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Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses

ISBN: 978-1-118-14760-3
224 pages
January 2013
Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today

Unique prospective on the big data analytics phenomenon for both business and IT professionals

The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability.

The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics.

  • Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.)
  • Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights
  • Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.
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FOREWORD xiii

PREFACE xix

ACKNOWLEDGMENTS xxi

CHAPTER 1 What Is Big Data and Why Is It Important? 1

A Flood of Mythic “Start-Up” Proportions 4

Big Data Is More Than Merely Big 5

Why Now? 6

A Convergence of Key Trends 7

Relatively Speaking . . . 9

A Wider Variety of Data 10

The Expanding Universe of Unstructured Data 11

Setting the Tone at the Top 15

Notes 18

CHAPTER 2 Industry Examples of Big Data 19

Digital Marketing and the Non-line World 19

Don’t Abdicate Relationships 22

Is IT Losing Control of Web Analytics? 23

Database Marketers, Pioneers of Big Data 24

Big Data and the New School of Marketing 27

Consumers Have Changed. So Must Marketers. 28

The Right Approach: Cross-Channel Lifecycle Marketing 28

Social and Affiliate Marketing 30

Empowering Marketing with Social Intelligence 31

Fraud and Big Data 34

Risk and Big Data 37

Credit Risk Management 38

Big Data and Algorithmic Trading 40

Crunching Through Complex Interrelated Data 41

Intraday Risk Analytics, a Constant Flow of Big Data 42

Calculating Risk in Marketing 43

Other Industries Benefit from Financial Services’ Risk Experience 43

Big Data and Advances in Health Care 44

“Disruptive Analytics” 46

A Holistic Value Proposition 47

BI Is Not Data Science 49

Pioneering New Frontiers in Medicine 50

Advertising and Big Data: From Papyrus to Seeing Somebody 51

Big Data Feeds the Modern-Day Donald Draper 52

Reach, Resonance, and Reaction 53

The Need to Act Quickly (Real-Time When Possible) 54

Measurement Can Be Tricky 55

Content Delivery Matters Too 56

Optimization and Marketing Mixed Modeling 56

Beard’s Take on the Three Big Data Vs in Advertising 57

Using Consumer Products as a Doorway 58

Notes 59

CHAPTER 3 Big Data Technology 61

The Elephant in the Room: Hadoop’s Parallel World 61

Old vs. New Approaches 64

Data Discovery: Work the Way People’s Minds Work 65

Open-Source Technology for Big Data Analytics 67

The Cloud and Big Data 69

Predictive Analytics Moves into the Limelight 70

Software as a Service BI 72

Mobile Business Intelligence is Going Mainstream 73

Ease of Mobile Application Deployment 75

Crowdsourcing Analytics 76

Inter- and Trans-Firewall Analytics 77

R&D Approach Helps Adopt New Technology 80

Adding Big Data Technology into the Mix 81

Big Data Technology Terms 83

Data Size 101 86

Notes 88

CHAPTER 4 Information Management 89

The Big Data Foundation 89

Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami) 92

Big Data Computation 93

More on Big Data Storage 96

Big Data Computational Limitations 96

Big Data Emerging Technologies 97

CHAPTER 5 Business Analytics 99

The Last Mile in Data Analysis 101

Geospatial Intelligence Will Make Your Life Better 103

Listening: Is It Signal or Noise? 106

Consumption of Analytics 108

From Creation to Consumption 110

Visualizing: How to Make It Consumable? 110

Organizations Are Using Data Visualization as a Way to Take Immediate Action 116

Moving from Sampling to Using All the Data 121

Thinking Outside the Box 122

360° Modeling 122

Need for Speed 122

Let’s Get Scrappy 123

What Technology Is Available? 124

Moving from Beyond the Tools to Analytic Applications 125

Notes 125

CHAPTER 6 The People Part of the Equation 127

Rise of the Data Scientist 128

Learning over Knowing 130

Agility 131

Scale and Convergence 131

Multidisciplinary Talent 131

Innovation 132

Cost Effectiveness 132

Using Deep Math, Science, and Computer Science 133

The 90/10 Rule and Critical Thinking 136

Analytic Talent and Executive Buy-in 137

Developing Decision Sciences Talent 139

Holistic View of Analytics 140

Creating Talent for Decision Sciences 142

Creating a Culture That Nurtures Decision Sciences Talent 144

Setting Up the Right Organizational Structure for

Institutionalizing Analytics 146

CHAPTER 7 Data Privacy and Ethics 151

The Privacy Landscape 152

The Great Data Grab Isn’t New 152

Preferences, Personalization, and Relationships 153

Rights and Responsibility 154

Playing in a Global Sandbox 159

Conscientious and Conscious Responsibility 161

Privacy May Be the Wrong Focus 162

Can Data Be Anonymized? 164

Balancing for Counterintelligence 165

Now What? 165

Notes 167

CONCLUSION 169

RECOMMENDED RESOURCES 175

ABOUT THE AUTHORS 177

INDEX 179

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Considered one of the top sales and marketing executives in the business analytics space, MICHAEL MINELLI is Vice President, Information Services, for MasterCard Advisors. The majority of his sixteen years of analytics industry experience was at SAS, where he spent over eleven years helping clients with large-scale analytic projects related to marketing, risk, supply chain, and finance.

MICHELE CHAMBERS is currently in the Big Data Analytics startup world and was formerly the General Manager & Vice President of Big Data Analytics at IBM, where her team was responsible for working with customers to fully exploit the IBM Big Data Platform.

AMBIGA DHIRAJ is the Head of Client Delivery for Mu Sigma, where she leads their delivery teams to solve high-impact business problems in the areas of marketing, supply chain, and risk analytics for market-leading companies across multiple verticals.

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