You are previewing Big Data Analytics: Turning Big Data into Big Money.

Big Data Analytics: Turning Big Data into Big Money

Cover of Big Data Analytics: Turning Big Data into Big Money by Frank J. Ohlhorst Published by John Wiley & Sons
  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Preface
  6. Acknowledgments
  7. 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
    4. Dealing with the Nuances of Big Data
    5. An Open Source Brings Forth Tools
    6. Caution: Obstacles Ahead
  8. Chapter 2: Why Big Data Matters
    1. Big Data Reaches Deep
    2. Obstacles Remain
    3. Data Continue to Evolve
    4. Data and Data Analysis are Getting More Complex
    5. The Future is Now
  9. Chapter 3: Big Data and the Business Case
    1. Realizing Value
    2. The Case for Big Data
    3. The Rise of Big Data Options
    4. Beyond Hadoop
    5. With Choice Come Decisions
  10. Chapter 4: Building the Big Data Team
    1. The Data Scientist
    2. The Team Challenge
    3. Different Teams, Different Goals
    4. Don’t Forget the Data
    5. Challenges Remain
    6. Teams versus Culture
    7. Gauging Success
  11. Chapter 5: Big Data Sources
    1. Hunting for Data
    2. Setting the Goal
    3. Big Data Sources Growing
    4. Diving Deeper into Big Data Sources
    5. A Wealth of Public Information
    6. Getting Started with Big Data Acquisition
    7. Ongoing Growth, No End in Sight
  12. Chapter 6: The Nuts and Bolts of Big Data
    1. The Storage Dilemma
    2. Building a Platform
    3. Bringing Structure to Unstructured Data
    4. Processing Power
    5. Choosing among In-house, Outsourced, or Hybrid Approaches
  13. Chapter 7: Security, Compliance, Auditing, and Protection
    1. Pragmatic Steps to Securing Big Data
    2. Classifying Data
    3. Protecting Big Data Analytics
    4. Big Data and Compliance
    5. The Intellectual Property Challenge
  14. Chapter 8: The Evolution of Big Data
    1. Big Data: The Modern Era
    2. Today, Tomorrow, and the Next Day
    3. Changing Algorithms
  15. Chapter 9: Best Practices for Big Data Analytics
    1. Start Small with Big Data
    2. Thinking Big
    3. Avoiding Worst Practices
    4. Baby Steps
    5. The Value of Anomalies
    6. Expediency versus Accuracy
    7. In-Memory Processing
  16. Chapter 10: Bringing it All Together
    1. The Path to Big Data
    2. The Realities of Thinking Big Data
    3. Hands-on Big Data
    4. The Big Data Pipeline in Depth
    5. Big Data Visualization
    6. Big Data Privacy
  17. Appendix: Supporting Data
    1. “The MapR Distribution for Apache Hadoop”
    2. “High Availability: No Single Points of Failure”
  18. About the Author
  19. Index
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Chapter 2

Why Big Data Matters

Knowing what Big Data is and knowing its value are two different things. Even with an understanding of Big Data analytics, the value of the information can still be difficult to visualize. At first glance, the well of structured, unstructured, and semistructured data seems almost unfathomable, with each bucket drawn being little more than a mishmash of unrelated data elements.

Finding what matters and why it matters is one of the first steps in drinking from the well of Big Data and the key to avoid drowning in information. However, this question still remains: Why does Big Data matter? It seems difficult to answer for small and medium businesses, especially those that have shunned business intelligence solutions in the past and have come to rely on other methods to develop their markets and meet their goals.

For the enterprise market, Big Data analytics has proven its value, and examples abound. Companies such as Facebook, Amazon, and Google have come to rely on Big Data analytics as part of their primary marketing schemes as well as a means of servicing their customers better.

For example, Amazon has leveraged its Big Data well to create an extremely accurate representation of what products a customer should buy. Amazon accomplishes that by storing each customer’s searches and purchases and almost any other piece of information available, and then applying algorithms to that information to compare one customer’s information with all of the other ...

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