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Analytics. The Agile Way. Wiley and SAS Business Series

  • ID: 4290437
  • Book
  • 304 Pages
  • John Wiley and Sons Ltd
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Praise for Analytics: The Agile Way

"As analytics moves from an IT reporting exercise to a mission critical business–led discipline, collaboration, expediency, and flexibility are more important than ever. Information is no longer an asset to be trifled with, but rather one that organizations must harness aggressively. To date, I have seen scant attempts at Agile analytics, but this book will most certainly launch a thousand more. It is a must–read for any analytics leader, and a must–do for any analytics professional."
Douglas Laney, VP and Distinguished Analyst, Gartner

"Analytics: The Agile Way makes accessible two of today′s key themes in modern business: data and how we get work done. A great book for analytics, but also a great book for management and leadership. Given that we are all responsible for our own education, perhaps all business books should now be written as this one is: perfect for a formal class but also for lifelong learning."
Terri Griffith, Associate Dean & Professor at Santa Clara University′s Leavey School of Business and author of the award–winning book The Plugged–In Manager

"Phil Simon adroitly shows the potential of combining Agile thinking and methods with data analytics to provide powerful, distinct competitive advantages to organizations. As Simon demonstrates clearly through multiple real–life examples, Agile analytics can enable organizations to create lasting value from their Big Data efforts without ignoring the privacy and security issues those efforts frequently create."
Robert N. Charette, President, ITABHI Corporation

"A thoroughly enjoyable guide to the critical importance of analytics and Big Data. Simon wisely counsels that no one method works for all companies. The key is to be flexible and nimble and follow the guidelines he explains so clearly and convincingly."
Gary N. Smith, Fletcher Jones Professor of Economics at Pomona College and author of Money Machine: The Surprisingly Simple Power of Value Investing

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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

Defi ning 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 Benefi ts 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

Staffi ng 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 Profi ling 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 Benefi ts 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

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Phil Simon
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