Decision Intelligence For Dummies

Learn to use, and not be used by, data to make more insightful decisions 

The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether? 

Decision Intelligence For Dummies pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible. 

In this timely book, you’ll learn to: 

• Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries 
• Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data 
• Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company 

Perfect for business leaders in technology and finance, Decision Intelligence For Dummies is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.  

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 Don’t 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 don’t 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

What’s 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

Don’t 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

Data visualization tools 112

Option round-up 113

Taking a Look at What Your Computing Stack Should Look Like Now 113

Part 3: Establishing Reality Checks 115

Chapter 8: Taking a Bow: Goodbye, Data Scientists — Hello, Data Strategists 117

Making Changes in Organizational Roles 118

Leveraging your current data scientist roles 120

Realigning your existing data teams 121

Looking at Emerging DI Jobs 122

Hiring data strategists versus hiring decision strategists 125

Onboarding mechanics and pot washers 127

The Chief Data Officer’s Fate 127

Freeing Executives to Lead Again 129

Chapter 9: Trusting AI and Tackling Scary Things 131

Discovering the Truth about AI 132

Thinking in AI 133

Thinking in human 136

Letting go of your ego 137

Seeing Whether You Can Trust AI 138

Finding out why AI is hard to test and harder to understand 140

Hearing AI’s confession 142

Two AIs Walk into a Bar 144

Doing the right math but asking the wrong question 146

Dealing with conflicting outputs 147

Battling AIs 148

Chapter 10: Meddling Data and Mindful Humans 151

Engaging with Decision Theory 152

Working with your gut instincts 153

Looking at the role of the social sciences 155

Examining the role of the managerial sciences 156

The Role of Data Science in Decision Intelligence 157

Fitting data science to decision intelligence 157

Reimagining the rules 159

Expanding the notion of a data source 161

Where There’s a Will, There’s a Way 163

Chapter 11: Decisions at Scale 165

Plugging and Unplugging AI into Automation 167

Dealing with Model Drifts and Bad Calls 168

Reining in AutoML 170

Seeing the Value of ModelOps 173

Bracing for Impact 174

Decide and dedicate 174

Make decisions with a specific impact in mind 175

Chapter 12: Metrics and Measures 179

Living with Uncertainty 180

Making the Decision 182

Seeing How Much a Decision Is Worth 185

Matching the Metrics to the Measure 187

Leaning into KPIs 188

Tapping into change data 191

Testing AI 193

Deciding When to Weigh the Decision and

When to Weigh the Impact 195

Part 4: Proposing A New Directive 197

Chapter 13: The Role of DI in the Idea Economy 199

Turning Decisions into Ideas 200

Repeating previous successes 201

Predicting new successes 202

Weighing the value of repeating successes versus

creating new successes 202

Leveraging AI to find more idea patterns 203

Disruption Is the Point 205

Creative problem-solving is the new competitive edge 205

Bending the company culture 207

Competing in the Moment 207

Changing Winds and Changing Business Models 209

Counting Wins in Terms of Impacts 210

Chapter 14: Seeing How Decision Intelligence Changes Industries and Markets 213

Facing the What-If Challenge 214

What-if analysis in scenarios in Excel 216

What-if analysis using a Data Tables feature 217

What-if analysis using a Goal Seek feature 218

Learning Lessons from the Pandemic 220

Refusing to make decisions in a vacuum 221

Living with toilet paper shortages and supply chain woes 222

Revamping businesses overnight 224

Seeing how decisions impact more than the Land of Now 226

Rebuilding at the Speed of Disruption 228

Redefining Industries 230

Chapter 15: Trickle-Down and Streaming-Up Decisioning 231

Understanding the Who, What, Where, and Why of

Decision-Making 232

Trickling Down Your Upstream Decisions 234

Looking at Streaming Decision-Making Models 236

Making Downstream Decisions 238

Thinking in Systems 240

Taking Advantage of Systems Tools 241

Conforming and Creating at the Same Time 244

Directing Your Business Impacts to a Common Goal 245

Dealing with Decision Singularities 246

Revisiting the Inverted V 248

Chapter 16: Career Makers and Deal-Breakers 251

Taking the Machine’s Advice 252

Adding Your Own Take 255

Mastering your decision intelligence superpowers 257

Ensuring that you have great data sidekicks 257

The New Influencers: Decision Masters 259

Preventing Wrong Influences from Affecting Decisions 262

Bad influences in AI and analytics 262

The blame game 265

Ugly politics and happy influencers 266

Risk Factors in Decision Intelligence 268

DI and Hyperautomation 270

Part 5: The Part of Tens 273

Chapter 17: Ten Steps to Setting Up a Smart Decision 275

Check Your Data Source 275

Track Your Data Lineage 276

Know Your Tools 277

Use Automated Visualizations 278

Impact = Decision 279

Do Reality Checks 280

Limit Your Assumptions 280

Think Like a Science Teacher 281

Solve for Missing Data 282

Partial versus incomplete data 282

Clues and missing answers 282

Take Two Perspectives and Call Me in the Morning 283

Chapter 18: Bias In, Bias Out (and Other Pitfalls) 285

A Pitfalls Overview 285

Relying on Racist Algorithms 286

Following a Flawed Model for Repeat Offenders 287

Using A Sexist Hiring Algorithm 287

Redlining Loans 287

Leaning on Irrelevant Information 288

Falling Victim to Framing Foibles 288

Being Overconfident 288

Lulled by Percentages 289

Dismissing with Prejudice 289

Index 291

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