Creating a Data-Driven Organization: Practical Advice from the Trenches

Creating a Data-Driven Organization: Practical Advice from the Trenches

by Carl Anderson

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Overview

What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company, from analysts and management to the C-Suite and the board.

Through interviews and examples from data scientists and analytics leaders in a variety of industries, author Carl Anderson explains the analytics value chain you need to adopt when building predictive business models—from data collection and analysis to the insights and leadership that drive concrete actions. You’ll learn what works and what doesn’t, and why creating a data-driven culture throughout your organization is essential.

  • Start from the bottom up: learn how to collect the right data the right way
  • Hire analysts with the right skills, and organize them into teams
  • Examine statistical and visualization tools, and fact-based story-telling methods
  • Collect and analyze data while respecting privacy and ethics
  • Understand how analysts and their managers can help spur a data-driven culture
  • Learn the importance of data leadership and C-level positions such as chief data officer and chief analytics officer

Product Details

ISBN-13: 9781491916865
Publisher: O'Reilly Media, Incorporated
Publication date: 07/23/2015
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 302
File size: 10 MB

About the Author

Carl Anderson is the Director of Data Science at Warby Parker in New York overseeing data engineering, data science, supporting the broader analystics org, and creating a data-driven organization. He has had a broad-ranging career, mostly in scientific computing, covering areas such as healthcare modeling, data compression, robotics, and agent based modeling. He holds a Ph.D. in mathematical biology from the University of Sheffield, UK.

Table of Contents

Preface vii

1 What Do We Mean by Data-Driven? 1

Data Collection 1

Data Access 2

Reporting 4

Alerting 5

From Reporting and Alerting to Analysis 6

Hallmarks of Data-Drivenness 9

Analytics Maturity 11

Overview 17

2 Data Quality 19

Facets of Data Quality 20

Dirty Data 22

Data Provenance 36

Data Quality Is a Shared Responsibility 38

3 Data Collection 41

Collect All the Things 41

Prioritizing Data Sources 44

Connecting the Dots 46

Data Collection 48

Purchasing Data 50

Data Retention 56

4 The Analyst Organization 59

Types of Analysts 59

Analytics Is a Team Sport 65

Skills and Qualities 69

Just One More Tool 71

5 Data Analysis 83

What Is Analysis? 84

Types of Analysis 86

6 Metric Design 111

Metric Design 112

Key Performance Indicators 119

7 Storytelling with Data 127

Storytelling 128

First Steps 131

Sell, Sell, Sell! 133

Data Visualization 134

Delivery 145

Summary 151

8 A/B Testing 155

Why A/B Test? 159

How To: Best Practices in A/B Testing 160

Other Approaches 171

Cultural Implications 174

9 Decision Making 177

How Are Decisions Made? 179

What Makes Decision Making Hard? 183

Solutions 193

Conclusion 200

10 Data-Driven Culture 203

Open, Trusting Culture 204

Broad Data Literacy 207

Goals-First Culture 209

Inquisitive, Questioning Culture 211

Iterative, Learning Culture 212

Anti-HiPPO Culture 214

Data Leadership 215

11 The Data-Driven C-Suite 217

Chief Data Officer 218

Chief Analytics Officer 228

Conclusion 234

12 Privacy, Ethics, and Risk 237

Respect Privacy 239

Practice Empathy 243

Data Quality 248

Security 249

Enforcement 251

Conclusions 252

13 Conclusion 255

Further Reading 263

Analytics Organizations 263

Data Analysis & Data Science 263

Decision Making 263

Data Visualization 264

A/B Testing 264

A On the Unreasonable Effectiveness of Data: Why Is More Data Better? 265

B Vision Statement 273

Index 277

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