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Big Data Analytics: Turning Big Data into Big Money

Book 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 AnalyticsM. 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.

Table of Contents

  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