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Big Data Analytics: Turning Big Data into Big Money

Big Data Analytics: Turning Big Data into Big Money

Frank J. Ohlhorst

ISBN: 978-1-119-20500-5

Sep 2015

176 pages

$49.95

Description

Unique insights to implement big data analytics and reap big returns to your bottom line

Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities.

  • Reveals big data analytics as the next wave for businesses looking for competitive advantage
  • Takes an in-depth look at the financial value of big data analytics
  • Offers tools and best practices for working with big data

Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

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Preface ix

Acknowledgments xiii

Chapter 1 What Is Big Data? 1

The Arrival of Analytics 2

Where Is the Value? 3

More to Big Data Than Meets the Eye 5

Dealing with the Nuances of Big Data 6

An Open Source Brings Forth Tools 7

Caution: Obstacles Ahead 8

Chapter 2 Why Big Data Matters 11

Big Data Reaches Deep 12

Obstacles Remain 13

Data Continue to Evolve 15

Data and Data Analysis Are Getting More Complex 17

The Future Is Now 18

Chapter 3 Big Data and the Business Case 21

Realizing Value 22

The Case for Big Data 22

The Rise of Big Data Options 25

Beyond Hadoop 27

With Choice Come Decisions 28

Chapter 4 Building the Big Data Team 29

The Data Scientist 29

The Team Challenge 30

Different Teams, Different Goals 31

Don’t Forget the Data 32

Challenges Remain 32

Teams versus Culture 34

Gauging Success 35

Chapter 5 Big Data Sources 37

Hunting for Data 38

Setting the Goal 39

Big Data Sources Growing 40

Diving Deeper into Big Data Sources 42

A Wealth of Public Information 43

Getting Started with Big Data Acquisition 44

Ongoing Growth, No End in Sight 46

Chapter 6 The Nuts and Bolts of Big Data 47

The Storage Dilemma 47

Building a Platform 52

Bringing Structure to Unstructured Data 57

Processing Power 59

Choosing among In-house, Outsourced, or Hybrid Approaches 61

Chapter 7 Security, Compliance, Auditing, and Protection 63

Pragmatic Steps to Securing Big Data 64

Classifying Data 65

Protecting Big Data Analytics 66

Big Data and Compliance 67

The Intellectual Property Challenge 72

Chapter 8 The Evolution of Big Data 77

Big Data: The Modern Era 80

Today, Tomorrow, and the Next Day 84

Changing Algorithms 90

Chapter 9 Best Practices for Big Data Analytics 93

Start Small with Big Data 94

Thinking Big 95

Avoiding Worst Practices 96

Baby Steps 98

The Value of Anomalies 101

Expediency versus Accuracy 103

In-Memory Processing 104

Chapter 10 Bringing It All Together 111

The Path to Big Data 112

The Realities of Thinking Big Data 113

Hands-on Big Data 115

The Big Data Pipeline in Depth 116

Big Data Visualization 121

Big Data Privacy 122

Appendix Supporting Data 125

“The MapR Distribution for Apache Hadoop” 126

“High Availability: No Single Points of Failure” 142

About the Author 151

Index 153