{"product_id":"decision-intelligence-for-dummies-isbn-9781119824848","title":"Decision Intelligence For Dummies","description":"\u003cp\u003e\u003cb\u003eLearn to use, and not be used by, data to make more insightful decisions \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe 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? \u003c\/p\u003e \u003cp\u003e\u003ci\u003eDecision Intelligence For Dummies \u003c\/i\u003epumps 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. \u003c\/p\u003e \u003cp\u003eIn this timely book, you’ll learn to: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eMake data a means to an end, rather than an end in itself, by expanding your decision-making inquiries \u003c\/li\u003e \u003cli\u003eFind a new path to solid decisions that includes, but isn’t dominated, by quantitative data \u003c\/li\u003e \u003cli\u003eMeasure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for business leaders in technology and finance, \u003ci\u003eDecision Intelligence For Dummies\u003c\/i\u003e 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.  \u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eConventions Used in This Book 3\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eWhat You Don’t Have to Read 4\u003c\/p\u003e \u003cp\u003eHow This Book Is Organized 5\u003c\/p\u003e \u003cp\u003ePart 1: Getting Started with Decision Intelligence 5\u003c\/p\u003e \u003cp\u003ePart 2: Reaching the Best Possible Decision 5\u003c\/p\u003e \u003cp\u003ePart 3: Establishing Reality Checks 5\u003c\/p\u003e \u003cp\u003ePart 4: Proposing a New Directive 6\u003c\/p\u003e \u003cp\u003ePart 5: The Part of Tens 6\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 6\u003c\/p\u003e \u003cp\u003eBeyond the Book 7\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Getting Started with Decision Intelligence 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Short Takes on Decision Intelligence 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Tale of Two Decision Trails 12\u003c\/p\u003e \u003cp\u003ePointing out the way 13\u003c\/p\u003e \u003cp\u003eMaking a decision 16\u003c\/p\u003e \u003cp\u003eDeputizing AI as Your Faithful Sidekick 18\u003c\/p\u003e \u003cp\u003eSeeing How Decision Intelligence Looks on Paper 20\u003c\/p\u003e \u003cp\u003eTracking the Inverted V 21\u003c\/p\u003e \u003cp\u003eEstimating How Much Decision Intelligence Will Cost You 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Mining Data versus Minding the Answer 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnowledge Is Power — Data Is Just Information 26\u003c\/p\u003e \u003cp\u003eExperiencing the epiphany 26\u003c\/p\u003e \u003cp\u003eEmbracing the new, not-so-new idea 28\u003c\/p\u003e \u003cp\u003eAvoiding thought boxes and data query borders 29\u003c\/p\u003e \u003cp\u003eReinventing Actionable Outcomes 32\u003c\/p\u003e \u003cp\u003eLiving with the fact that we have answers and still don’t know what to do 32\u003c\/p\u003e \u003cp\u003eGoing where humans fear to tread on data 34\u003c\/p\u003e \u003cp\u003eUshering in The Great Revival: Institutional knowledge and human expertise 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Cryptic Patterns and Wild Guesses 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachines Make Human Mistakes, Too 40\u003c\/p\u003e \u003cp\u003eSeeing the Trouble Math Makes 42\u003c\/p\u003e \u003cp\u003eThe limits of math-only approaches 42\u003c\/p\u003e \u003cp\u003eThe right math for the wrong question 43\u003c\/p\u003e \u003cp\u003eWhy data scientists and statisticians often make bad question-makers 46\u003c\/p\u003e \u003cp\u003eIdentifying Patterns and Missing the Big Picture 48\u003c\/p\u003e \u003cp\u003eAll the helicopters are broken 48\u003c\/p\u003e \u003cp\u003eMIA: Chunks of crucial but hard-to-get real-world data 49\u003c\/p\u003e \u003cp\u003eEvaluating man-versus-machine in decision-making 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: The Inverted V Approach 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePutting Data First Is the Wrong Move 54\u003c\/p\u003e \u003cp\u003eWhat’s a decision, anyway? 55\u003c\/p\u003e \u003cp\u003eAny road will take you there 56\u003c\/p\u003e \u003cp\u003eThe great rethink when it comes to making decisions at scale 57\u003c\/p\u003e \u003cp\u003eApplying the Upside-Down V: The Path to the Output and Back Again 59\u003c\/p\u003e \u003cp\u003eEvaluating Your Inverted V Revelations 60\u003c\/p\u003e \u003cp\u003eHaving Your Inverted V Lightbulb Moment 61\u003c\/p\u003e \u003cp\u003eRecognizing Why Things Go Wrong 63\u003c\/p\u003e \u003cp\u003eAiming for too broad an outcome 63\u003c\/p\u003e \u003cp\u003eMimicking data outcomes 64\u003c\/p\u003e \u003cp\u003eFailing to consider other decision sciences 64\u003c\/p\u003e \u003cp\u003eMistaking gut instincts for decision science 64\u003c\/p\u003e \u003cp\u003eFailing to change the culture 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Reaching the Best Possible Decision 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Shaping a Decision into a Query 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Smart versus Intelligent 70\u003c\/p\u003e \u003cp\u003eDiscovering That Business Intelligence Is Not Decision Intelligence 71\u003c\/p\u003e \u003cp\u003eDiscovering the Value of Context and Nuance 72\u003c\/p\u003e \u003cp\u003eDefining the Action You Seek 73\u003c\/p\u003e \u003cp\u003eSetting Up the Decision 74\u003c\/p\u003e \u003cp\u003eDecision science versus data science 75\u003c\/p\u003e \u003cp\u003eFraming your decision 77\u003c\/p\u003e \u003cp\u003eHeuristics and other leaps of faith 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Mapping a Path Forward 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePutting Data Last 82\u003c\/p\u003e \u003cp\u003eRecognizing when you can (and should) skip the data entirely 83\u003c\/p\u003e \u003cp\u003eLeaning on CRISP-DM 84\u003c\/p\u003e \u003cp\u003eUsing the result you seek to identify the data you need 85\u003c\/p\u003e \u003cp\u003eDigital decisioning and decision intelligence 85\u003c\/p\u003e \u003cp\u003eDon’t store all your data — know when to throw it out 87\u003c\/p\u003e \u003cp\u003eAdding More Humans to the Equation 88\u003c\/p\u003e \u003cp\u003eThe shift in thinking at the business line level 90\u003c\/p\u003e \u003cp\u003eHow decision intelligence puts executives and ordinary humans back in charge 92\u003c\/p\u003e \u003cp\u003eLimiting Actions to What Your Company Will Actually Do 94\u003c\/p\u003e \u003cp\u003eLooking at budgets versus the company will 95\u003c\/p\u003e \u003cp\u003eSetting company culture against company resources 98\u003c\/p\u003e \u003cp\u003eUsing long-term decisioning to craft short-term returns 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Your DI Toolbox 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Intelligence Is a Rethink, Not a Data Science Redo 102\u003c\/p\u003e \u003cp\u003eTaking Stock of What You Already Have 103\u003c\/p\u003e \u003cp\u003eThe tool overview 104\u003c\/p\u003e \u003cp\u003eWorking with BI apps 105\u003c\/p\u003e \u003cp\u003eAccessing cloud tools 106\u003c\/p\u003e \u003cp\u003eTaking inventory and finding the gaps 107\u003c\/p\u003e \u003cp\u003eAdding Other Tools to the Mix 108\u003c\/p\u003e \u003cp\u003eDecision modeling software 109\u003c\/p\u003e \u003cp\u003eBusiness rule management systems 110\u003c\/p\u003e \u003cp\u003eMachine learning and model stores 110\u003c\/p\u003e \u003cp\u003eData platforms 112\u003c\/p\u003e \u003cp\u003eData visualization tools 112\u003c\/p\u003e \u003cp\u003eOption round-up 113\u003c\/p\u003e \u003cp\u003eTaking a Look at What Your Computing Stack Should Look Like Now 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Establishing Reality Checks 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Taking a Bow: Goodbye, Data Scientists — Hello, Data Strategists 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMaking Changes in Organizational Roles 118\u003c\/p\u003e \u003cp\u003eLeveraging your current data scientist roles 120\u003c\/p\u003e \u003cp\u003eRealigning your existing data teams 121\u003c\/p\u003e \u003cp\u003eLooking at Emerging DI Jobs 122\u003c\/p\u003e \u003cp\u003eHiring data strategists versus hiring decision strategists 125\u003c\/p\u003e \u003cp\u003eOnboarding mechanics and pot washers 127\u003c\/p\u003e \u003cp\u003eThe Chief Data Officer’s Fate 127\u003c\/p\u003e \u003cp\u003eFreeing Executives to Lead Again 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Trusting AI and Tackling Scary Things 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscovering the Truth about AI 132\u003c\/p\u003e \u003cp\u003eThinking in AI 133\u003c\/p\u003e \u003cp\u003eThinking in human 136\u003c\/p\u003e \u003cp\u003eLetting go of your ego 137\u003c\/p\u003e \u003cp\u003eSeeing Whether You Can Trust AI 138\u003c\/p\u003e \u003cp\u003eFinding out why AI is hard to test and harder to understand 140\u003c\/p\u003e \u003cp\u003eHearing AI’s confession 142\u003c\/p\u003e \u003cp\u003eTwo AIs Walk into a Bar 144\u003c\/p\u003e \u003cp\u003eDoing the right math but asking the wrong question 146\u003c\/p\u003e \u003cp\u003eDealing with conflicting outputs 147\u003c\/p\u003e \u003cp\u003eBattling AIs 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Meddling Data and Mindful Humans 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEngaging with Decision Theory 152\u003c\/p\u003e \u003cp\u003eWorking with your gut instincts 153\u003c\/p\u003e \u003cp\u003eLooking at the role of the social sciences 155\u003c\/p\u003e \u003cp\u003eExamining the role of the managerial sciences 156\u003c\/p\u003e \u003cp\u003eThe Role of Data Science in Decision Intelligence 157\u003c\/p\u003e \u003cp\u003eFitting data science to decision intelligence 157\u003c\/p\u003e \u003cp\u003eReimagining the rules 159\u003c\/p\u003e \u003cp\u003eExpanding the notion of a data source 161\u003c\/p\u003e \u003cp\u003eWhere There’s a Will, There’s a Way 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Decisions at Scale 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePlugging and Unplugging AI into Automation 167\u003c\/p\u003e \u003cp\u003eDealing with Model Drifts and Bad Calls 168\u003c\/p\u003e \u003cp\u003eReining in AutoML 170\u003c\/p\u003e \u003cp\u003eSeeing the Value of ModelOps 173\u003c\/p\u003e \u003cp\u003eBracing for Impact 174\u003c\/p\u003e \u003cp\u003eDecide and dedicate 174\u003c\/p\u003e \u003cp\u003eMake decisions with a specific impact in mind 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Metrics and Measures 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLiving with Uncertainty 180\u003c\/p\u003e \u003cp\u003eMaking the Decision 182\u003c\/p\u003e \u003cp\u003eSeeing How Much a Decision Is Worth 185\u003c\/p\u003e \u003cp\u003eMatching the Metrics to the Measure 187\u003c\/p\u003e \u003cp\u003eLeaning into KPIs 188\u003c\/p\u003e \u003cp\u003eTapping into change data 191\u003c\/p\u003e \u003cp\u003eTesting AI 193\u003c\/p\u003e \u003cp\u003eDeciding When to Weigh the Decision and When to Weigh the Impact 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Proposing A New Directive 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: The Role of DI in the Idea Economy 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTurning Decisions into Ideas 200\u003c\/p\u003e \u003cp\u003eRepeating previous successes 201\u003c\/p\u003e \u003cp\u003ePredicting new successes 202\u003c\/p\u003e \u003cp\u003eWeighing the value of repeating successes versus creating new successes 202\u003c\/p\u003e \u003cp\u003eLeveraging AI to find more idea patterns 203\u003c\/p\u003e \u003cp\u003eDisruption Is the Point 205\u003c\/p\u003e \u003cp\u003eCreative problem-solving is the new competitive edge 205\u003c\/p\u003e \u003cp\u003eBending the company culture 207\u003c\/p\u003e \u003cp\u003eCompeting in the Moment 207\u003c\/p\u003e \u003cp\u003eChanging Winds and Changing Business Models 209\u003c\/p\u003e \u003cp\u003eCounting Wins in Terms of Impacts 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Seeing How Decision Intelligence Changes Industries and Markets 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFacing the What-If Challenge 214\u003c\/p\u003e \u003cp\u003eWhat-if analysis in scenarios in Excel 216\u003c\/p\u003e \u003cp\u003eWhat-if analysis using a Data Tables feature 217\u003c\/p\u003e \u003cp\u003eWhat-if analysis using a Goal Seek feature 218\u003c\/p\u003e \u003cp\u003eLearning Lessons from the Pandemic 220\u003c\/p\u003e \u003cp\u003eRefusing to make decisions in a vacuum 221\u003c\/p\u003e \u003cp\u003eLiving with toilet paper shortages and supply chain woes 222\u003c\/p\u003e \u003cp\u003eRevamping businesses overnight 224\u003c\/p\u003e \u003cp\u003eSeeing how decisions impact more than the Land of Now 226\u003c\/p\u003e \u003cp\u003eRebuilding at the Speed of Disruption 228\u003c\/p\u003e \u003cp\u003eRedefining Industries 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Trickle-Down and Streaming-Up Decisioning 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Who, What, Where, and Why of Decision-Making 232\u003c\/p\u003e \u003cp\u003eTrickling Down Your Upstream Decisions 234\u003c\/p\u003e \u003cp\u003eLooking at Streaming Decision-Making Models 236\u003c\/p\u003e \u003cp\u003eMaking Downstream Decisions 238\u003c\/p\u003e \u003cp\u003eThinking in Systems 240\u003c\/p\u003e \u003cp\u003eTaking Advantage of Systems Tools 241\u003c\/p\u003e \u003cp\u003eConforming and Creating at the Same Time 244\u003c\/p\u003e \u003cp\u003eDirecting Your Business Impacts to a Common Goal 245\u003c\/p\u003e \u003cp\u003eDealing with Decision Singularities 246\u003c\/p\u003e \u003cp\u003eRevisiting the Inverted V 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Career Makers and Deal-Breakers 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTaking the Machine’s Advice 252\u003c\/p\u003e \u003cp\u003eAdding Your Own Take 255\u003c\/p\u003e \u003cp\u003eMastering your decision intelligence superpowers 257\u003c\/p\u003e \u003cp\u003eEnsuring that you have great data sidekicks 257\u003c\/p\u003e \u003cp\u003eThe New Influencers: Decision Masters 259\u003c\/p\u003e \u003cp\u003ePreventing Wrong Influences from Affecting Decisions 262\u003c\/p\u003e \u003cp\u003eBad influences in AI and analytics 262\u003c\/p\u003e \u003cp\u003eThe blame game 265\u003c\/p\u003e \u003cp\u003eUgly politics and happy influencers 266\u003c\/p\u003e \u003cp\u003eRisk Factors in Decision Intelligence 268\u003c\/p\u003e \u003cp\u003eDI and Hyperautomation 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: The Part of Tens 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Ten Steps to Setting Up a Smart Decision 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCheck Your Data Source 275\u003c\/p\u003e \u003cp\u003eTrack Your Data Lineage 276\u003c\/p\u003e \u003cp\u003eKnow Your Tools 277\u003c\/p\u003e \u003cp\u003eUse Automated Visualizations 278\u003c\/p\u003e \u003cp\u003eImpact = Decision 279\u003c\/p\u003e \u003cp\u003eDo Reality Checks 280\u003c\/p\u003e \u003cp\u003eLimit Your Assumptions 280\u003c\/p\u003e \u003cp\u003eThink Like a Science Teacher 281\u003c\/p\u003e \u003cp\u003eSolve for Missing Data 282\u003c\/p\u003e \u003cp\u003ePartial versus incomplete data 282\u003c\/p\u003e \u003cp\u003eClues and missing answers 282\u003c\/p\u003e \u003cp\u003eTake Two Perspectives and Call Me in the Morning 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Bias In, Bias Out (and Other Pitfalls) 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Pitfalls Overview 285\u003c\/p\u003e \u003cp\u003eRelying on Racist Algorithms 286\u003c\/p\u003e \u003cp\u003eFollowing a Flawed Model for Repeat Offenders 287\u003c\/p\u003e \u003cp\u003eUsing A Sexist Hiring Algorithm 287\u003c\/p\u003e \u003cp\u003eRedlining Loans 287\u003c\/p\u003e \u003cp\u003eLeaning on Irrelevant Information 288\u003c\/p\u003e \u003cp\u003eFalling Victim to Framing Foibles 288\u003c\/p\u003e \u003cp\u003eBeing Overconfident 288\u003c\/p\u003e \u003cp\u003eLulled by Percentages 289\u003c\/p\u003e \u003cp\u003eDismissing with Prejudice 289\u003c\/p\u003e \u003cp\u003eIndex 291 \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePam Baker\u003c\/b\u003eis 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 \u003ci\u003eData Divination – Big Data Strategies\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eBundle human intelligence with machine intelligence\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAre you looking for a way to use your company’s data to inform—but not overwhelm—your decisions? This book offers insight into how to combine the best of human and machine intelligence to make rock-solid decisions based on the best available data and talent. Use every available resource, including human ones, to find new solutions to old problems. Learn how to use data as it was meant to be used: as a means to an end, and not an end in itself. Discover how to use data as a guide, instead of following it blindly, with this invaluable book. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside…\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\u003cb\u003e\u003cli\u003eIdentify relevant data patterns\u003c\/li\u003e \u003cli\u003ePut outcomes—not data—first\u003c\/li\u003e \u003cli\u003eCraft the perfect question\u003c\/li\u003e \u003cli\u003eDesign a path forward\u003c\/li\u003e \u003cli\u003eBuild your decision toolbox\u003c\/li\u003e \u003cli\u003eUse, but verify, AI\u003c\/li\u003e \u003cli\u003eMake decisions at scale\u003c\/li\u003e \u003cli\u003eLearn why AI projects fail\u003c\/li\u003e\u003c\/b\u003e\u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989030289637,"sku":"NP9781119824848","price":34.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119824848.jpg?v=1761782508","url":"https:\/\/k12savings.com\/products\/decision-intelligence-for-dummies-isbn-9781119824848","provider":"K12savings","version":"1.0","type":"link"}