Analytics the Right Way - A Business Leader's Guide to Putting Data to Productive Use

Expert guide to productively and profitably put your organization's data to use Providing both underlying theory and practical solutions, Analytics the Right Way is a thorough exploration of how to create tangible business value with data. Written by Tim Wilson, seasoned industry professional with more than 20 years of proven experience, and Dr. Joe Sutherland, renowned professor and researcher who served in The White House during the Obama administration, this book shows readers how to find the answers to common data and analytics frustrations and anxieties, including lack of actionable insights, ineffective recommendations, difficulties scaling, and unclear ROI. Written in accessible language with helpful illustrations to elucidate key concepts included throughout, this book explores topics including:

• Economic, institutional, and psychological factors that inadvertently reinforce misconceptions of data and analytics and the misguided allocation of resources and efforts
• The potential outcomes framework, a mental model through which to view decision making and the possible versions of the world that may emerge as a result of the decision you make
• Three fundamentally different ways that data can be used within an organization to drive value: measuring performance, validating hypotheses, and enabling operational processes
• Ways that digitally enabled, profitable, AI-first enterprises are distinguished by the leader's ability to elegantly weave the three uses of data together

Analytics the Right Way is an essential resource for business leaders, entrepreneurs, data and analytics professionals, executives, and all professionals seeking to cut through the noise and start putting data to use in a way that is productive, profitable, and even fun.

List of contents

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 
Chapter 8 Descriptive Evidence: Pitfalls and Solutions 141 
Historical Data Analysis Gone Wrong 142 
Descriptive Analyses Done Right 146 
Unit of Analysis 146 
Independent and Dependent Variables 149 
Omitted Variables Bias 151 
Time Is Uniquely Complicating 153 
Describing Data vs. Making Inferences 154 
Quantifying Uncertainty 156 
Summary 163 
Chapter 9 Pitfalls and Solutions for Scientific Evidence 165 
Making Statistical Inferences 166 
Detecting and Solving Problems with Selection Bias 168 
Define the Population 168 
Compare the Population to the Sample 168 
Determine What Differences Are Unexpectedly Different 169 
Random and Nonrandom Selection Bias 169 
The Scientist's Mind: It's the Thought That Counts! 170 
Making Causal Inferences 171 
Detecting and Solving Problems with Confounding Bias 172 
Create a List of Things That Could Affect the Concept We're Analyzing 173 
Draw Causal Arrows 173 
Look for Confounding "Triangles" Between the Circles and the Box 174 
Solving for Confounding in the Past and the Future 175 
Controlled Experimentation 176 
The Gold Standard of Causation: Controlled Experimentation 177 
The Fundamental Requirements for a Controlled Experiment 179 
Some Cautionary Notes About Controlled Experimentation 184 
Summary 185 
Chapter 10 Operational Enablement Using Data 187 
The Balancing Act: Value and Efficiency 189 
The Factory: How to Think About Data for Operational Enablement 191 
Trade Secrets: The Original Business Logic 192 
How Hypothesis Validation Develops Trade Secrets and Business Logic 193 
Operational Enablement and Data in Defined Processes 194 
Output Complexity and Automation Costs 196 
Machine Learning and AI 199 
Machine Learning: Discovering Mechanisms Without Manual Intervention 199 
Simple Machine-learned Rulesets 200 
Complex Machine-learned Rulesets 202 
AI: Executing Mechanisms Autonomously 203 
Judgment: Deciding to Act on a Prediction 204 
Degrees of Delegation: In-the-loop, On-the-loop, and Out-of-the-loop 204 
Why Machine Learning Is Important for Operational Enablement 209 
Chapter 11 Bringing It All Together 211 
The Interconnected Nature of the Framework 212 
Performance Measurement Triggering Hypothesis Validation 212 
Level 1: Manager Knowledge 213 
Level 2: Peer Knowledge 214 
Level 3: Not Readily Apparent 215 
Hypothesis Validation Triggering Performance Measurement 216 
Did the Corrective Action Work? 216 
"Performance Measurement" as a Validation Technique 216 
Operational Enablement Resulting from Hypothesis Validation 220 
Operational Enablement Needs Performance Measurement 222 
A Call Center Example 223 
Enabling Good Ideas to Thrive: Effective Communication 225 
Alright, Alright: You Do Need Technology 226 
What Technology Does Well 227 
What Technology Doesn't Do Well 228 
Final Thoughts on Decision-making 230 
Index 233

Product details

• Authors: Joe Sutherland, Tim Wilson, Tim Sutherland Wilson
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 23.01.2025
• EAN: 9781394264490
• ISBN: 978-1-394-26449-0
• No. of pages: 256
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Social sciences, law, business > Business; Data Mining, BUSINESS & ECONOMICS / Business Communication / General, Mathematics, computer science, Business communication and presentation, COMPUTERS / Data Science / Data Analytics, COMPUTERS / Data Science / General, Data capture and analysis