Winning with Data
Introduction ix
Chapter 1 Mad Men to Math Men: The Power of the
Data-Driven Culture 1
Operationalizing Data: Uber’s Competitive
Weapon 2
The Era of Instant Data: You Better Get
Yourself Together 4
Data Supply Chains: Buckling Under
the Load 6
Management by Opinion: The Illusion
of Knowledge 8
Our Vantage Points 10
Chapter 2 Four Problems with Data Today:
Breadlines, Obscurity, Fragmentation,
and Brawls 15
Data Breadlines for the Data-Poor 15
Data Obscurity: The Failure of the Card
Catalog 17
Rogue Databases and Analysts: The Data
Fragmentation Problem 19
Data Brawls: When Miscommunication
Devolves into Arguments 21
Chapter 3 Business Intelligence: How We
Got Here 23
Business Intelligence Is Born:
The First Query 23
Databases for the Masses: Oracle
Commercializes Codd’s Invention 24
Legacy BI: A Three-Layer Cake 26
v
vi Contents
Google’s Answer to Huge Data:
Vanilla Boxes 27
600 Petabytes per Day: HiPal at Facebook 30
Extreme Data Collection: The New Normal 32
Looker: Weaving the Data Fabric 33
Chapter 4 Achieving Data Enlightenment: Gathering
Data in the Morning and Changing Your
Business’s Operations in the Afternoon 37
Not Just Another Person with an Opinion 37
Aligning Sales Teams in Real Time 48
Scaling Sales Teams with Data 50
Determining Customer Satisfaction at
Every Point in the Buyer Journey 52
The Rosetta Stone: Developing a Shared
Data Language 55
The One Equation That Defines
the Business 57
Brutal Intellectual Honesty: Speaking Data
to Power 60
Putting Pride in Its Place: How Data
Transforms Cultures 66
Chapter 5 Five Steps to Creating a Data-Driven
Company—From Recruiting to Regression,
It All Starts with Curiosity: Changing the
Culture 71
It All Starts with Curiosity 71
Why You Should Stop Listening to
Your Boss 72
How to Recruit Curious People 76
Chapter 6 From Hacks to Harmony: The Typical
Progression of Data-Driven Companies 83
Step 1: Ask Your Friend, the Engineer 84
Step 2: Bastardize an Existing Solution 84
Step 3: Access Raw Data 85
The Crux of the Problem 85
Contents vii
Bring Your Own BI: The Five Letters That
Will Change the Data World 86
The Power of a Unified Data-Modeling Layer 89
The Final Step: A Data Fabric 92
Chapter 7 Data Literacy and Empowerment: The
Core Responsibilities of the Data Team 95
The Illusion of Validity: How to Avoid
Data Biases 95
Correlation versus Causation 98
How Facebook and Zendesk Engender
Data Literacy 100
Walking the Data Gemba: Training
by Walking Around 104
Chapter 8 Deeper Analyses: Asking the Right
Questions 109
When Data Confounds Our Intuition:
How to Handle Ambiguity 112
Data Is Useless Unless You Can Act on It 115
Defining New Opportunities by Creating
New Metrics That Matter 120
The Fastest Growing Media Site of All Time 122
How to Run a Data-Backed Experiment:
Step by Step 124
Chapter 9 Changing the Way We Operate 129
Change Begins with a Story 129
Deliver Data with Panache: Structuring
Presentations to Inspire 133
Chapter 10 Putting It All Together 141
Acknowledgments 145
Appendix: Revenue Metrics 147
Index 155