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 1About This Book 2Conventions Used in This Book 3Foolish Assumptions 3What You Don’t Have to Read 4How This Book Is Organized 5Part 1: Getting Started with Decision Intelligence 5Part 2: Reaching the Best Possible Decision 5Part 3: Establishing Reality Checks 5Part 4: Proposing a New Directive 6Part 5: The Part of Tens 6Icons Used in This Book 6Beyond the Book 7Where to Go from Here 7Part 1: Getting Started with Decision Intelligence 9Chapter 1: Short Takes on Decision Intelligence 11The Tale of Two Decision Trails 12Pointing out the way 13Making a decision 16Deputizing AI as Your Faithful Sidekick 18Seeing How Decision Intelligence Looks on Paper 20Tracking the Inverted V 21Estimating How Much Decision Intelligence Will Cost You 22Chapter 2: Mining Data versus Minding the Answer 25Knowledge Is Power — Data Is Just Information 26Experiencing the epiphany 26Embracing the new, not-so-new idea 28Avoiding thought boxes and data query borders 29Reinventing Actionable Outcomes 32Living with the fact that we have answers and still don’t know what to do 32Going where humans fear to tread on data 34Ushering in The Great Revival: Institutional knowledge and human expertise 36Chapter 3: Cryptic Patterns and Wild Guesses 39Machines Make Human Mistakes, Too 40Seeing the Trouble Math Makes 42The limits of math-only approaches 42The right math for the wrong question 43Why data scientists and statisticians often make bad question-makers 46Identifying Patterns and Missing the Big Picture 48All the helicopters are broken 48MIA: Chunks of crucial but hard-to-get real-world data 49Evaluating man-versus-machine in decision-making 51Chapter 4: The Inverted V Approach 53Putting Data First Is the Wrong Move 54What’s a decision, anyway? 55Any road will take you there 56The great rethink when it comes to making decisions at scale 57Applying the Upside-Down V: The Path to the Output and Back Again 59Evaluating Your Inverted V Revelations 60Having Your Inverted V Lightbulb Moment 61Recognizing Why Things Go Wrong 63Aiming for too broad an outcome 63Mimicking data outcomes 64Failing to consider other decision sciences 64Mistaking gut instincts for decision science 64Failing to change the culture 65Part 2: Reaching the Best Possible Decision 67Chapter 5: Shaping a Decision into a Query 69Defining Smart versus Intelligent 70Discovering That Business Intelligence Is Not Decision Intelligence 71Discovering the Value of Context and Nuance 72Defining the Action You Seek 73Setting Up the Decision 74Decision science versus data science 75Framing your decision 77Heuristics and other leaps of faith 78Chapter 6: Mapping a Path Forward 81Putting Data Last 82Recognizing when you can (and should) skip the data entirely 83Leaning on CRISP-DM 84Using the result you seek to identify the data you need 85Digital decisioning and decision intelligence 85Don’t store all your data — know when to throw it out 87Adding More Humans to the Equation 88The shift in thinking at the business line level 90How decision intelligence puts executives and ordinary humans back in charge 92Limiting Actions to What Your Company Will Actually Do 94Looking at budgets versus the company will 95Setting company culture against company resources 98Using long-term decisioning to craft short-term returns 99Chapter 7: Your DI Toolbox 101Decision Intelligence Is a Rethink, Not a Data Science Redo 102Taking Stock of What You Already Have 103The tool overview 104Working with BI apps 105Accessing cloud tools 106Taking inventory and finding the gaps 107Adding Other Tools to the Mix 108Decision modeling software 109Business rule management systems 110Machine learning and model stores 110Data platforms 112Data visualization tools 112Option round-up 113Taking a Look at What Your Computing Stack Should Look Like Now 113Part 3: Establishing Reality Checks 115Chapter 8: Taking a Bow: Goodbye, Data Scientists — Hello, Data Strategists 117Making Changes in Organizational Roles 118Leveraging your current data scientist roles 120Realigning your existing data teams 121Looking at Emerging DI Jobs 122Hiring data strategists versus hiring decision strategists 125Onboarding mechanics and pot washers 127The Chief Data Officer’s Fate 127Freeing Executives to Lead Again 129Chapter 9: Trusting AI and Tackling Scary Things 131Discovering the Truth about AI 132Thinking in AI 133Thinking in human 136Letting go of your ego 137Seeing Whether You Can Trust AI 138Finding out why AI is hard to test and harder to understand 140Hearing AI’s confession 142Two AIs Walk into a Bar 144Doing the right math but asking the wrong question 146Dealing with conflicting outputs 147Battling AIs 148Chapter 10: Meddling Data and Mindful Humans 151Engaging with Decision Theory 152Working with your gut instincts 153Looking at the role of the social sciences 155Examining the role of the managerial sciences 156The Role of Data Science in Decision Intelligence 157Fitting data science to decision intelligence 157Reimagining the rules 159Expanding the notion of a data source 161Where There’s a Will, There’s a Way 163Chapter 11: Decisions at Scale 165Plugging and Unplugging AI into Automation 167Dealing with Model Drifts and Bad Calls 168Reining in AutoML 170Seeing the Value of ModelOps 173Bracing for Impact 174Decide and dedicate 174Make decisions with a specific impact in mind 175Chapter 12: Metrics and Measures 179Living with Uncertainty 180Making the Decision 182Seeing How Much a Decision Is Worth 185Matching the Metrics to the Measure 187Leaning into KPIs 188Tapping into change data 191Testing AI 193Deciding When to Weigh the Decision and When to Weigh the Impact 195Part 4: Proposing A New Directive 197Chapter 13: The Role of DI in the Idea Economy 199Turning Decisions into Ideas 200Repeating previous successes 201Predicting new successes 202Weighing the value of repeating successes versus creating new successes 202Leveraging AI to find more idea patterns 203Disruption Is the Point 205Creative problem-solving is the new competitive edge 205Bending the company culture 207Competing in the Moment 207Changing Winds and Changing Business Models 209Counting Wins in Terms of Impacts 210Chapter 14: Seeing How Decision Intelligence Changes Industries and Markets 213Facing the What-If Challenge 214What-if analysis in scenarios in Excel 216What-if analysis using a Data Tables feature 217What-if analysis using a Goal Seek feature 218Learning Lessons from the Pandemic 220Refusing to make decisions in a vacuum 221Living with toilet paper shortages and supply chain woes 222Revamping businesses overnight 224Seeing how decisions impact more than the Land of Now 226Rebuilding at the Speed of Disruption 228Redefining Industries 230Chapter 15: Trickle-Down and Streaming-Up Decisioning 231Understanding the Who, What, Where, and Why of Decision-Making 232Trickling Down Your Upstream Decisions 234Looking at Streaming Decision-Making Models 236Making Downstream Decisions 238Thinking in Systems 240Taking Advantage of Systems Tools 241Conforming and Creating at the Same Time 244Directing Your Business Impacts to a Common Goal 245Dealing with Decision Singularities 246Revisiting the Inverted V 248Chapter 16: Career Makers and Deal-Breakers 251Taking the Machine’s Advice 252Adding Your Own Take 255Mastering your decision intelligence superpowers 257Ensuring that you have great data sidekicks 257The New Influencers: Decision Masters 259Preventing Wrong Influences from Affecting Decisions 262Bad influences in AI and analytics 262The blame game 265Ugly politics and happy influencers 266Risk Factors in Decision Intelligence 268DI and Hyperautomation 270Part 5: The Part of Tens 273Chapter 17: Ten Steps to Setting Up a Smart Decision 275Check Your Data Source 275Track Your Data Lineage 276Know Your Tools 277Use Automated Visualizations 278Impact = Decision 279Do Reality Checks 280Limit Your Assumptions 280Think Like a Science Teacher 281Solve for Missing Data 282Partial versus incomplete data 282Clues and missing answers 282Take Two Perspectives and Call Me in the Morning 283Chapter 18: Bias In, Bias Out (and Other Pitfalls) 285A Pitfalls Overview 285Relying on Racist Algorithms 286Following a Flawed Model for Repeat Offenders 287Using A Sexist Hiring Algorithm 287Redlining Loans 287Leaning on Irrelevant Information 288Falling Victim to Framing Foibles 288Being Overconfident 288Lulled by Percentages 289Dismissing with Prejudice 289Index 291