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

Book Description

Unique prospective on the big data analytics phenomenon for both business and IT professionals

The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability.

The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics.

  • Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.)

  • Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights

  • Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

Table of Contents

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Foreword: Big Data and Corporate Evolution
  7. Preface
  8. Acknowledgments
  9. Chapter 1: What Is Big Data and Why Is It Important?
    1. A Flood of Mythic “Start-Up” Proportions
    2. Big Data Is More Than Merely Big
    3. Why Now?
    4. A Convergence of Key Trends
    5. Relatively Speaking . . .
    6. A Wider Variety of Data
    7. The Expanding Universe of Unstructured Data
    8. Setting the Tone at the Top
    9. Notes
  10. Chapter 2: Industry Examples of Big Data
    1. Digital Marketing and the Non-line World
    2. Database Marketers, Pioneers of Big Data
    3. Big Data and the New School of Marketing
    4. Fraud and Big Data
    5. Risk and Big Data
    6. Credit Risk Management
    7. Big Data and Algorithmic Trading
    8. Big Data and Advances in Health Care
    9. Pioneering New Frontiers in Medicine
    10. Advertising and Big Data: From Papyrus to Seeing Somebody
    11. Using Consumer Products as a Doorway
    12. Notes
  11. Chapter 3: Big Data Technology
    1. The Elephant in the Room: Hadoop’s Parallel World
    2. Old vs. New Approaches
    3. Data Discovery: Work the Way People’s Minds Work
    4. Open-Source Technology for Big Data Analytics
    5. The Cloud and Big Data
    6. Predictive Analytics Moves into the Limelight
    7. Software as a Service BI
    8. Mobile Business Intelligence Is Going Mainstream
    9. Crowdsourcing Analytics
    10. Inter- and Trans-Firewall Analytics
    11. R&D Approach Helps Adopt New Technology
    12. Big Data Technology Terms
    13. Data Size 101
    14. Notes
  12. Chapter 4: Information Management
    1. The Big Data Foundation
    2. Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami)
    3. Big Data Computation
    4. More on Big Data Storage
    5. Big Data Computational Limitations
    6. Big Data Emerging Technologies
  13. Chapter 5: Business Analytics
    1. The Last Mile in Data Analysis
    2. Geospatial Intelligence Will Make Your Life Better
    3. Listening: Is It Signal or Noise?
    4. Consumption of Analytics
    5. From Creation to Consumption
    6. Visualizing: How to Make It Consumable?
    7. Organizations Are Using Data Visualization as a Way to Take Immediate Action
    8. Moving from Sampling to Using All the Data
    9. Thinking Outside the Box
    10. 360° Modeling
    11. Need for Speed
    12. Let’s Get Scrappy
    13. What Technology Is Available?
    14. Moving from Beyond the Tools to Analytic Applications
    15. Notes
  14. Chapter 6: The People Part of the Equation
    1. Rise of the Data Scientist
    2. Using Deep Math, Science, and Computer Science
    3. The 90/10 Rule and Critical Thinking
    4. Analytic Talent and Executive Buy-in
    5. Developing Decision Sciences Talent
    6. Holistic View of Analytics
    7. Creating Talent for Decision Sciences
    8. Creating a Culture That Nurtures Decision Sciences Talent
    9. Setting Up the Right Organizational Structure for Institutionalizing Analytics
  15. Chapter 7: Data Privacy and Ethics
    1. The Privacy Landscape
    2. The Great Data Grab Isn’t New
    3. Preferences, Personalization, and Relationships
    4. Rights and Responsibility
    5. Conscientious and Conscious Responsibility
    6. Privacy May Be the Wrong Focus
    7. Can Data Be Anonymized?
    8. Balancing for Counterintelligence
    9. Now What?
    10. Notes
  16. Conclusion
  17. Recommended Resources
  18. About the Authors
  19. Index