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.
Cuprins
Foreword
Preface
Acknowledgments
Chapter 1 What Is Big Data and Why Is It Important?
A Flood of Mythic ‘Start-up’ Proportions
Big Data is More Than Merely Big
Why Now?
A Convergence of Key Trends
Relatively Speaking…
A Wider Variety of Data
The Expanding Universe of Unstructured Data
Setting the Tone at the Top
Notes
Chapter 2 Industry Examples of Big Data
Digital Marketing and the Non-line World
Don’t Abdicate Relationships
Is IT Losing Control of Web Analytics?
Database Marketers, Pioneers of Big Data
Big Data and the New School of Marketing
Fraud and Big Data
Risk and Big Data
Credit Risk Management
Big Data and Algorithmic Trading
Calculating Risk in Marketing
Other Industries Benefit from Banking’s Risk Experience
Big Data and Advances in Healthcare
‘Disruptive Analytics’
A Holistic Value Proposition
BI is not Data Science
Pioneering New Frontiers in Medicine
Advertising and Big Data: From Papyrus to Seeing Somebody
Big Data Feeds the Modern Day Draper
Measurement Can Be Tricky
Beard’s Take on the Three Big Data V’s in Advertising
Using Consumer Products as a Doorway
Notes
Chapter 3 Big Data Technology
The Elephant in the Room: Hadoop’s Parallel World
Old versus New Approaches
Data Discovery: Work the Way People’s Minds Work
Open Source Technology for Big Data Analytics
The Cloud and Big Data
Predictive Analytics Moves into the Limelight
Software as a Service BI
Mobile Business Intelligence is Going Mainstream
Ease of Mobile Application Deployment
Crowdsourcing Analytics
Inter and Trans Firewall Analytics
R&D Approach Helps Adopt New Technology
Adding Big Data Technology into the Mix
Big Data Technology Terms
Data Size 101
Notes
Chapter 4 Information Management
The Big Data Foundation
Big Data Computing Platforms (or Computing Platforms that can handle the Big Data Analytics Tsunami)
Big Data Computation
More on Big Data Storage
Big Data Computational Limitations
Big Data Emerging Technologies
Chapter 5 Business Analytics
The Last Mile in Data Analysis
Listening: Is it Signal or Noise?
Consumption of Analytics
From Creation to Consumption
Visualizing: How to Make It Consumable?
Organizations are using Data Visualization as a Way to Take Immediate Action
Moving from Sampling to Using All the Data
Thinking Outside the Box
360 Degree Modeling
Need for Speed
Let’s Get Scrappy
What Technology is Available?
Moving from beyond the Tools to Analytic Applications
Notes
Chapter 6 The People Part of the Equation
Rise of the Data Scientist
Learning over Knowing
Using Deep Math, Science and Computer Science
The 90/10 Rule and Critical Thinking
Analytic Talent and Executive Buy-in
Developing Decision Sciences Talent
Holistic View of Analytics
Creating Talent for Decision Sciences
Creating a Culture that Nurtures Decision Sciences Talent
Setting up the Right Organizational Structure for Institutionalizing Analytics
Chapter 7 Data Privacy and Ethics
The Privacy Landscape
The Great Data Grab Isn’t New
Preferences, Personalization, and Relationships
Rights and Responsibility
Playing in a Global Sandbox
Conscientious and Conscious Responsibility
Privacy May Be the Wrong Focus
Can Data Be Anonymized?
Balancing for Counter Intelligence
Now What?
Notes
Conclusion
Recommended Resources
About the Authors
Index
Despre autor
Considered one of the top sales and marketing executives in the business analytics space, MICHAEL MINELLI is Vice President, Information Services, for Master Card 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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