Decision Intelligence For Dummies

Pam Bakeris a veteran business analyst and journalist whose work is focused on big data, artificial intelligence, machine learning, business intelligence, and data analysis. She is the author of Data Divination Big Data Strategies.

Introduction 1 About This Book 2 Conventions Used in This Book 3 Foolish Assumptions 3 What You Dont Have to Read 4 How This Book Is Organized 5 Part 1: Getting Started with Decision Intelligence 5 Part 2: Reaching the Best Possible Decision 5 Part 3: Establishing Reality Checks 5 Part 4: Proposing a New Directive 6 Part 5: The Part of Tens 6 Icons Used in This Book 6 Beyond the Book 7 Where to Go from Here 7 Part 1: Getting Started with Decision Intelligence 9 Chapter 1: Short Takes on Decision Intelligence 11 The Tale of Two Decision Trails 12 Pointing out the way 13 Making a decision 16 Deputizing AI as Your Faithful Sidekick 18 Seeing How Decision Intelligence Looks on Paper 20 Tracking the Inverted V 21 Estimating How Much Decision Intelligence Will Cost You 22 Chapter 2: Mining Data versus Minding the Answer 25 Knowledge Is Power Data Is Just Information 26 Experiencing the epiphany 26 Embracing the new, not-so-new idea 28 Avoiding thought boxes and data query borders 29 Reinventing Actionable Outcomes 32 Living with the fact that we have answers and still dont know what to do 32 Going where humans fear to tread on data 34 Ushering in The Great Revival: Institutional knowledge and human expertise 36 Chapter 3: Cryptic Patterns and Wild Guesses 39 Machines Make Human Mistakes, Too 40 Seeing the Trouble Math Makes 42 The limits of math-only approaches 42 The right math for the wrong question 43 Why data scientists and statisticians often make bad question-makers 46 Identifying Patterns and Missing the Big Picture 48 All the helicopters are broken 48 MIA: Chunks of crucial but hard-to-get real-world data 49 Evaluating man-versus-machine in decision-making 51 Chapter 4: The Inverted V Approach 53 Putting Data First Is the Wrong Move 54 Whats a decision, anyway? 55 Any road will take you there 56 The great rethink when it comes to making decisions at scale 57 Applying the Upside-Down V: The Path to the Output and Back Again 59 Evaluating Your Inverted V Revelations 60 Having Your Inverted V Lightbulb Moment 61 Recognizing Why Things Go Wrong 63 Aiming for too broad an outcome 63 Mimicking data outcomes 64 Failing to consider other decision sciences 64 Mistaking gut instincts for decision science 64 Failing to change the culture 65 Part 2: Reaching the Best Possible Decision 67 Chapter 5: Shaping a Decision into a Query 69 Defining Smart versus Intelligent 70 Discovering That Business Intelligence Is Not Decision Intelligence 71 Discovering the Value of Context and Nuance 72 Defining the Action You Seek 73 Setting Up the Decision 74 Decision science versus data science 75 Framing your decision 77 Heuristics and other leaps of faith 78 Chapter 6: Mapping a Path Forward 81 Putting Data Last 82 Recognizing when you can (and should) skip the data entirely 83 Leaning on CRISP-DM 84 Using the result you seek to identify the data you need 85 Digital decisioning and decision intelligence 85 Dont store all your data know when to throw it out 87 Adding More Humans to the Equation 88 The shift in thinking at the business line level 90 How decision intelligence puts executives and ordinary humans back in charge 92 Limiting Actions to What Your Company Will Actually Do 94 Looking at budgets versus the company will 95 Setting company culture against company resources 98 Using long-term decisioning to craft short-term returns 99 Chapter 7: Your DI Toolbox 101 Decision Intelligence Is a Rethink, Not a Data Science Redo 102 Taking Stock of What You Already Have 103 The tool overview 104 Working with BI apps 105 Accessing cloud tools 106 Taking inventory and finding the gaps 107 Adding Other Tools to the Mix 108 Decision modeling software 109 Business rule management systems 110 Machine learning and model stores 110 Data platforms 112