Taming the Big Data Tidal Wave : Finding Opportunities in Huge Data Streams with Advanced Analytics

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2012-04-24
  • Publisher: Wiley

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Supplemental Materials

What is included with this book?


Written in an informal style, by someone who has lived analytics in big business, this provides a "from the trenches" viewpoint of analytics. This book shows how Big Data is changing the world of analytics; what people, processes, and mindsets are necessary to succeed in analytics; what trends are developing that will change how businesses do analytics as Big Data alters the scale of the inputs and growing requirements change the scale of the outputs; how to think bigger in terms of what analytics can do for a business; how analytics has evolved in the past decades and how it will continue to evolve in the coming decades; some of the key changes within the tools and technologies used for analytics and how they've changed the game already, with more to come; and what leading companies are doing to ensure innovation in analytics is occurring within their organizations.

Author Biography

Bill Franks is Chief Analytics Officer for Teradata's global alliance programs. Bill also oversees the Business Analytics Innovation Center, which is jointly sponsored by Teradata and SAS and focuses on helping clients pursue innovative analytics. In addition, Bill is a faculty member of the International Institute for Analytics and is an active speaker and blogger. His analytic consulting work has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations.

Table of Contents

Forewordp. xiii
Prefacep. xvii
Acknowledgmentsp. xxv
The Rise of Big Datap. 1
What is Big Data and Why Does It Matter?p. 3
What is Big Data?p. 4
Is the "Big" Part or the "Data" Part More Important?p. 5
How is Big Data Different?p. 7
How Is Big Data More of the Same?p. 9
Risks of Big Datap. 10
Why You Need to Tame Big Datap. 12
The Structure of Big Datap. 14
Exploring Big Datap. 16
Most Big Data Doesn't Matterp. 17
Filtering Big Data Effectivelyp. 20
Mixing Big Data with Traditional Datap. 21
The Need for Standardsp. 22
Today's Big Data Is Not Tomorrow's Big Datap. 24
Wrap-Upp. 26
Notesp. 27
web Data: The Original Big Datap. 29
Web Data Overviewp. 30
What Web Data Revealsp. 36
Web Data in Actionp. 42
Wrap-Upp. 50
Notep. 51
A Cross-Section of Big Data Sources and the Value They Holdp. 53
Auto Insurance: The Value of Telematics Datap. 54
Multiple industries: The Value of Text Datap. 57
Multiple Industries: The Value of Time and Location Datap. 60
Retail and Manufacturing: The Value of Radio Frequency Identification Datap. 64
Utilities: The Value of Smart-Grid Datap. 68
Gaming: The Value of Casino Chip Tracking Datap. 71
Industrial Engines and Equipment: The Value of Sensor Datap. 73
Video Games: The Value of Telemetry Datap. 76
Telecommunications and Other Industries: The Value of Social Network Datap. 78
Wrap-Upp. 82
Taming Big Data: The Technologies, Processes, and Methodsp. 85
The Evolution of Analytic Scalabilityp. 87
A History of Scalabilityp. 88
The Convergence of the Analytic and Data Environmentsp. 90
Massively Parallel Processing Systemsp. 93
Cloud Computingp. 102
Grid Computingp. 109
MapReducep. 110
It Isn't an Either/Or Choice!p. 117
Wrap-Upp. 118
Notesp. 119
The Evolution of Analytic Processesp. 121
The Analytic Sandboxp. 122
What Is an Analytic Data Set?p. 133
Enterprise Analytic Data Setsp. 137
Embedded Scoringp. 145
Wrap-Upp. 151
The Evolution of Analytic Tools and Methodsp. 153
The Evolution of Analytic Methodsp. 154
The Evolution of Analytic Toolsp. 163
Wrap-Upp. 175
Notesp. 176
Taming Big Data: The People and Approachesp. 177
What Makes a Great Analysis?p. 179
Analysis versus Reportingp. 179
Analysis: Make It G.R.E.A.T.!p. 184
Core Analytics versus Advanced Analyticsp. 186
Listen to Your Analysisp. 188
Framing the Problem Correctlyp. 189
Statistical Significance versus Business Importancep. 191
Samples versus Populationsp. 195
Making Inferences versus Computing Statisticsp. 198
Wrap-Upp. 200
What Makes a Great Analytic Professional?p. 201
Who Is the Analytic Professional?p. 202
The Common Misconceptions about Analytic Professionalsp. 203
Every Great Analytic Professional Is an Exceptionp. 204
The Often Underrated Traits of a Great Analytic Professionalp. 208
Is Analytics Certification Needed, or Is It Noise?p. 222
Wrap-Upp. 224
What Makes a Great Analytics Team?p. 227
All Industries Are Not Created Equalp. 228
Just Get Started!p. 230
There's a Talent Crunch out Therep. 231
Team Structuresp. 232
Keeping a Great Team's Skills Upp. 237
Who Should Be Doing Advanced Analytics?p. 241
Why Can't IT and Analytic Professionals Get Along?p. 245
Wrap-upp. 247
Notesp. 248
Bringing it Together: The Analytics Culturep. 249
Enabling Analytic innovationp. 251
Businesses Need More Innovationp. 252
Traditional Approaches Hamper innovationp. 253
Defining Analytic innovationp. 255
Iterative Approaches to Analytic Innovationp. 256
Consider a Change in Perspectivep. 257
Are You Ready for an Analytic Innovation Center?p. 259
Wrap-Upp. 269
Notep. 270
Creating a Culture of Innovation and Discoveryp. 271
Setting the Stagep. 272
Overview of the Key Principlesp. 274
Wrap-Upp. 290
Notesp. 291
Conclusion: Think Bigger!p. 293
About the Authorp. 295
Indexp. 297
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