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

by Minelli, Michael; Chambers, Michele; Dhiraj, Ambiga

• ISBN13: 9781118147603
• ISBN10: 111814760X
• eBook ISBN(s): 9781118239155
• Edition: 1st
• Format: Hardcover
• Copyright: 2013-01-22
• Publisher: Wiley
• More Book Details

Summary

Practical guidance for using open source analytic tools for maximum advantage There is much confusion in the market about the difference between business intelligence and predictive analytics. Big Data, Big Analytics clears up the confusion. Exploring how Open Source R is being used by many companies to create their predictive analytic software, Big Data, Big Analytics examines the current hot trends, such as Open Source R, in business analytics today and shows how to use them effectively. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics, including Cloudera, Google, Amazon, Microsoft, Workday, Salesforce.com, Verizon, and more. Shows how open source analytic tools can be used effectively and introduces some of the new trends in analytics Explores how Open Source R is being used by many companies to create predictive analytic software Explains this new technology and how companies can use them effectively to gather the data that they need A new generation of analytic technologies is emerging to turn your unintelligible deluge of data into usable information. Big Data. Big Analytics explains it all, with practical, straightforward guidance on the latest technology trends in business intelligence.

Author Biography

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.

Table of Contents

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