Decision Intelligence For Dummies
- Length: 320 pages
- Edition: 1
- Language: English
- Publisher: For Dummies
- Publication Date: 2022-02-08
- ISBN-10: 1119824842
- ISBN-13: 9781119824848
- Sales Rank: #5298033 (See Top 100 Books)
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.
Title Page Copyright Page Table of Contents Introduction About This Book Conventions Used in This Book Foolish Assumptions What You Don’t Have to Read How This Book Is Organized Part 1: Getting Started with Decision Intelligence Part 2: Reaching the Best Possible Decision Part 3: Establishing Reality Checks Part 4: Proposing a New Directive Part 5: The Part of Tens 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 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 Hyperautomation 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 EULA
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