Decision Intelligence for Dummies

Decision Intelligence for Dummies /

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Bibliographic Details
Main Author: Baker, Pamela
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: Hoboken, New Jersey : John Wiley & Sons, [2022]
Subjects:
Decision making.
Decision Making
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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505 0 0 |a Machine generated contents note:   |t About This Book --   |t Conventions Used in This Book --   |t Foolish Assumptions --   |t What You Don't Have to Read --   |t How This Book Is Organized --   |t Part 1: Getting Started with Decision Intelligence --   |t Part 2: Reaching the Best Possible Decision --   |t Part 3: Establishing Reality Checks --   |t Part 4: Proposing a New Directive --   |t Part 5: The Part of Tens --   |t Icons Used in This Book --   |t Beyond the Book --   |t Where to Go from Here --   |g ch. 1   |t Short Takes on Decision Intelligence --   |t Tale of Two Decision Trails --   |t Pointing out the way --   |t Making a decision --   |t Deputizing AI as Your Faithful Sidekick --   |t Seeing How Decision Intelligence Looks on Paper --   |t Tracking the Inverted V --   |t Estimating How Much Decision Intelligence Will Cost You --   |g ch. 2   |t Mining Data versus Minding the Answer --   |t Knowledge Is Power - Data Is Just Information --   |t Experiencing the epiphany --   |t Embracing the new, not-so-new idea --   |t Avoiding thought boxes and data query borders --   |t Reinventing Actionable Outcomes --   |t Living with the fact that we have answers and still don't know what to do --   |t Going where humans fear to tread on data --   |t Ushering in The Great Revival: Institutional knowledge and human expertise --   |g ch. 3   |t Cryptic Patterns and Wild Guesses --   |t Machines Make Human Mistakes, Too --   |t Seeing the Trouble Math Makes --   |t limits of math-only approaches --   |t right math for the wrong question --   |t Why data scientists and statisticians often make bad question-makers --   |t Identifying Patterns and Missing the Big Picture --   |t All the helicopters are broken --   |t MIA: Chunks of crucial but hard-to-get real-world data --   |t Evaluating man-versus-machine in decision-making --   |g ch. 4   |t Inverted V Approach --   |t Putting Data First Is the Wrong Move --   |t What's a decision, anyway? --   |t Any road will take you there --   |t great rethink when it comes to making decisions at scale --   |t Applying the Upside-Down V: The Path to the Output and Back Again --   |t Evaluating Your Inverted V Revelations --   |t Having Your Inverted V Lightbulb Moment --   |t Recognizing Why Things Go Wrong --   |t Aiming for too broad an outcome --   |t Mimicking data outcomes --   |t Failing to consider other decision sciences --   |t Mistaking gut instincts for decision science --   |t Failing to change the culture --   |g ch. 5   |t Shaping a Decision into a Query --   |t Defining Smart versus Intelligent --   |t Discovering That Business Intelligence Is Not Decision Intelligence --   |t Discovering the Value of Context and Nuance --   |t Defining the Action You Seek --   |t Setting Up the Decision --   |t Decision science versus data science --   |t Framing your decision --   |t Heuristics and other leaps of faith --   |g ch. 6   |t Mapping a Path Forward --   |t Putting Data Last --   |t Recognizing when you can (and should) skip the data entirely --   |t Leaning on CRISP-DM --   |t Using the result you seek to identify the data you need --   |t Digital decisioning and decision intelligence --   |t Don't store all your data - know when to throw it out --   |t Adding More Humans to the Equation --   |t shift in thinking at the business line level --   |t How decision intelligence puts executives and ordinary humans back in charge --   |t Limiting Actions to What Your Company Will Actually Do --   |t Looking at budgets versus the company will --   |t Setting company culture against company resources --   |t Using long-term decisioning to craft short-term returns --   |g ch. 7   |t Your DI Toolbox --   |t Decision Intelligence Is a Rethink, Not a Data Science Redo --   |t Taking Stock of What You Already Have --   |t tool overview --   |t Working with BI apps --   |t Accessing cloud tools --   |t Taking inventory and finding the gaps --   |t Adding Other Tools to the Mix --   |t Decision modeling software --   |t Business rule management systems --   |t Machine learning and model stores --   |t Data platforms --   |t Data visualization tools --   |t Option round-up --   |t Taking a Look at What Your Computing Stack Should Look Like Now --   |g ch. 8   |t Taking a Bow: Goodbye, Data Scientists - Hello, Data Strategists --   |t Making Changes in Organizational Roles --   |t Leveraging your current data scientist roles --   |t Realigning your existing data teams --   |t Looking at Emerging DI Jobs --   |t Hiring data strategists versus hiring decision strategists --   |t Onboarding mechanics and pot washers --   |t Chief Data Officers Fate --   |t Freeing Executives to Lead Again --   |g ch. 9   |t Trusting AI and Tackling Scary Things --   |t Discovering the Truth about AI --   |t Thinking in AI --   |t Thinking in human --   |t Letting go of your ego --   |t Seeing Whether You Can Trust AI --   |t Finding out why AI is hard to test and harder to understand --   |t Hearing AI's confession --   |t Two AI's Walk into a Bar --   |t Doing the right math but asking the wrong question --   |t Dealing with conflicting outputs --   |t Battling AI's --   |g ch. 10   |t Meddling Data and Mindful Humans --   |t Engaging with Decision Theory --   |t Working with your gut instincts --   |t Looking at the role of the social sciences --   |t Examining the role of the managerial sciences --   |t Role of Data Science in Decision Intelligence --   |t Fitting data science to decision intelligence --   |t Reimagining the rules --   |t Expanding the notion of a data source --   |t Where There's a Will, There's a Way --   |g ch. 11   |t Decisions at Scale --   |t Plugging and Unplugging AI into Automation --   |t Dealing with Model Drifts and Bad Calls --   |t Reining in AutoML --   |t Seeing the Value of ModelOps --   |t Bracing for Impact --   |t Decide and dedicate --   |t Make decisions with a specific impact in mind --   |g ch. 12   |t Metrics and Measures --   |t Living with Uncertainty --   |t Making the Decision --   |t Seeing How Much a Decision Is Worth --   |t Matching the Metrics to the Measure --   |t Leaning into KPIs --   |t Tapping into change data --   |t Testing AI --   |t Deciding When to Weigh the Decision and When to Weigh the Impact --   |g ch. 13   |t Role of DI in the Idea Economy --   |t Turning Decisions into Ideas --   |t Repeating previous successes --   |t Predicting new successes --   |t Weighing the value of repeating successes versus creating new successes --   |t Leveraging AI to find more idea patterns --   |t Disruption Is the Point --   |t Creative problem-solving is the new competitive edge --   |t Bending the company culture --   |t Competing in the Moment --   |t Changing Winds and Changing Business Models --   |t Counting Wins in Terms of Impacts --   |g ch. 14   |t Seeing How Decision Intelligence Changes Industries and Markets --   |t Facing the What-If Challenge --   |t What-if analysis in scenarios in Excel --   |t What-if analysis using a Data Tables feature --   |t What-if analysis using a Goal Seek feature --   |t Learning Lessons from the Pandemic --   |t Refusing to make decisions in a vacuum --   |t Living with toilet paper shortages and supply chain woes --   |t Revamping businesses overnight --   |t Seeing how decisions impact more than the Land of Now --   |t Rebuilding at the Speed of Disruption --   |t Redefining Industries --   |g ch. 15   |t Trickle-Down and Streaming-Up Decisioning --   |t Understanding the Who, What, Where, and Why of Decision-Making --   |t Trickling Down Your Upstream Decisions --   |t Looking at Streaming Decision-Making Models --   |t Making Downstream Decisions --   |t Thinking in Systems --   |t Taking Advantage of Systems Tools --   |t Conforming and Creating at the Same Time --   |t Directing Your Business Impacts to a Common Goal --   |t Dealing with Decision Singularities --   |t Revisiting the Inverted V --   |g ch. 16   |t Career Makers and Deal-Breakers --   |t Taking the Machine's Advice --   |t Adding Your Own Take --   |t Mastering your decision intelligence superpowers --   |t Ensuring that you have great data sidekicks --   |t New Influencers: Decision Masters --   |t Preventing Wrong Influences from Affecting Decisions --   |t Bad influences in AI and analytics --   |t blame game --   |t Ugly politics and happy influencers --   |t Risk Factors in Decision Intelligence --   |t DI and Hyperautomation --   |g ch. 17   |t Ten Steps to Setting Up a Smart Decision --   |t Check Your Data Source --   |t Track Your Data Lineage --   |t Know Your Tools --   |t Use Automated Visualizations --   |t Impact = Decision --   |t Do Reality Checks --   |t Limit Your Assumptions --   |t Think Like a Science Teacher --   |t Solve for Missing Data --   |t Partial versus incomplete data --   |t Clues and missing answers --   |t Take Two Perspectives and Call Me in the Morning --   |g ch. 18   |t Bias In, Bias Out (and Other Pitfalls) --   |t Pitfalls Overview --   |t Relying on Racist Algorithms --   |t Following a Flawed Model for Repeat Offenders --   |t Using A Sexist Hiring Algorithm --   |t Redlining Loans --   |t Leaning on Irrelevant Information --   |t Falling Victim to Framing Foibles --   |t Being Overconfident --   |t Lulled by Percentages --   |t Dismissing with Prejudice. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 0 |a Online resource; title from digital title page (viewed on January 26, 2022). 
650 0 |a Decision making. 
650 2 |a Decision Making 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |a Baker, Pamela.  |t Decision Intelligence for Dummies.  |d Newark : John Wiley & Sons, Incorporated, ©2022  |z 9781119824848 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=6837889  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
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919 |a .ulebk  |b 2020-07-09 
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