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Big Data Revolution

Book Description

Exploit the power and potential of Big Data to revolutionize business outcomes

Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist.

Companies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology.

  • Examine the major emerging patterns in Big Data
  • Consider the debate surrounding the ethical use of data
  • Recognize patterns and improve personal and organizational performance
  • Make more informed decisions with quantifiable results

In an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.

Table of Contents

    1. Prologue
      1. Berkeley, 1930s
      2. Pattern Recognition
        1. Nelson Peltz
      3. Committing to One Percent
      4. The Big Data Revolution
    2. Introduction
      1. Storytelling
      2. Objective
      3. Outline
        1. Part I “The Revolution Starts Now: 9 Industries Transforming with Data”
        2. Part II “Learning from Patterns in Big Data”
        3. Part III “Leading the Revolution”
      4. Storytelling (Continued)
    3. Part I: The Revolution Starts Now: 9 Industries Transforming with Data
      1. Chapter 1: Transforming Farms with Data
        1. California, 2013
        2. Brief History of Farming
        3. The Data Era
          1. Potato Farming
          2. Precision Farming
          3. Capturing Farm Data
        4. Deere & Company Versus Monsanto
          1. Integrated Farming Systems
        5. Data Prevails
          1. The Climate Corporation
          2. Growsafe Systems
        6. Farm of the Future
        7. California, 2013 (Continued)
      2. Chapter 2: Why Doctors Will Have Math Degrees
        1. United States, 2014
        2. The history of Medical Education
          1. Scientific Method
          2. Rise of Specialists
        3. We have a Problem
          1. Ben Goldacre
          2. Vinod Khosla
        4. The Data Era
          1. Collecting Data
          2. Telemedicine
          3. Innovating with Data
          4. Implications of a Data-Driven Medical World
        5. The Future of Medical School
          1. A Typical Medical School
          2. A Medical School for the Data Era
        6. United States, 2030
      3. Chapter 3: Revolutionizing Insurance: Why Actuaries Will Become Data Scientists
        1. Middle of Somewhere, 2012
        2. Short History of Property & Casualty Insurance and Underwriting
        3. Actuarial Science In Insurance
          1. Pensions, Insurance, Leases
          2. Compound Interest
          3. Probability
          4. Mortality Data
        4. Modern-Day Insurance
          1. Eight Weeks to Eight Days
          2. Online Policies
        5. The Data Era
          1. Dynamic Risk Management
          2. Catastrophe Risk
          3. Open Access Modeling
          4. Opportunities
        6. Middle of Somewhere, 2012 (Continued)
      4. Chapter 4: Personalizing retail and fashion
        1. Karolina
        2. A Brief History of Retail
          1. Retail Eras
          2. Aristide Boucicaut
          3. The Shift
        3. The Data Era
          1. Stitch Fix
          2. Keaton Row
          3. Zara
        4. Karolina (Continued)
      5. Chapter 5: Transforming Customer relationships with data
        1. Buying a House
        2. Brief History of Customer Service
          1. Customer Service Over Time
          2. Boeing
          3. Financial Services
        3. The Data Era
          1. An Automobile Manufacturer
          2. Zendesk
        4. Buying a House (Continued)
      6. Chapter 6: Intelligent Machines
        1. Denmark
        2. Intelligent Machines
          1. Machine Data
        3. The Data Era
          1. General Electric
          2. Drones
          3. Tesla
        4. Networks of Data
        5. Denmark (Continued)
      7. Chapter 7: Government and Society
        1. Egypt, 2011
        2. Social Media
        3. Intelligence
          1. Snowden Effect
          2. Privacy Risk Versus Reward
          3. Observation or Surveillance
          4. Development Targets
          5. Open Data
          6. Hackathons
          7. Open Access
        4. Ensuring Personal Protection
          1. Private Clouds
          2. Sanitizing Data
        5. Evidence-Based Policy
        6. Public-Private Partnerships
        7. Impact Bonds
          1. Social Impact Bond
          2. Development Impact Bonds
          3. The Role of Big Data
        8. Egypt, 2011 (Continued)
      8. Chapter 8: Corporate Sustainability
        1. City of London
        2. Global Megaforces
          1. Population
          2. Carbon Footprint
          3. Water Scarcity
        3. Environmental Risk
          1. BP and Exxon Mobile
        4. Early Warning Systems
          1. Social Media
        5. Risk and Resilience
        6. Measuring Sustainability
        7. Long-Term Decision Making
        8. Stranded Assets
        9. City of London (Continued)
      9. Chapter 9: Weather and Energy
        1. India, 2012
        2. The Weather
        3. Forecasting the Weather
          1. When are Weather Forecasts Wrong?
          2. Chaos
          3. Ensemble Forecasts
          4. Communication
        4. Renewable Energy
          1. Solar, Hydro, and Wind Power
          2. Volatile or Intermittent Supply
        5. Energy Consumption
          1. Smart Meters
          2. Intelligent Demand-Side Management
        6. India, 2012 (Continued)
    4. Part II: Learning from Patterns in Big Data
      1. Chapter 10: Pattern Recognition
        1. Elements of Success Rhyme
        2. Pattern Recognition: A Gift or Trap?
        3. What Fish Teach us about Pattern Recognition
          1. Bayes’ Theorem
          2. Tsukiji Market
        4. Pattern Recognition
          1. Rochester Institute of Technology
          2. A Method for Recognizing Patterns
        5. Elements of Success Rhyme (Continued)
      2. Chapter 11: Why Patterns in Big Data Have Emerged
        1. Meatpacking District
        2. Business Models in the Data Era
        3. Data as a Competitive Advantage
        4. Data Improves Existing Products or Services
        5. Data as the Product
          1. Dun & Bradstreet
          2. CoStar
          3. IHS
        6. Meatpacking District (Continued)
      3. Chapter 12: Patterns in Big Data
        1. The Data Factor
        2. Summary of Big Data Patterns
          1. Redefining a Skilled Worker
          2. Creating and Utilizing New Sources of Data
          3. Building New Data Applications
          4. Transforming and Creating New Business Processes
          5. Data Collection for Competitive Advantage
          6. Exposing Opinion-Based Biases
          7. Real-Time Monitoring and Decision Making
          8. Social Networks Leveraging and Creating Data
          9. Deconstructing the Value Chain
          10. New Product Offerings
          11. Building for Customers Instead of Markets
          12. Tradeoff Between Privacy and Insight
          13. Changing the Definition of a Product
          14. Inverting the Search Paradigm for Data Discovery
          15. Data Security
          16. New Partnerships Founded on Data
          17. Shortening the Innovation Lifecycle
          18. Defining New Channels to Market
          19. New Economic Models
          20. Forecasting and Predicting Future Events
          21. Changing Incentives
          22. New Partnerships (Public/Private)
          23. Real-Time Monitoring and Decision Making (Early Warning Systems)
        3. A Framework for Big Data Patterns
    5. Part III: Leading the Revolution
      1. Chapter 13: The Data Opportunity
        1. What Oil Teaches Us About Data
        2. Bain Study
        3. Seizing the Opportunity
      2. Chapter 14: Porsche
        1. Rome
        2. Ferdinand Porsche
        3. The Birth of Porsche
        4. The Porsche Sports Car
        5. Porsche Today
        6. Rome (Continued)
      3. Chapter 15: Puma
        1. Herzogenaurach
        2. Advertising Wars
        3. Jochen Zeitz
        4. Environmental Profit and Loss
        5. Herzogenaurach (Continued)
      4. Chapter 16: Patterns in Big Data
        1. Introduction
        2. The Method
        3. Step 1: Understand Data Assets
          1. The Patterns
        4. Step 2: Explore Data
          1. Challenges
          2. Questions
          3. Hypotheses
          4. Data
          5. Models
          6. Statistical Significance
        5. Step 3: Design the Future
          1. The Patterns
        6. Step 4: Design a Data-Driven Business Model
          1. The Patterns
        7. Step 5: Transform Business Processes for the Data Era
          1. The Patterns
        8. Step 6: Design for Governance and Security
          1. The Patterns
        9. Step 7: Share Metrics and Incentives
      5. Chapter 17: Big Data Architecture
        1. Introduction
        2. Architect for the Future
        3. Lessons from Stuttgart
          1. Big Data Reference Architectures
        4. Leveraging Investments in Architecture
        5. Big Data Reference Architectures
          1. Business View
          2. Logical View
      6. Chapter 18: Business View Reference Architecture
        1. Introduction
        2. Men’s Trunk: a Retailer in the Data Era
        3. The Business View Reference Architecture
          1. Answer Fabric
          2. Data Virtualization
          3. Data Engines
          4. Management
          5. Data Governance
          6. User Interface, Applications, and Business Processes
        4. Summary
      7. Chapter 19: Logical View Reference Architecture
        1. Introduction
        2. Men’s Trunk: a Retailer in the Data Era (Continued)
        3. The Logical View Reference Architecture
        4. Data Ingest
        5. Analytics
          1. Discovery
          2. Landing
          3. Operational Warehouse
        6. Information Insight
        7. Operational Data
        8. Governance
        9. Men’s Trunk: a Retailer in the Data Era (Continued)
      8. Chapter 20: The Architecture of the Future
        1. Men’s Trunk: a Retailer in the Data Era (Continued)
        2. Men’s Trunk: Applying the Methodology
          1. Step 1: Understand Data Assets
          2. Step 2: Explore the Data
          3. Step 3: Design the Future
          4. Step 4: Design a Data-Driven Business Model
          5. Step 5: Transform Business Processes for the Data Era
          6. Step 6: Design for Governance and Security
          7. Step 7: Share Metrics and Incentives
        3. Men’s Trunk: the Business View Reference Architecture
          1. Answer Fabric
          2. Data Virtualization
          3. Data Engines
          4. Management
          5. Data Governance
          6. User Interface, Applications, and Business Processes
        4. Men’s Trunk: the Logical View Reference Architecture
          1. Approach
        5. Men’s Trunk: a Retailer in the Data Era (Continued)
    6. Epilogue
      1. The Time is Now
      2. Taking Action
      3. Fear not Usual Competitors
      4. The Future
    7. End User License Agreement