You receive an e-mail. It contains an offer for a complete personal computer system. It seems like the retailer read your mind since you were exploring computers on their web site just a few hours prior…. As you drive to the store to buy the computer bundle, you get an offer for a discounted coffee from the coffee shop you are getting ready to drive past. It says that since you’re in the area, you can get 10% off if you stop by in the next 20 minutes….
As you drink your coffee, you receive an apology from the manufacturer of a product that you complained about yesterday on your Facebook page, as well as on the company’s web site….
Finally, once you get back home, you receive notice of a special armor upgrade available for purchase in your favorite online video game. It is just what is needed to get past some spots you’ve been struggling with….
Sound crazy? Are these things that can only happen in the distant future? No. All of these scenarios are possible today! Big data. Advanced analytics. Big data analytics. It seems you can’t escape such terms today. Everywhere you turn people are discussing, writing about, and promoting big data and advanced analytics. Well, you can now add this book to the discussion.
What is real and what is hype? Such attention can lead one to the suspicion that perhaps the analysis of big data is something that is more hype than substance. While there has been a lot of hype over the past few years, the reality is that we are in a transformative era in terms of analytic capabilities and the leveraging of massive amounts of data. If you take the time to cut through the sometimes-over-zealous hype present in the media, you’ll find something very real and very powerful underneath it. With big data, the hype is driven by genuine excitement and anticipation of the business and consumer benefits that analyzing it will yield over time.
Big data is the next wave of new data sources that will drive the next wave of analytic innovation in business, government, and academia. These innovations have the potential to radically change how organizations view their business. The analysis that big data enables will lead to decisions that are more informed and, in some cases, different from what they are today. It will yield insights that many can only dream about today. As you’ll see, there are many consistencies with the requirements to tame big data and what has always been needed to tame new data sources. However, the additional scale of big data necessitates utilizing the newest tools, technologies, methods, and processes. The old way of approaching analysis just won’t work. It is time to evolve the world of advanced analytics to the next level. That’s what this book is about.
Taming the Big Data Tidal Wave isn’t just the title of this book, but rather an activity that will determine which businesses win and which lose in the next decade. By preparing and taking the initiative, organizations can ride the big data tidal wave to success rather than being pummeled underneath the crushing surf. What do you need to know and how do you prepare in order to start taming big data and generating exciting new analytics from it? Sit back, get comfortable, and prepare to find out!
Table of Contents:
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
About the Author :
BILL FRANKS is the Chief Analytics Officer for Teradata, where he provides insight on trends in the analytics and big data space and helps organizations implement their analytics effectively. In addition, Bill is a faculty member of the International Institute for Analytics and is an active speaker and blogger. His consulting work has spanned many industries for companies ranging from Fortune 100 companies to small non-profits.
