Foreword xiii

Preface xvii

Acknowledgments xxv

Part One The Rise of Big Data 1

Chapter 1 What Is Big Data and Why Does It Matter? 3

What Is Big Data? 4

Is the “Big” Part or the “Data” Part More Important? 5

How Is Big Data Different? 7

How Is Big Data More of the Same? 9

Risks of Big Data 10

Why You Need to Tame Big Data 12

The Structure of Big Data 14

Exploring Big Data 16

Most Big Data Doesn’t Matter 17

Filtering Big Data Effectively 20

Mixing Big Data with Traditional Data 21

The Need for Standards 22

Today’s Big Data Is Not Tomorrow’s Big Data 24

Wrap-Up 26

Notes 27

Chapter 2 Web Data: The Original Big Data 29

Web Data Overview 30

What Web Data Reveals 36

Web Data in Action 42

Wrap-Up 50

Note 51

Chapter 3 A Cross-Section of Big Data Sources and the Value They Hold 53

Auto Insurance: The Value of Telematics Data 54

Multiple Industries: The Value of Text Data 57

Multiple Industries: The Value of Time and Location Data 60

Retail and Manufacturing: The Value of Radio Frequency Identification Data 64

Utilities: The Value of Smart-Grid Data 68

Gaming: The Value of Casino Chip Tracking Data 71

Industrial Engines and Equipment: The Value of Sensor Data 73

Video Games: The Value of Telemetry Data 76

Telecommunications and Other Industries: The Value of Social Network Data 78

Wrap-Up 82

Part Two Taming Big Data: The Technologies, Processes, and Methods 85

Chapter 4 The Evolution of Analytic Scalability 87

A History of Scalability 88

The Convergence of the Analytic and Data Environments 90

Massively Parallel Processing Systems 93

Cloud Computing 102

Grid Computing 109

MapReduce 110

It Isn’t an Either/Or Choice! 117

Wrap-Up 118

Notes 119

Chapter 5 The Evolution of Analytic Processes 121

The Analytic Sandbox 122

What Is an Analytic Data Set? 133

Enterprise Analytic Data Sets 137

Embedded Scoring 145

Wrap-Up 151

Chapter 6 The Evolution of Analytic Tools and Methods 153

The Evolution of Analytic Methods 154

The Evolution of Analytic Tools 163

Wrap-Up 175

Notes 176

Part Three Taming Big Data: The People and Approaches?177

Chapter 7 What Makes a Great Analysis? 179

Analysis versus Reporting 179

Analysis: Make It G.R.E.A.T.! 184

Core Analytics versus Advanced Analytics 186

Listen to Your Analysis 188

Framing the Problem Correctly 189

Statistical Significance versus Business Importance 191

Samples versus Populations 195

Making Inferences versus Computing Statistics 198

Wrap-Up 200

Chapter 8 What Makes a Great Analytic Professional? 201

Who Is the Analytic Professional? 202

The Common Misconceptions about Analytic Professionals 203

Every Great Analytic Professional Is an Exception 204

The Often Underrated Traits of a Great Analytic Professional 208

Is Analytics Certifi cation Needed, or Is It Noise? 222

Wrap-Up 224

Chapter 9 What Makes a Great Analytics Team? 227

All Industries Are Not Created Equal 228

Just Get Started! 230

There’s a Talent Crunch out There 231

Team Structures 232

Keeping a Great Team’s Skills Up 237

Who Should Be Doing Advanced Analytics? 241

Why Can’t IT and Analytic Professionals Get Along? 245

Wrap-Up 247

Notes 248

PART FOUR BRINGING IT TOGETHER: THE ANALYTICS CULTURE 249

Chapter 10 Enabling Analytic Innovation 251

Businesses Need More Innovation 252

Traditional Approaches Hamper Innovation 253

Defining Analytic Innovation 255

Iterative Approaches to Analytic Innovation 256

Consider a Change in Perspective 257

Are You Ready for an Analytic Innovation Center? 259

Wrap-Up 269

Note 270

Chapter 11 Creating a Culture of Innovation and Discovery?271

Setting the Stage 272

Overview of the Key Principles 274

Wrap-Up 290

Notes 291

Conclusion: Think Bigger! 293

About the Author 295

Index 297?