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Book Description

The era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle today's increasing streams, volumes, and types of data.

Amidst all of the chaos, though, a new type of organization is emerging.

In The Visual Organization, award-winning author and technology expert Phil Simon looks at how an increasingly number of organizations are embracing new dataviz tools and, more important, a new mind-set based upon data discovery and exploration. Simon adroitly shows how Amazon, Apple, Facebook, Google, Twitter, and other tech heavyweights use powerful data visualization tools to garner fascinating insights into their businesses. But make no mistake: these companies are hardly alone. Organizations of all types, industries, sizes are representing their data in new and amazing ways. As a result, they are asking better questions and making better business decisions.

Rife with real-world examples and case studies, The Visual Organization is a full-color tour-de-force.

Table of Contents

  1. Cover Page
  2. Additional praise for The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions
  3. Wiley & SAS Business Series
  4. Title Page
  5. Copyright
  6. Other Books by Phil Simon
  7. Dedication
  8. Contents
  9. List of Figures and Tables
  10. Preface: A Tale of Two IPOs
  11. Acknowledgments
  12. How to Help This Book
  13. Part I: Book Overview and Background
    1. Introduction
      1. Adventures in Twitter Data Discovery
      2. Contemporary Dataviz 101
      3. Book Overview
      4. Next
      5. Notes
    2. Chapter 1: The Ascent of the Visual Organization
      1. The Rise of Big Data
      2. Open Data
      3. The Burgeoning Data Ecosystem
      4. The New Web: Visual, Semantic, and API-Driven
      5. Better Data Tools
      6. Greater Organizational Transparency
      7. The Copycat Economy: Monkey See, Monkey Do
      8. Data Journalism and the Nate Silver Effect
      9. Digital Man
      10. Next
      11. Notes
    3. Chapter 2: Transforming Data into Insights: The Tools
      1. Dataviz: Part of an Intelligent and Holistic Strategy
      2. The Tyranny of Terminology: Dataviz, BI, Reporting, Analytics, and KPIs
      3. The Dataviz Fab Five
      4. The Final Word: One Size Doesn't Fit All
      5. Next
      6. Notes
  14. Part II: Introducing the Visual Organization
    1. Chapter 3: The Quintessential Visual Organization
      1. Netflix 1.0: Upsetting the Applecart
      2. Netflix 2.0: Self-Cannibalization
      3. Dataviz: Part of a Holistic Big Data Strategy
      4. Dataviz: Imbued in the Netflix Culture
      5. Lessons
      6. Next
      7. Notes
    2. Chapter 4: Dataviz in the DNA
      1. The Beginnings
      2. UX Is Paramount
      3. The Plumbing
      4. Lessons
      5. Next
      6. Note
    3. Chapter 5: Transparency in Texas
      1. Background
      2. Early Dataviz Efforts
      3. Embracing Traditional BI
      4. Data Discovery
      5. Results
      6. Lessons
      7. Next
      8. Notes
  15. Part III: Getting Started: Becoming a Visual Organization
    1. Chapter 6: The Four-Level Visual Organization Framework
      1. Big Disclaimers
      2. A Simple Model
      3. Next
    2. Chapter 7: WWVOD?
      1. Visualizing the Impact of a Reorg
      2. A Marketing Example
      3. Next
      4. Notes
    3. Chapter 8: Building the Visual Organization
      1. Data Tips and Best Practices
      2. Design Tips and Best Practices
      3. Technology Tips and Best Practices
      4. Management Tips and Best Practices
      5. Next
      6. Notes
    4. Chapter 9: The Inhibitors: Mistakes, Myths, and Challenges
      1. Mistakes
      2. Myths
      3. Challenges
      4. Next
      5. Notes
  16. Part IV: Conclusion and the Future of Dataviz
    1. Coda: We're Just Getting Started
      1. Four Critical Data-Centric Trends
      2. Final Thoughts: Nothing Stops This Train
      3. Notes
  17. Afterword: My Life in Data
  18. Appendix: Supplemental Dataviz Resources
  19. Selected Bibliography
  20. About the Author
  21. Index