{"product_id":"analytics-the-right-way-isbn-9781394264490","title":"Analytics the Right Way","description":"\u003cp\u003e\u003cb\u003eCLEAR AND CONCISE TECHNIQUES FOR USING ANALYTICS TO DELIVER BUSINESS IMPACT AT ANY ORGANIZATION\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eOrganizations have more data at their fingertips than ever, and their ability to put that data to productive use \u003ci\u003eshould\u003c\/i\u003e be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of “actionable insights” often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, “So what?” \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAnalytics the Right Way\u003c\/i\u003e is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The result: a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization’s use of data and analytics. \u003c\/p\u003e\u003cp\u003eThe book uses a combination of real-world examples from the authors’ direct experiences—working inside organizations, as external consultants, and as educators—mixed with vivid hypotheticals and illustrations—little green aliens, petty criminals with an affinity for ice cream, skydiving without parachutes, and more—to empower the reader to put foundational analytical and statistical concepts to effective use in a business context. \u003c\/p\u003e\u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eAbout the Authors xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Is This Book Right for You? 1\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eThe Digital Age = The Data Age 3 \u003c\/p\u003e \u003cp\u003eWhat You Will Learn in This Book 6 \u003c\/p\u003e \u003cp\u003eWill This Book Deliver Value? 7 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 How We Got Here 9\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eMisconceptions About Data Hurt Our Ability to Draw Insights 11 \u003c\/p\u003e \u003cp\u003eMisconception 1: With Enough Data, Uncertainty Can Be Eliminated 12 \u003c\/p\u003e \u003cp\u003eHaving More Data Doesn’t Mean You Have the Right Data 13 \u003c\/p\u003e \u003cp\u003eEven with an Immense Amount of Data, You Cannot Eliminate Uncertainty 16 \u003c\/p\u003e \u003cp\u003eData Can Cost More Than the Benefit You Get from It 18 \u003c\/p\u003e \u003cp\u003eIt Is Impossible to Collect and Use “All” of the Data 18 \u003c\/p\u003e \u003cp\u003eMisconception 2: Data Must Be Comprehensive to Be Useful 19 \u003c\/p\u003e \u003cp\u003e“Small Data” Can Be Just As Effective As, If Not More Effective Than, “Big Data” 20 \u003c\/p\u003e \u003cp\u003eMisconception 3: Data Are Inherently Objective and Unbiased 21 \u003c\/p\u003e \u003cp\u003eIn Private, Data Always Bend to the User’s Will 23 \u003c\/p\u003e \u003cp\u003eEven When You Don’t Want the Data to Be Biased, They Are 24 \u003c\/p\u003e \u003cp\u003eMisconception 4: Democratizing Access to Data Makes an Organization Data-Driven 26 \u003c\/p\u003e \u003cp\u003eConclusion 28 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Making Decisions with Data: Causality and Uncertainty 29\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eLife and Business in a Nutshell: Making Decisions Under Uncertainty 30 \u003c\/p\u003e \u003cp\u003eWhat’s in a Good Decision? 32 \u003c\/p\u003e \u003cp\u003eMinimizing Regret in Decisions 33 \u003c\/p\u003e \u003cp\u003eThe Potential Outcomes Framework 34 \u003c\/p\u003e \u003cp\u003eWhat’s a Counterfactual? 34 \u003c\/p\u003e \u003cp\u003eUncertainty and Causality 36 \u003c\/p\u003e \u003cp\u003ePotential Outcomes in Summary 42 \u003c\/p\u003e \u003cp\u003eSo, What Now? 43 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 A Structured Approach to Using Data 45\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Making Decisions Through Performance Measurement 53\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eA Simple Idea That Trips Up Organizations 54 \u003c\/p\u003e \u003cp\u003e“What Are Your KPIs?” Is a Terrible Question 58 \u003c\/p\u003e \u003cp\u003eTwo Magic Questions 60 \u003c\/p\u003e \u003cp\u003eA KPI Without a Target Is Just a Metric 68 \u003c\/p\u003e \u003cp\u003eSetting Targets with the Backs of Some Napkins 72 \u003c\/p\u003e \u003cp\u003eSetting Targets by Bracketing the Possibilities 74 \u003c\/p\u003e \u003cp\u003eSetting Targets by Just Picking a Number 78 \u003c\/p\u003e \u003cp\u003eDashboards as a Performance Measurement Tool 80 \u003c\/p\u003e \u003cp\u003eSummary 82 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Making Decisions Through Hypothesis Validation 85\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eWithout Hypotheses, We See a Drought of Actionable Insights 88 \u003c\/p\u003e \u003cp\u003eBreaking the Lamentable Cycle and Creating Actionable Insight 89 \u003c\/p\u003e \u003cp\u003eArticulating and Validating Hypotheses: A Framework 91 \u003c\/p\u003e \u003cp\u003eArticulating Hypotheses That Can Be Validated 92 \u003c\/p\u003e \u003cp\u003eThe Idea: We believe [some idea] 95 \u003c\/p\u003e \u003cp\u003eThe Theory: …because [some evidence or rationale]… 96 \u003c\/p\u003e \u003cp\u003eThe Action: If we are right, we will… 98 \u003c\/p\u003e \u003cp\u003eExercise: Formulate a Hypothesis 101 \u003c\/p\u003e \u003cp\u003eCapturing Hypotheses in a Hypothesis Library 101 \u003c\/p\u003e \u003cp\u003eJust Write It Down: Ideating a Hypothesis vs. Inventorying a Hypothesis 104 \u003c\/p\u003e \u003cp\u003eAn Abundance of Hypotheses 105 \u003c\/p\u003e \u003cp\u003eHypothesis Prioritization 106 \u003c\/p\u003e \u003cp\u003eAlignment to Business Goals 107 \u003c\/p\u003e \u003cp\u003eThe Ongoing Process of Hypothesis Validation 108 \u003c\/p\u003e \u003cp\u003eTracking Hypotheses Through Their Life Cycle 109 \u003c\/p\u003e \u003cp\u003eSummary 110 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Hypothesis Validation with New Evidence 113\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eHypotheses Already Have Validating Information in Them 115 \u003c\/p\u003e \u003cp\u003e100% Certainty Is Never Achievable 116 \u003c\/p\u003e \u003cp\u003eMethodologies for Validating Hypotheses 118 \u003c\/p\u003e \u003cp\u003eAnecdotal Evidence 119 \u003c\/p\u003e \u003cp\u003eStrengths of Anecdotal Evidence 120 \u003c\/p\u003e \u003cp\u003eWeaknesses of Anecdotal Evidence 121 \u003c\/p\u003e \u003cp\u003eDescriptive Evidence 122 \u003c\/p\u003e \u003cp\u003eStrengths of Descriptive Evidence 123 \u003c\/p\u003e \u003cp\u003eWeaknesses of Descriptive Evidence 124 \u003c\/p\u003e \u003cp\u003eScientific Evidence 128 \u003c\/p\u003e \u003cp\u003eStrengths of Scientific Evidence 129 \u003c\/p\u003e \u003cp\u003eWeaknesses of Scientific Evidence 135 \u003c\/p\u003e \u003cp\u003eMatching the Method to the Costs and Importance of the Hypothesis 137 \u003c\/p\u003e \u003cp\u003eSummary 139 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Descriptive Evidence: Pitfalls and Solutions 141\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eHistorical Data Analysis Gone Wrong 142 \u003c\/p\u003e \u003cp\u003eDescriptive Analyses Done Right 146 \u003c\/p\u003e \u003cp\u003eUnit of Analysis 146 \u003c\/p\u003e \u003cp\u003eIndependent and Dependent Variables 149 \u003c\/p\u003e \u003cp\u003eOmitted Variables Bias 151 \u003c\/p\u003e \u003cp\u003eTime Is Uniquely Complicating 153 \u003c\/p\u003e \u003cp\u003eDescribing Data vs. Making Inferences 154 \u003c\/p\u003e \u003cp\u003eQuantifying Uncertainty 156 \u003c\/p\u003e \u003cp\u003eSummary 163 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Pitfalls and Solutions for Scientific Evidence 165\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eMaking Statistical Inferences 166 \u003c\/p\u003e \u003cp\u003eDetecting and Solving Problems with Selection Bias 168 \u003c\/p\u003e \u003cp\u003eDefine the Population 168 \u003c\/p\u003e \u003cp\u003eCompare the Population to the Sample 168 \u003c\/p\u003e \u003cp\u003eDetermine What Differences Are Unexpectedly Different 169 \u003c\/p\u003e \u003cp\u003eRandom and Nonrandom Selection Bias 169 \u003c\/p\u003e \u003cp\u003eThe Scientist’s Mind: It’s the Thought That Counts! 170 \u003c\/p\u003e \u003cp\u003eMaking Causal Inferences 171 \u003c\/p\u003e \u003cp\u003eDetecting and Solving Problems with Confounding Bias 172 \u003c\/p\u003e \u003cp\u003eCreate a List of Things That Could Affect the Concept We’re Analyzing 173 \u003c\/p\u003e \u003cp\u003eDraw Causal Arrows 173 \u003c\/p\u003e \u003cp\u003eLook for Confounding “Triangles” Between the Circles and the Box 174 \u003c\/p\u003e \u003cp\u003eSolving for Confounding in the Past and the Future 175 \u003c\/p\u003e \u003cp\u003eControlled Experimentation 176 \u003c\/p\u003e \u003cp\u003eThe Gold Standard of Causation: Controlled Experimentation 177 \u003c\/p\u003e \u003cp\u003eThe Fundamental Requirements for a Controlled Experiment 179 \u003c\/p\u003e \u003cp\u003eSome Cautionary Notes About Controlled Experimentation 184 \u003c\/p\u003e \u003cp\u003eSummary 185 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Operational Enablement Using Data 187\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eThe Balancing Act: Value and Efficiency 189 \u003c\/p\u003e \u003cp\u003eThe Factory: How to Think About Data for Operational Enablement 191 \u003c\/p\u003e \u003cp\u003eTrade Secrets: The Original Business Logic 192 \u003c\/p\u003e \u003cp\u003eHow Hypothesis Validation Develops Trade Secrets and Business Logic 193 \u003c\/p\u003e \u003cp\u003eOperational Enablement and Data in Defined Processes 194 \u003c\/p\u003e \u003cp\u003eOutput Complexity and Automation Costs 196 \u003c\/p\u003e \u003cp\u003eMachine Learning and AI 199 \u003c\/p\u003e \u003cp\u003eMachine Learning: Discovering Mechanisms Without Manual Intervention 199 \u003c\/p\u003e \u003cp\u003eSimple Machine-learned Rulesets 200 \u003c\/p\u003e \u003cp\u003eComplex Machine-learned Rulesets 202 \u003c\/p\u003e \u003cp\u003eAI: Executing Mechanisms Autonomously 203 \u003c\/p\u003e \u003cp\u003eJudgment: Deciding to Act on a Prediction 204 \u003c\/p\u003e \u003cp\u003eDegrees of Delegation: In-the-loop, On-the-loop, and Out-of-the-loop 204 \u003c\/p\u003e \u003cp\u003eWhy Machine Learning Is Important for Operational Enablement 209 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Bringing It All Together 211\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eThe Interconnected Nature of the Framework 212 \u003c\/p\u003e \u003cp\u003ePerformance Measurement Triggering Hypothesis Validation 212 \u003c\/p\u003e \u003cp\u003eLevel 1: Manager Knowledge 213 \u003c\/p\u003e \u003cp\u003eLevel 2: Peer Knowledge 214 \u003c\/p\u003e \u003cp\u003eLevel 3: Not Readily Apparent 215 \u003c\/p\u003e \u003cp\u003eHypothesis Validation Triggering Performance Measurement 216 \u003c\/p\u003e \u003cp\u003eDid the Corrective Action Work? 216 \u003c\/p\u003e \u003cp\u003e“Performance Measurement” as a Validation Technique 216 \u003c\/p\u003e \u003cp\u003eOperational Enablement Resulting from Hypothesis Validation 220 \u003c\/p\u003e \u003cp\u003eOperational Enablement Needs Performance Measurement 222 \u003c\/p\u003e \u003cp\u003eA Call Center Example 223 \u003c\/p\u003e \u003cp\u003eEnabling Good Ideas to Thrive: Effective Communication 225 \u003c\/p\u003e \u003cp\u003eAlright, Alright: You Do Need Technology 226 \u003c\/p\u003e \u003cp\u003eWhat Technology Does Well 227 \u003c\/p\u003e \u003cp\u003eWhat Technology Doesn’t Do Well 228 \u003c\/p\u003e \u003cp\u003eFinal Thoughts on Decision-making 230 \u003c\/p\u003e \u003cp\u003eIndex 233\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eTIM WILSON\u003c\/b\u003e has been an analytics practitioner since 2001, working in roles from business intelligence at high-tech B2B companies, to analytics leadership at marketing agencies, to consulting with Fortune Global 500 companies to improve their analytics investments. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDR. JOE SUTHERLAND\u003c\/b\u003e has worked as an executive, public servant, and educator for the Dow Jones 30, The White House, and our nation’s top universities. His firm, J.L. Sutherland \u0026amp; Associates, has attracted clients such as Box, Cisco, Canva, The Conference Board, and Fulcrum Equity Partners. He founded the Center for AI Learning at Emory University, which focuses on AI literacy and integration for the general public.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCLEAR AND CONCISE TECHNIQUES FOR USING ANALYTICS TO DELIVER BUSINESS IMPACT AT ANY ORGANIZATION\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eOrganizations have more data at their fingertips than ever, and their ability to put that data to productive use \u003ci\u003eshould\u003c\/i\u003e be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of “actionable insights” often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, “So what?” \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAnalytics the Right Way\u003c\/i\u003e is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The result: a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization’s use of data and analytics. \u003c\/p\u003e\u003cp\u003eThe book uses a combination of real-world examples from the authors’ direct experiences—working inside organizations, as external consultants, and as educators—mixed with vivid hypotheticals and illustrations—little green aliens, petty criminals with an affinity for ice cream, skydiving without parachutes, and more—to empower the reader to put foundational analytical and statistical concepts to effective use in a business context.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988736458981,"sku":"NP9781394264490","price":35.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394264490.jpg?v=1761781388","url":"https:\/\/k12savings.com\/es\/products\/analytics-the-right-way-isbn-9781394264490","provider":"K12savings","version":"1.0","type":"link"}