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

Chapter 4

Building the Big Data Team

One of the most important elements of a Big Data project is a rather obvious but often overlooked item: people. Without human involvement or interpretation, Big Data analytics becomes useless, having no purpose and no value. It takes a team to make Big Data work, and even if that team consists of only two individuals, it is still a necessary element.

Bringing people together to build a team can be an arduous process that involves multiple meetings, perhaps recruitment, and, of course, personnel management. Several specialized skills in Big Data are required, and that is what defines the team. Determining those skills is one of the first steps in putting a team together.


One of the first concepts to become acquainted with is the data scientist; a relatively new title, it is not readily recognized or accepted by many organizations, but it is here to stay.

A data scientist is normally associated with an employee or a business intelligence (BI) consultant who excels at analyzing data, particularly large amounts of data, to help a business gain a competitive edge. The data scientist is usually the de facto team leader during a Big Data analytics project.

The title data scientist is sometimes disparaged because it lacks specificity and can be perceived as an aggrandized synonym for data analyst. Nevertheless, the position is gaining acceptance with large enterprises that are interested in deriving meaning from Big Data, the voluminous ...

The best content for your career. Discover unlimited learning on demand for around $1/day.