your First CDD, Right Now!” in the next chapter. Decision Intelligence in a Nutshell Simply put, DI helps organizations make better decisions. It helps decision makers understand how the potential actions they can take today (the things they can do) could affect their desired outcomes (the things they want to accomplish). To get from actions to outcomes, DI centers around a drawing called a causal decision diagram xiv | Preface

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(CDD), which acts as a “decision blueprint.” The CDD lets you design a decision. Its purpose is to get everyone on the same page—technologists, decision makers, and even the stakeholders affected by the decision. To give you an idea of what a CDD looks like, Figure P-1 shows a very simple one. Figure P-1. A simple causal decision diagram (CDD). We’ll have more to say about CDDs in later chapters, but you can see a few things right off the bat. We draw actions on the left-hand side of the diagram and outcomes on the right. Between the two is a chain of consequences. (Note that these are consequences, not tasks, and are—as a rule—outside of your control after you take an action.) Figure P-2 shows a more complex CDD, including some annotation showing where technology fits into a decision. (To look at the details, you can download a PDF from the book’s supplemental materials repository.) Preface | xv

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Figure P-2. A CDD that helps building managers decide “When and how should I go about opening my building and keeping people safe from the pandemic?” To draw a CDD, you document your desired outcomes and the actions you can take to achieve them. You capture these in a diagram that also shows your understanding of the cause-and-effect chains that connect actions to outcomes. Later, if you want, you can add data, evidence, models, analytics, and human expertise that inform your decision, so that your diagram isn’t just on paper but also can be simulated by a xvi | Preface

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computer. In this form, the simulation based on the CDD is effectively a “decision digital twin,” providing an evidence-based way to determine which actions will be most effective in achieving your outcomes. We’ll walk you through the process for creating CDDs in this book. At its most fundamental, DI is an integration and design discipline, connecting tech‐ nologies together with each other and with human decision makers. And the CDD shows how these pieces fit together. Simply put, DI helps organizations leverage the best of human expertise, hand in hand with all sorts of technology. DI begins and ends with the group or person making the decision. Technology is secondary, used in support of better decisions. Not fully automated, yet not just based on human judg‐ ment, DI is a “hybrid” AI method—one of the fastest-growing technology markets. The DI methodology holds that structured decision making can be represented as a set of well-defined processes. These follow a lifecycle, beginning with formulating the decision at hand and ending with retrospectively analyzing the effectiveness of the chosen course of action, and possibly reusing the decision or elements of it for future decisions. That might sound like a lot, but an important aspect of DI is that it’s easy to do, especially at the start. Rather than asking you to think about decisions in a new way, DI simply asks that you document the way that you think about decisions today. You’ll find that just drawing a picture of a decision—as we’ll teach you to do in the next chapter—goes a long way. Along the way, you’ll find that DI includes these important elements: • Clearly defining decision requirements • Representing decision making as a set of well-defined processes that follow a life‐ cycle, from formulating the decision to retrospectively analyzing its effectiveness and potential for reuse • Following an iterative design process that incorporates data, analytics, and expert judgment; allows for multiple scenarios; and models different potential worlds • Creating a CDD as a unifying graphical representation for a designed decision • Integrating decision assets like data, human knowledge, and machine learning (ML) and AI models with elements of the CDD; this lets decisions be driven by data and more • Emphasizing quality assurance and security • Transforming into a decision-centric organization using organizational and cul‐ tural best practices Preface | xvii

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LLMs, OMG As this book goes to press, ChatGPT and other large language models (LLMs) are turning the technology world upside down, supercharging writers, coders, scammers, and more. And DI is no exception. We are already seeing LLM technology provide incredibly valuable advice to our DISP clients, acting as a superpowered new collabo‐ rator in multiple phases of the DI processes you’ll read about here. In particular, we’ve seen LLMs surface actions, externals, outcomes, and unintended consequences that no one had previously considered, helping to reduce “tunnel vision.” We see LLMs’ role in DI as a sort of “super Google,” giving decision modelers and decision makers much easier access to a wide range of assets that they can use to inform their decision making. But LLMs don’t do action-to-outcome simulations, so they’re complementary to the decision-reasoning methods we describe here. Who Is This Book For? This book is for you if you’d like to learn how to introduce DI to your organization or to your clients. You might be an executive who takes decisions seriously, combining the best of diverse human and computer knowledge to drive competitive advantage. You might be passionate about addressing climate change, but you know that there’s a lot of earth observation (EO) data that’s going unused because data scientists don’t know how to connect it to decision making. You might be a data or AI consultant or an employee in one of the emerging DISP companies, looking to differentiate your practice by providing something new and valuable. You might be an ML expert who wants to maximize the value of this important technology, or a head of analytics or business intelligence who needs a way to communicate with your internal clients so that your technology helps them with better evidence-based decisions. We’ve written this book for the “insurgent” bottom-up perspective, as well as for the lucky few who have obtained centralized executive sponsorship to take DI organization-wide. Indeed, we wrote this book in collaboration with a G20 central bank in the process of doing just that, and the bank has adapted this book for its internal use. What You Will Learn After completing this book, you will: • Understand the kinds of decisions organizations make and which ones DI can help with • Understand how to create, read, use, maintain, and reuse CDDs xviii | Preface

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• Have a “starter kit” of DI documents and templates that you can tailor for your organization And you’ll understand how to use DI to: • Structure decision conversations around desired outcomes (financial or not) and actions to achieve them • Use state-of-the-art collaborative tools to map AI, knowledge, data, and more into decisions • Find simplicity and order amid the confusion of complex data, tools, and decisions • Provide value to your data management projects by guiding them toward the 10% of data that has 90% of the value • Identify ways to integrate silos within your organization • Specify your requirements for automated decision-reasoning simulations to a software team • Earn greater trust and credibility for your data/analytics/AI team, because you speak your customer’s language Please note that there are several topics that are not covered here. For instance, we don’t delve into the broader societal impacts of DI or its potential for solving complex problems like the climate and pandemics. These impacts are covered in Link. Finally, we don’t get into the technical specifics of how to build DI tools such APIs, interfaces, or AI and statistical models that interoperate with computerized DI models. Those technologies change quickly, but the principles we offer here stand on their own, independent of specific technological choices. Assumptions This Book Makes We do not assume that you have any specific technical knowledge. We have written this book to help all the participants in a decision-making process—not only the executives, managers, and stakeholders, but the analysts and data scientists who provide data and other evidence to decision makers. We also do not assume that you have read Link. We’ll introduce everything about DI that you need to know. Where Link was a visionary survey of the field, this book gives you actionable steps you can take to do DI, today and right now, with or without technology. This book also focuses on DI processes: our emphasis here is on the sequence of steps to take within your organization to make better decisions with better outcomes. Preface | xix

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