Description
Book SynopsisLearn to use, and not be used by, datato make more insightful decisions The availability of data and various forms of AI unlock countless possibilities forbusiness decision makers.But what do you do when youfeelpressuredto cede yourposition in the decision-making process altogether? Decision IntelligenceForDummiespumps the brakes onthe 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 toachieve maximum flexibilitybyusingeveryavailable 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 thananend in itself, by expanding yourdecision-making inquiriesFind a new path to solid decisionsthat includes, but isn't dominated, by quantitative dataMeasure the results of your newframeworkto prove its effectiveness and efficiencyand expand it to a whole team or company Perfect forbusiness leaders in technology and finance,Decision IntelligenceForDummiesis ideal for anyone who recognizes that data isnot the only powerful tool in your decision-making toolbox.This book shows you how to be guided, and not ruled, by the data.
Table of ContentsIntroduction 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
