Decision Intelligence For Dummies

Introduction

About This Book  

Conventions Used in This Book  

Foolish Assumptions  

What You Don't Have to Read  

How This Book Is Organized  

Icons Used in This Book  

Beyond the Book  

Where to Go from Here  

Part 1: Getting Started with Decision Intelligence 

Chapter 1: Short Takes on Decision Intelligence 

• The Tale of Two Decision Trails  
• Pointing out the way  
• Making a decision  
• Deputizing AI as Your Faithful Sidekick  
• Seeing How Decision Intelligence Looks on Paper  
• Tracking the Inverted V  
• Estimating How Much Decision Intelligence Will Cost You  

Chapter 2: Mining Data versus Minding the Answer 

• Knowledge Is Power -- Data Is Just Information  
• Experiencing the epiphany  
• Embracing the new, not-so-new idea  
• Avoiding thought boxes and data query borders  
• Reinventing Actionable Outcomes  
• Living with the fact that we have answers and still don't know what to do  
• Going where humans fear to tread on data  
• Ushering in The Great Revival: Institutional knowledge and human expertise  

Chapter 3: Cryptic Patterns and Wild Guesses 

• Machines Make Human Mistakes, Too  
• Seeing the Trouble Math Makes  
• The limits of math-only approaches  
• The right math for the wrong question  
• Why data scientists and statisticians often make bad question-makers  
• Identifying Patterns and Missing the Big Picture  
• All the helicopters are broken  
• MIA: Chunks of crucial but hard-to-get real-world data  
• Evaluating man-versus-machine in decision-making  

Chapter 4: The Inverted V Approach 

• Putting Data First Is the Wrong Move  
• What's a decision, anyway?  
• Any road will take you there  
• The great rethink when it comes to making decisions at scale  
• Applying the Upside-Down V: The Path to the Output and Back Again  
• Evaluating Your Inverted V Revelations  
• Having Your Inverted V Lightbulb Moment  
• Recognizing Why Things Go Wrong  
• Aiming for too broad an outcome  
• Mimicking data outcomes  
• Failing to consider other decision sciences  
• Mistaking gut instincts for decision science  
• Failing to change the culture  

Part 2: Reaching the Best Possible Decision 

Chapter 5: Shaping a Decision into a Query 

• Defining Smart versus Intelligent  
• Discovering That Business Intelligence Is Not Decision Intelligence  
• Discovering the Value of Context and Nuance  
• Defining the Action You Seek  
• Setting Up the Decision  
• Decision science versus data science  
• Framing your decision  
• Heuristics and other leaps of faith  

Chapter 6: Mapping a Path Forward 

• Putting Data Last  
• Recognizing when you can (and should) skip the data entirely  
• Leaning on CRISP-DM  
• Using the result you seek to identify the data you need  
• Digital decisioning and decision intelligence  
• Don't store all your data -- know when to throw it out  
• Adding More Humans to the Equation  
• The shift in thinking at the business line level  
• How decision intelligence puts executives and ordinary humans back in charge  
• Limiting Actions to What Your Company Will Actually Do  
• Looking at budgets versus the company will  
• Setting company culture against company resources  
• Using long-term decisioning to craft short-term returns  

Chapter 7: Your DI Toolbox 

• Decision Intelligence Is a Rethink, Not a Data Science Redo  
• Taking Stock of What You Already Have  
• The tool overview  
• Working with BI apps  
• Accessing cloud tools  
• Taking inventory and finding the gaps  
• Adding Other Tools to the Mix  
• Decision modeling software  
• Business rule management systems  
• Machine learning and model stores  
• Data platforms  
• Data visualization tools  
• Option round-up  
• Taking a Look at What Your Computing Stack Should Look Like Now

Part 3: Establishing Reality Checks 

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

• Making Changes in Organizational Roles  
• Leveraging your current data scientist roles  
• Realigning your existing data teams  
• Looking at Emerging DI Jobs  
• Hiring data strategists versus hiring decision strategists  
• Onboarding mechanics and pot washers  
• The Chief Data Officer's Fate  
• Freeing Executives to Lead Again  

Chapter 9: Trusting AI and Tackling Scary Things 

• Discovering the Truth about AI  
• Thinking in AI  
• Thinking in human  
• Letting go of your ego  
• Seeing Whether You Can Trust AI  
• Finding out why AI is hard to test and harder to understand  
• Hearing AI's confession  
• Two AIs Walk into a Bar  
• Doing the right math but asking the wrong question  
• Dealing with conflicting outputs  
• Battling AIs  

Chapter 10: Meddling Data and Mindful Humans 

• Engaging with Decision Theory  
• Working with your gut instincts  
• Looking at the role of the social sciences  
• Examining the role of the managerial sciences  
• The Role of Data Science in Decision Intelligence  
• Fitting data science to decision intelligence  
• Reimagining the rules  
• Expanding the notion of a data source  
• Where There's a Will, There's a Way  

Chapter 11: Decisions at Scale 

• Plugging and Unplugging AI into Automation  
• Dealing with Model Drifts and Bad Calls  
• Reining in AutoML  
• Seeing the Value of ModelOps  
• Bracing for Impact  
• Decide and dedicate  
• Make decisions with a specific impact in mind  

Chapter 12: Metrics and Measures 

• Living with Uncertainty  
• Making the Decision  
• Seeing How Much a Decision Is Worth  
• Matching the Metrics to the Measure  
• Leaning into KPIs  
• Tapping into change data  
• Testing AI  
• Deciding When to Weigh the Decision and When to Weigh the Impact  

Part 4: Proposing A New Directive 

Chapter 13: The Role of DI in the Idea Economy 

• Turning Decisions into Ideas  
• Repeating previous successes  
• Predicting new successes  
• Weighing the value of repeating successes versus creating new successes  
• Leveraging AI to find more idea patterns  
• Disruption Is the Point  
• Creative problem-solving is the new competitive edge  
• Bending the company culture  
• Competing in the Moment  
• Changing Winds and Changing Business Models  
• Counting Wins in Terms of Impacts  

Chapter 14: Seeing How Decision Intelligence Changes Industries and Markets 

• Facing the What-If Challenge  
• What-if analysis in scenarios in Excel  
• What-if analysis using a Data Tables feature  
• What-if analysis using a Goal Seek feature  
• Learning Lessons from the Pandemic  
• Refusing to make decisions in a vacuum  
• Living with toilet paper shortages and supply chain woes  
• Revamping businesses overnight  
• Seeing how decisions impact more than the Land of Now  
• Rebuilding at the Speed of Disruption  
• Redefining Industries

Chapter 15: Trickle-Down and Streaming-Up Decisioning 

• Understanding the Who, What, Where, and Why of Decision-Making  
• Trickling Down Your Upstream Decisions  
• Looking at Streaming Decision-Making Models  
• Making Downstream Decisions  
• Thinking in Systems  
• Taking Advantage of Systems Tools  
• Conforming and Creating at the Same Time  
• Directing Your Business Impacts to a Common Goal  
• Dealing with Decision Singularities  
• Revisiting the Inverted V

Chapter 16: Career Makers and Deal-Breakers 

• Taking the Machine's Advice  
• Adding Your Own Take  
• Mastering your decision intelligence superpowers  
• Ensuring that you have great data sidekicks  
• The New Influencers: Decision Masters  
• Preventing Wrong Influences from Affecting Decisions  
• Bad influences in AI and analytics  
• The blame game  
• Ugly politics and happy influencers  
• Risk Factors in Decision Intelligence  
• DI and Hyper automation  

Part 5: The Part of Tens 

Chapter 17: Ten Steps to Setting Up a Smart Decision 

• Check Your Data Source  
• Track Your Data Lineage  
• Know Your Tools  
• Use Automated Visualizations  
• Impact = Decision  
• Do Reality Checks  
• Limit Your Assumptions  
• Think Like a Science Teacher  
• Solve for Missing Data  
• Partial versus incomplete data  
• Clues and missing answers  
• Take Two Perspectives and Call Me in the Morning

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

• A Pitfalls Overview  
• Relying on Racist Algorithms  
• Following a Flawed Model for Repeat Offenders  
• Using A Sexist Hiring Algorithm  
• Redlining Loans  
• Leaning on Irrelevant Information  
• Falling Victim to Framing Foibles  
• Being Overconfident  
• Lulled by Percentages  
• Dismissing with Prejudice  

Index