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Win with Advanced Business Analytics: Creating Business Value from Your Data

Book Description

Plain English guidance for strategic business analytics and big data implementation

In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice.

  • Provides the essential concept and framework to implement business analytics

  • Written clearly for a nontechnical audience

  • Filled with case studies across a variety of industries

  • Uniquely focuses on integrating multiple types of big data intelligence into your business

Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

Table of Contents

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Chapter 1: The Challenge of Business Analytics
    1. The Challenge from Outside
    2. The Challenge from Within
  9. Chapter 2: Pillars of Business Analytics Success
    1. Business Challenges Pillar
    2. Data Foundation Pillar
    3. Analytics Implementation Pillar
    4. Insight Pillar
    5. Execution and Measurement Pillar
    6. Distributed Knowledge Pillar
    7. Innovation Pillar
    8. Conclusion
  10. Chapter 3: Aligning Key Business Challenges across the Enterprise
    1. Mission Statement
    2. Business Challenge
    3. Identifying Business Challenges as a Consultative Process
    4. Identify and Prioritize Business Challenges
    5. Analytics Solutions for Business Challenges
  11. Chapter 4: Big and Little Data
    1. Big Data
    2. Little Data
    3. Laying the Data Foundation: Data Quality
    4. Data Sources and Locations
    5. Data Definition and Governance
    6. Data Dictionary and Data Key Users
    7. Sanity Check and Data Visualization
    8. Customer Data Integration and Data Management
    9. Data Privacy
  12. Chapter 5: Who Cares about Data?
    1. The IMPACT Cycle
    2. Curiosity Can Kill the Cat
    3. Master the Data
    4. A Fact in Search of Meaning
    5. Actions Speak Louder Than Data
    6. “Eat Like a Bird, Poop Like an Elephant”
    7. Track Your Outcomes
    8. The IMPACT Cycle in Action: The Monster Employment Index
  13. Chapter 6: Data Visualization
    1. Convey Meaning
    2. Objectivity: Be True to Your Data
    3. Necessity: Don’t Boil the Ocean
    4. Visual Honesty: Size Matters
    5. Imagine the Audience
    6. Nimble: No Death by 1,000 Graphs
    7. Context
    8. Encourage Interaction
    9. Conclusion
  14. Chapter 7: Analytics Implementation
    1. Analytics Implementation Model
    2. Vision and Mandate
    3. Strategy
    4. Organizational Collaboration
    5. Human Capital
    6. Metrics and Measurement
    7. Integrated Processes
    8. Customer Experience
    9. Technology and Tools
    10. Change Management
  15. Chapter 8: Voice-of-the-Customer Analytics and Insights
    1. Customer Feedback Is Invaluable
    2. The Makings of an Effective Voice-of-the-Customer Program
    3. Strategy and Elements of the VOC System
    4. Common VOC Program Pitfalls
  16. Chapter 9: Leveraging Digital Analytics Effectively
    1. Strategic and Tactical Use of Digital Analytics
    2. Understanding Digital Analytics Concepts
    3. Digital Analytics Team: People Are Most Important for Analytical Success
    4. Digital Analytics Tools
    5. Advanced Digital Analytics
    6. Digital Analytics and Voice of the Customer
    7. Analytics of Site and Landing Page Optimization
    8. Call to Action: Unify Traditional and Digital Analytics
  17. Chapter 10: Effective Predictive Analytics
    1. What Is Predictive Analytics?
    2. Unlocking Stage
    3. Prediction Stage
    4. Optimization Stage
    5. Diverse Applications for Diverse Business Problems
    6. Financial Service Industries as Pioneers
  18. Chapter 11: Predictive Analytics Applied to Human Resources
    1. Staff Roles
    2. Assessment: Beyond People
    3. Planning Shift
    4. Competency versus Capability
    5. Production
    6. HR Process Management
    7. HR Analysis and Predictability
    8. Elevate HR with Analytics
    9. Value Hierarchy
    10. HR Reporting
    11. HR Success through Analytics
  19. Chapter 12: Social Media Analytics
    1. Social Media Is Multidimensional
    2. Understanding Social Media Analytics: Useful Concepts
    3. Is Social Media about Brand or Direct Response?
    4. Social Media “Brand” and “Direct Response” Analytics
    5. Social Media Tools
    6. Social Media Analytical Techniques
    7. Social Media Analytics and Privacy
  20. Chapter 13: The Competitive Intelligence Mandate
    1. Competitive Intelligence Defined
    2. Principles for CI Success
  21. Chapter 14: Mobile Analytics
    1. Understanding Mobile Analytics Concepts
    2. How Is Mobile Analytics Different from Site Analytics?
    3. Importance of Measuring Mobile Analytics
    4. Mobile Analytics Tools
    5. Business Optimization with Mobile Analytics
  22. Chapter 15: Effective Analytics Communication Strategies
    1. Communication: The Gap between Analysts and Executives
    2. An Effective Analytics Communication Strategy
    3. Analytics Communication Tips
    4. Communicating through Mobile Business Intelligence
  23. Chapter 16: Business Performance Tracking
    1. Analytics’ Fundamental Questions
    2. Analytics Execution
    3. Business Performance Tracking
    4. Analytics and Marketing
  24. Chapter 17: Analytics and Innovation
    1. What Is Innovation?
    2. What Is the Promise of Advanced Analytics?
    3. What Makes Up Innovation in Analytics?
    4. Intersection between Analytics and Innovation
  25. Chapter 18: Unstructured Data Analytics
    1. What Is Unstructured Data Analytics?
    2. The Unstructured Data Analytics Industry
    3. Uses of Unstructured Data Analytics
    4. How Unstructured Data Analytics Works
    5. Why Unstructured Data Is the Next Analytical Frontier
    6. Unstructured Analytics Success Stories
  26. Chapter 19: The Future of Analytics
    1. Data Become Less Valuable
    2. Predictive Becomes the New Standard
    3. Social Information Processing and Distributed Computing
    4. Advances in Machine Learning
    5. Traditional Data Models Evolve
    6. Analytics Becomes More Accessible to the Nonanalyst
    7. Data Science Becomes a Specialized Department
    8. Human-Centered Computing
    9. Analytics to Solve Social Problems
    10. Location-Based Data Explosion
    11. Data Privacy Backlash
  27. About the Authors
  28. Index