Analytics the Right Way - Wilson, Tim
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Présentation Analytics The Right Way de Wilson, Tim Format Broché
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Résumé : Acknowledgments xiii About the Authors xvii Chapter 1 Is This Book Right for You? 1? The Digital Age = The Data Age 3? What You Will Learn in This Book 6? Will This Book Deliver Value? 7? Chapter 2 How We Got Here 9? Misconceptions About Data Hurt Our Ability to Draw Insights 11? Misconception 1: With Enough Data, Uncertainty Can Be Eliminated 12? Having More Data Doesn't Mean You Have the Right Data 13? Even with an Immense Amount of Data, You Cannot Eliminate Uncertainty 16? Data Can Cost More Than the Benefit You Get from It 18? It Is Impossible to Collect and Use All of the Data 18? Misconception 2: Data Must Be Comprehensive to Be Useful 19? Small Data Can Be Just As Effective As, If Not More Effective Than, Big Data 20? Misconception 3: Data Are Inherently Objective and Unbiased 21? In Private, Data Always Bend to the User's Will 23? Even When You Don't Want the Data to Be Biased, They Are 24? Misconception 4: Democratizing Access to Data Makes an Organization Data-Driven 26? Conclusion 28? Chapter 3 Making Decisions with Data: Causality and Uncertainty 29? Life and Business in a Nutshell: Making Decisions Under Uncertainty 30? What's in a Good Decision? 32? Minimizing Regret in Decisions 33? The Potential Outcomes Framework 34? What's a Counterfactual? 34? Uncertainty and Causality 36? Potential Outcomes in Summary 42? So, What Now? 43? Chapter 4 A Structured Approach to Using Data 45? Chapter 5 Making Decisions Through Performance Measurement 53? A Simple Idea That Trips Up Organizations 54? What Are Your KPIs? Is a Terrible Question 58? Two Magic Questions 60? A KPI Without a Target Is Just a Metric 68? Setting Targets with the Backs of Some Napkins 72? Setting Targets by Bracketing the Possibilities 74? Setting Targets by Just Picking a Number 78? Dashboards as a Performance Measurement Tool 80? Summary 82? Chapter 6 Making Decisions Through Hypothesis Validation 85? Without Hypotheses, We See a Drought of Actionable Insights 88? Breaking the Lamentable Cycle and Creating Actionable Insight 89? Articulating and Validating Hypotheses: A Framework 91? Articulating Hypotheses That Can Be Validated 92? The Idea: We believe [some idea] 95? The Theory: ...because [some evidence or rationale]... 96? The Action: If we are right, we will... 98? Exercise: Formulate a Hypothesis 101? Capturing Hypotheses in a Hypothesis Library 101? Just Write It Down: Ideating a Hypothesis vs. Inventorying a Hypothesis 104? An Abundance of Hypotheses 105? Hypothesis Prioritization 106? Alignment to Business Goals 107? The Ongoing Process of Hypothesis Validation 108? Tracking Hypotheses Through Their Life Cycle 109? Summary 110? Chapter 7 Hypothesis Validation with New Evidence 113? Hypotheses Already Have Validating Information in Them 115? 100% Certainty Is Never Achievable 116? Methodologies for Validating Hypotheses 118? Anecdotal Evidence 119? Strengths of Anecdotal Evidence 120? Weaknesses of Anecdotal Evidence 121? Descriptive Evidence 122? Strengths of Descriptive Evidence 123? Weaknesses of Descriptive Evidence 124? Scientific Evidence 128? Strengths of Scientific Evidence 129? Weaknesses of Scientific Evidence 135? Matching the Method to the Costs and Importance of the Hypothesis 137? Summary 139? Sommaire:
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