Details

Decision Intelligence For Dummies

1. Aufl.

von: Pam Baker
22,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	31.12.2021
ISBN/EAN:	9781119824862
Sprache:	englisch
Anzahl Seiten:	320

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Beschreibungen

Titelbeschreibung

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: <ul> <li>Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries </li> <li>Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data </li> <li>Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company </li> </ul> 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.

Inhaltsverzeichnis

Introduction 1 About This Book 2 Conventions Used in This Book 3 Foolish Assumptions 3 What You Don’t Have to Read 4 How This Book Is Organized 5 Part 1: Getting Started with Decision Intelligence 5 Part 2: Reaching the Best Possible Decision 5 Part 3: Establishing Reality Checks 5 Part 4: Proposing a New Directive 6 Part 5: The Part of Tens 6 Icons Used in This Book 6 Beyond the Book 7 Where to Go from Here 7 Part 1: Getting Started with Decision Intelligence 9 Chapter 1: Short Takes on Decision Intelligence 11 The Tale of Two Decision Trails 12 Pointing out the way 13 Making a decision 16 Deputizing AI as Your Faithful Sidekick 18 Seeing How Decision Intelligence Looks on Paper 20 Tracking the Inverted V 21 Estimating How Much Decision Intelligence Will Cost You 22 Chapter 2: Mining Data versus Minding the Answer 25 Knowledge Is Power — Data Is Just Information 26 Experiencing the epiphany 26 Embracing the new, not-so-new idea 28 Avoiding thought boxes and data query borders 29 Reinventing Actionable Outcomes 32 Living with the fact that we have answers and still don’t know what to do 32 Going where humans fear to tread on data 34 Ushering in The Great Revival: Institutional knowledge and human expertise 36 Chapter 3: Cryptic Patterns and Wild Guesses 39 Machines Make Human Mistakes, Too 40 Seeing the Trouble Math Makes 42 The limits of math-only approaches 42 The right math for the wrong question 43 Why data scientists and statisticians often make bad question-makers 46 Identifying Patterns and Missing the Big Picture 48 All the helicopters are broken 48 MIA: Chunks of crucial but hard-to-get real-world data 49 Evaluating man-versus-machine in decision-making 51 Chapter 4: The Inverted V Approach 53 Putting Data First Is the Wrong Move 54 What’s a decision, anyway? 55 Any road will take you there 56 The great rethink when it comes to making decisions at scale 57 Applying the Upside-Down V: The Path to the Output and Back Again 59 Evaluating Your Inverted V Revelations 60 Having Your Inverted V Lightbulb Moment 61 Recognizing Why Things Go Wrong 63 Aiming for too broad an outcome 63 Mimicking data outcomes 64 Failing to consider other decision sciences 64 Mistaking gut instincts for decision science 64 Failing to change the culture 65 Part 2: Reaching the Best Possible Decision 67 Chapter 5: Shaping a Decision into a Query 69 Defining Smart versus Intelligent 70 Discovering That Business Intelligence Is Not Decision Intelligence 71 Discovering the Value of Context and Nuance 72 Defining the Action You Seek 73 Setting Up the Decision 74 Decision science versus data science 75 Framing your decision 77 Heuristics and other leaps of faith 78 Chapter 6: Mapping a Path Forward 81 Putting Data Last 82 Recognizing when you can (and should) skip the data entirely 83 Leaning on CRISP-DM 84 Using the result you seek to identify the data you need 85 Digital decisioning and decision intelligence 85 Don’t store all your data — know when to throw it out 87 Adding More Humans to the Equation 88 The shift in thinking at the business line level 90 How decision intelligence puts executives and ordinary humans back in charge 92 Limiting Actions to What Your Company Will Actually Do 94 Looking at budgets versus the company will 95 Setting company culture against company resources 98 Using long-term decisioning to craft short-term returns 99 Chapter 7: Your DI Toolbox 101 Decision Intelligence Is a Rethink, Not a Data Science Redo 102 Taking Stock of What You Already Have 103 The tool overview 104 Working with BI apps 105 Accessing cloud tools 106 Taking inventory and finding the gaps 107 Adding Other Tools to the Mix 108 Decision modeling software 109 Business rule management systems 110 Machine learning and model stores 110 Data platforms 112 Data visualization tools 112 Option round-up 113 Taking a Look at What Your Computing Stack Should Look Like Now 113 Part 3: Establishing Reality Checks 115 Chapter 8: Taking a Bow: Goodbye, Data Scientists — Hello, Data Strategists 117 Making Changes in Organizational Roles 118 Leveraging your current data scientist roles 120 Realigning your existing data teams 121 Looking at Emerging DI Jobs 122 Hiring data strategists versus hiring decision strategists 125 Onboarding mechanics and pot washers 127 The Chief Data Officer’s Fate 127 Freeing Executives to Lead Again 129 Chapter 9: Trusting AI and Tackling Scary Things 131 Discovering the Truth about AI 132 Thinking in AI 133 Thinking in human 136 Letting go of your ego 137 Seeing Whether You Can Trust AI 138 Finding out why AI is hard to test and harder to understand 140 Hearing AI’s confession 142 Two AIs Walk into a Bar 144 Doing the right math but asking the wrong question 146 Dealing with conflicting outputs 147 Battling AIs 148 Chapter 10: Meddling Data and Mindful Humans 151 Engaging with Decision Theory 152 Working with your gut instincts 153 Looking at the role of the social sciences 155 Examining the role of the managerial sciences 156 The Role of Data Science in Decision Intelligence 157 Fitting data science to decision intelligence 157 Reimagining the rules 159 Expanding the notion of a data source 161 Where There’s a Will, There’s a Way 163 Chapter 11: Decisions at Scale 165 Plugging and Unplugging AI into Automation 167 Dealing with Model Drifts and Bad Calls 168 Reining in AutoML 170 Seeing the Value of ModelOps 173 Bracing for Impact 174 Decide and dedicate 174 Make decisions with a specific impact in mind 175 Chapter 12: Metrics and Measures 179 Living with Uncertainty 180 Making the Decision 182 Seeing How Much a Decision Is Worth 185 Matching the Metrics to the Measure 187 Leaning into KPIs 188 Tapping into change data 191 Testing AI 193 Deciding When to Weigh the Decision and When to Weigh the Impact 195 Part 4: Proposing A New Directive 197 Chapter 13: The Role of DI in the Idea Economy 199 Turning Decisions into Ideas 200 Repeating previous successes 201 Predicting new successes 202 Weighing the value of repeating successes versus creating new successes 202 Leveraging AI to find more idea patterns 203 Disruption Is the Point 205 Creative problem-solving is the new competitive edge 205 Bending the company culture 207 Competing in the Moment 207 Changing Winds and Changing Business Models 209 Counting Wins in Terms of Impacts 210 Chapter 14: Seeing How Decision Intelligence Changes Industries and Markets 213 Facing the What-If Challenge 214 What-if analysis in scenarios in Excel 216 What-if analysis using a Data Tables feature 217 What-if analysis using a Goal Seek feature 218 Learning Lessons from the Pandemic 220 Refusing to make decisions in a vacuum 221 Living with toilet paper shortages and supply chain woes 222 Revamping businesses overnight 224 Seeing how decisions impact more than the Land of Now 226 Rebuilding at the Speed of Disruption 228 Redefining Industries 230 Chapter 15: Trickle-Down and Streaming-Up Decisioning 231 Understanding the Who, What, Where, and Why of Decision-Making 232 Trickling Down Your Upstream Decisions 234 Looking at Streaming Decision-Making Models 236 Making Downstream Decisions 238 Thinking in Systems 240 Taking Advantage of Systems Tools 241 Conforming and Creating at the Same Time 244 Directing Your Business Impacts to a Common Goal 245 Dealing with Decision Singularities 246 Revisiting the Inverted V 248 Chapter 16: Career Makers and Deal-Breakers 251 Taking the Machine’s Advice 252 Adding Your Own Take 255 Mastering your decision intelligence superpowers 257 Ensuring that you have great data sidekicks 257 The New Influencers: Decision Masters 259 Preventing Wrong Influences from Affecting Decisions 262 Bad influences in AI and analytics 262 The blame game 265 Ugly politics and happy influencers 266 Risk Factors in Decision Intelligence 268 DI and Hyperautomation 270 Part 5: The Part of Tens 273 Chapter 17: Ten Steps to Setting Up a Smart Decision 275 Check Your Data Source 275 Track Your Data Lineage 276 Know Your Tools 277 Use Automated Visualizations 278 Impact = Decision 279 Do Reality Checks 280 Limit Your Assumptions 280 Think Like a Science Teacher 281 Solve for Missing Data 282 Partial versus incomplete data 282 Clues and missing answers 282 Take Two Perspectives and Call Me in the Morning 283 Chapter 18: Bias In, Bias Out (and Other Pitfalls) 285 A Pitfalls Overview 285 Relying on Racist Algorithms 286 Following a Flawed Model for Repeat Offenders 287 Using A Sexist Hiring Algorithm 287 Redlining Loans 287 Leaning on Irrelevant Information 288 Falling Victim to Framing Foibles 288 Being Overconfident 288 Lulled by Percentages 289 Dismissing with Prejudice 289 Index 291

Autorenportrait

Pam Bakeris 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 Data Divination – Big Data Strategies.

Back cover copy

Bundle human intelligence with machine intelligence Are 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. Inside… <ul><li>Identify relevant data patterns</li> <li>Put outcomes—not data—first</li> <li>Craft the perfect question</li> <li>Design a path forward</li> <li>Build your decision toolbox</li> <li>Use, but verify, AI</li> <li>Make decisions at scale</li> <li>Learn why AI projects fail</li></ul>