Table Of ContentAnalytical Skills
for AI & Data Science
Building Skills for an AI-Driven Enterprise
Daniel Vaughan
Analytical Skills for AI
and Data Science
Building Skills for an AI-Driven Enterprise
Daniel Vaughan
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Analytical Skills for AI and Data Science
by Daniel Vaughan
Copyright © 2020 Daniel Vaughan. All rights reserved.
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978-1-492-06094-9
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Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1. Analytical Thinking and the AI-Driven Enterprise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is AI? 2
Why Current AI Won’t Deliver on Its Promises 3
How Did We Get Here? 4
The Data Revolution 5
A Tale of Unrealized Expectations 8
Analytical Skills for the Modern AI-Driven Enterprise 9
Key Takeways 10
Further Reading 10
2. Intro to Analytical Thinking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Descriptive, Predictive, and Prescriptive Questions 13
When Predictive Analysis Is Powerful: The Case of Cancer Detection 14
Descriptive Analysis: The Case of Customer Churn 15
Business Questions and KPIs 17
KPIs to Measure the Success of a Loyalty Program 18
An Anatomy of a Decision: A Simple Decomposition 20
An Example: Why Did You Buy This Book? 22
A Primer on Causation 23
Defining Correlation and Causation 23
Some Difficulties in Estimating Causal Effects 24
Uncertainty 31
Uncertainty from Simplification 32
Uncertainty from Heterogeneity 33
Uncertainty from Social Interactions 34
Uncertainty from Ignorance 36
iii
Key Takeaways 36
Further Reading 37
3. Learning to Ask Good Business Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
From Business Objectives to Business Questions 40
Descriptive, Predictive, and Prescriptive Questions 42
Always Start with the Business Question and Work Backward 42
Further Deconstructing the Business Questions 43
Example with a Two-Sided Platform 44
Learning to Ask Business Questions: Examples from Common Use Cases 46
Lowering Churn 46
Cross-Selling: Next-Best Offer 49
CAPEX Optimization 50
Store Locations 52
Who Should I Hire? 53
Delinquency Rates 54
Stock or Inventory Optimization 56
Store Staffing 57
Key Takeaways 58
Further Reading 58
4. Actions, Levers, and Decisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Understanding What Is Actionable 62
Physical Levers 63
Human Levers 64
Why Do We Behave the Way We Do? 65
Levers from Restrictions 66
Levers That Affect Our Preferences 69
Levers That Change Your Expectations 76
Revisiting Our Use Cases 77
Customer Churn 77
Cross-Selling 77
Capital Expenditure (CAPEX) Optimization 78
Store Locations 78
Who Should I Hire? 78
Delinquency Rates 79
Stock Optimization 79
Store Staffing 79
Key Takeaways 80
Further Reading 80
iv | Table of Contents
5. From Actions to Consequences: Learning How to Simplify. . . . . . . . . . . . . . . . . . . . . . . . 83
Why Do We Need to Simplify? 84
First- and Second-Order Effects 85
Exercising Our Analytical Muscle: Welcome Fermi 86
How Many Tennis Balls Fit the Floor of This Rectangular Room? 86
How Much Would You Charge to Clean Every Window in Mexico City? 89
Fermi Problems to Make Preliminary Business Cases 91
Revisiting the Examples from Chapter 3 96
Customer Churn 96
Cross-Selling 97
CAPEX Optimization 98
Store Locations 99
Delinquency Rates 99
Stock Optimization 102
Store Staffing 103
Key Takeaways 103
Further Reading 104
6. Uncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Where Does Uncertainty Come From? 106
Quantifying Uncertainty 106
Expected Values 108
Making Decisions Without Uncertainty 112
Making Simple Decisions Under Uncertainty 114
Decisions Under Uncertainty 116
Is This the Best We Can Do? 117
But This Is a Frequentist Argument 120
Normative and Descriptive Theories of Decision-Making 121
Some Paradoxes in Decision-Making Under Uncertainty 121
The St. Petersburg Paradox 122
Risk Aversion 124
Putting it All into Practice 126
Estimating the Probabilities 127
Estimating Expected Values 131
Frequentist and Bayesian Methods 131
Revisiting Our Use Cases 132
Customer Churn 132
Cross-Selling 135
CAPEX Optimization 136
Store Locations 137
Who to Hire 138
Delinquency Rates 139
Table of Contents | v
Stock Optimization 140
Key Takeaways 140
Further Reading 141
7. Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
What Is Optimization? 146
Numerical Optimization Is Hard 151
Optimization Is Not New in Business Settings 151
Price and Revenue Optimization 151
Optimization Without Uncertainty 153
Customer Churn 153
Cross-Selling 155
CAPEX Investment 157
Optimal Staffing 163
Optimal Store Locations 169
Optimization with Uncertainty 169
Customer Churn 170
Cross-Selling 171
Optimal Staffing 171
Tricks for Solving Optimization Problems Under Uncertainty 174
Key Takeaways 177
Further Reading 178
8. Wrapping Up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Analytical Skills 179
Asking Prescriptive Questions 179
Understanding Causality 181
Simplify 182
Embracing Uncertainty 182
Tackling Optimization 184
The AI-Driven Enterprise of the Future 186
Back to AI 186
Some Final Thoughts 189
Appendix. A Brief Introduction to Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
vi | Table of Contents
Preface
Why Analytical Skills for AI?
Judging from the headlines and commentary in social media during the second half
of the 2010s, the age of artificial intelligence has finally arrived with its promises of
automation and value creation. Not too long ago, a similar promise came with the big
data revolution that started around 2005. And while it is true that some select compa‐
nies have been able to disrupt industries through AI- and data-driven business mod‐
els, many have yet to realize the promises.
There are several explanations for this lack of measurable results—all with some val‐
idity, surely—but the one put forward in this book is the general lack of analytical
skills that are complementary to these new technologies.
The central premise of the book is that value at the enterprise is created by making
decisions, not with data or predictive technologies alone. Nonetheless, we can piggy‐
back on the big data and AI revolutions and start making better choices in a system‐
atic and scalable way, by transforming our companies into modern AI- and data-
driven decision-making enterprises.
To make better decisions, we first need to ask the right questions, forcing us to move
from descriptive and predictive analyses to prescriptive courses of action. I will devote
the first few chapters to clarifying these concepts and explaining how to ask better
business questions suitable for this type of analysis. I will then delve into the anatomy
of decision-making, starting with the consequences or outcomes we want to achieve,
moving backward to the actions we can take, and discussing the problems and oppor‐
tunities created by intervening uncertainty and causality. Finally, we will learn how to
pose and solve prescriptive problems.
vii
Use Case-Driven Approach
Since my aim is to help practitioners to create value from AI and data science using
this analytical skillset, each chapter will illustrate how each skill works with the help
of a collection of use cases. These were selected because I’ve found them valuable at
work, because of their generality across industries, because students found them par‐
ticularly interesting or useful, or because they are important building blocks for more
complex problems commonly found in the industry. But in the end this choice was
subjective, and depending on your industry, they may be more or less relevant.
What This Book Isn’t
This book isn’t about artificial intelligence or machine learning. This book is about
the extra skills needed to be successful at creating value from these predictive
technologies.
I have provided an introduction to machine learning in the Appendix for the purpose
of being self-contained, but it isn’t a detailed presentation of machine learning-related
material nor was it planned as one. For that, you can consult many of the great books
out there (some are mentioned in the Further Reading section of the Appendix).
Who This Book Is For
This book is for anyone wanting to create value from machine learning. I’ve used
parts of the material with business students, data scientists, and business people alike.
The most advanced material deals with decision-making under uncertainty and opti‐
mization, so having a background in probability, statistics, or calculus will definitely
help. For readers without this background, I’ve tried to make the presentation self-
contained. On a first pass, you might just skip the technical details and focus on
developing an intuition and an understanding of the main messages for each chapter.
• If you’re a business person with no interest whatsoever in doing machine learning
yourself, this book should at least help redirect the questions you want your data
scientists to answer. Business people have great ideas, but they may have diffi‐
culty expressing what they want to more technical types. If you want to start
using AI in your own line of work, this book will help you formulate and trans‐
late the questions so that others can work on the solutions. My hope is that it will
also serve as inspiration to solve new problems you didn’t think were resolvable.
• If you’re a data scientist, this book will provide a holistic view of how you can
approach your stakeholders and generate ideas to apply your technical knowl‐
edge. In my experience, data scientists become really good at solving predictive
problems, but many times have difficulties delivering prescriptive courses of
viii | Preface
