For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all.
This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends-and act upon them until it's too late.
But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating?
What if there were a better way to do analytics?
Fortunately, you're in luck...
Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon.
Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you.
Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors.
Preface: The Power of Dynamic Data xvii
List of Figures and Tables xxvii
Introduction: It Didn't Used to Be This Way 1
A Little History Lesson 2
Analytics and the Need for Speed 5
Book Scope, Approach, and Style 9
Intended Audience 12
Plan of Attack 13
Next 14
Notes 14
Part One Background and Trends 17
Chapter 1 Signs of the Times: Why Data and Analytics Are Dominating Our World 19
The Moneyball Effect 20
Digitization and the Great Unbundling 22
Amazon Web Services and Cloud Computing 24
Not Your Father's Data Storage 26
Moore's Law 28
The Smartphone Revolution 28
The Democratization of Data 29
The Primacy of Privacy 29
The Internet of Things 31
The Rise of the Data-Savvy Employee 31
The Burgeoning Importance of Data Analytics 32
Data-Related Challenges 40
Companies Left Behind 41
The Growth of Analytics Programs 42
Next 43
Notes 43
Chapter 2 The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It 45
Types of Data 46
Getting the Data 52
Data in Motion 61
Next 63
Notes 63
Chapter 3 The Fundamentals of Analytics: Peeling Back the Onion 65
Defining Analytics 66
Types of Analytics 69
Streaming Data Revisited 72
A Final Word on Analytics 74
Next 75
Notes 75
Part Two Agile Methods and Analytics 77
Chapter 4 A Better Way to Work: The Benefits and Core Values of Agile Development 79
The Case against Traditional Analytics Projects 80
Proving the Superiority of Agile Methods 82
The Case for Guidelines over Rules 84
Next 88
Notes 88
Chapter 5 Introducing Scrum: Looking at One of Today's Most Popular Agile Methods 89
A Very Brief History 90
Scrum Teams 91
User Stories 94
Backlogs 97
Sprints and Meetings 98
Releases 101
Estimation Techniques 102
Other Scrum Artifacts, Tools, and Concepts 109
Next 112
Chapter 6 A Framework for Agile Analytics: A Simple Model for Gathering Insights 113
Perform Business Discovery 115
Perform Data Discovery 117
Prepare the Data 118
Model the Data 120
Score and Deploy 127
Evaluate and Improve 128
Next 130
Notes 130
Part Three Analytics in Action 131
Chapter 7 University Tutoring Center: An In-Depth Case Study on Agile Analytics 133
The UTC and Project Background 134
Project Goals and Kickoff 136
Iteration One 139
Iteration Two 140
Iteration Three 145
Iteration Four 146
Results 147
Lessons 148
Next 148
Chapter 8 People Analytics at Google/Alphabet: Not Your Father's HR Department 149
The Value of Business Experiments 150
PiLab's Adventures in Analytics 151
A Better Approach to Hiring 153
Staffing 156
The Value of Perks 158
Results and Lessons 162
Next 162
Notes 163
Chapter 9 The Anti-Google: Beneke Pharmaceuticals 165
Project Background 166
Business and Data Discovery 167
The Friction Begins 168
Astonishing Results 169
Developing Options 171
The Grand Finale 172
Results and Lessons 173
Next 174
Chapter 10 Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem 175
Paying Nurses 176
Enter the Consultant 178
User Stories 179
Agile: The Better Way 182
Results 183
Lessons 183
Next 184
Chapter 11 Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster 185
Unintended but Familiar Consequences 187
Evaluating the Problem 188
Results and Lessons 193
Next 195
Notes 195
Part Four Making the Most Out of Agile Analytics ..........197
Chapter 12 The Benefits of Agile Analytics: The Upsides of Small Batches 199
Life at IAC 200
Life at RDC 203
Comparing the Two 206
Next 206
Chapter 13 No Free Lunch: The Impediments to-and Limitations of-Agile Analytics 209
People Issues 210
Data Issues 212
The Limitations of Agile Analytics 216
Next 219
Chapter 14 The Importance of Designing for Data: Lessons from the Upstarts 221
The Genes of Music 222
The Tension between Data and Design 226
Next 229
Notes 229
Part Five Conclusions and Next Steps 231
Chapter 15 What Now?: A Look Forward 233
A Tale of Two Retailers 234
The Blurry Futures of Data, Analytics, and Related Issues 239
Final Thoughts and Next Steps 242
Notes 243
Afterword 245
Acknowledgments 247
Selected Bibliography 249
Books 249
Articles and Essays 251
About the Author 253
Index 255
