Читать книгу Decision Intelligence For Dummies - Pamela Baker - Страница 3

1  Cover

2  Title Page

3  Copyright

4  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

5  Part 1: Getting Started with Decision Intelligence Chapter 1: Short Takes on Decision Intelligence The Tale of Two Decision Trails 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 Reinventing Actionable Outcomes Chapter 3: Cryptic Patterns and Wild Guesses Machines Make Human Mistakes, Too Seeing the Trouble Math Makes Identifying Patterns and Missing the Big Picture Chapter 4: The Inverted V Approach Putting Data First Is the Wrong Move 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

6  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 Chapter 6: Mapping a Path Forward Putting Data Last Adding More Humans to the Equation Limiting Actions to What Your Company Will Actually Do Chapter 7: Your DI Toolbox Decision Intelligence Is a Rethink, Not a Data Science Redo Taking Stock of What You Already Have Adding Other Tools to the Mix Taking a Look at What Your Computing Stack Should Look Like Now

7  Part 3: Establishing Reality Checks Chapter 8: Taking a Bow: Goodbye, Data Scientists — Hello, Data Strategists Making Changes in Organizational Roles Looking at Emerging DI Jobs The Chief Data Officer’s Fate Freeing Executives to Lead Again Chapter 9: Trusting AI and Tackling Scary Things Discovering the Truth about AI Seeing Whether You Can Trust AI Two AIs Walk into a Bar … Chapter 10: Meddling Data and Mindful Humans Engaging with Decision Theory The Role of Data Science in Decision Intelligence 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 Chapter 12: Metrics and Measures Living with Uncertainty Making the Decision Seeing How Much a Decision Is Worth Matching the Metrics to the Measure Deciding When to Weigh the Decision and When to Weigh the Impact

8  Part 4: Proposing a New Directive Chapter 13: The Role of DI in the Idea Economy Turning Decisions into Ideas Disruption Is the Point 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 Learning Lessons from the Pandemic 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 The New Influencers: Decision Masters Preventing Wrong Influences from Affecting Decisions Risk Factors in Decision Intelligence DI and Hyperautomation

9  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 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

10  Index

11  About the Book Author

12  Connect with Dummies

13  End User License Agreement