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.
Table of Contents:
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
About the Author :
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.
