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Data Insights

Book Description

Data Insights: New Ways to Visualize and Make Sense of Data offers thought-provoking insights into how visualization can foster a clearer and more comprehensive understanding of data. The book offers perspectives from people with different backgrounds, including data scientists, statisticians, painters, and writers. It argues that all data is useless, or misleading, if we do not know what it means.
Organized into seven chapters, the book explores some of the ways that data visualization and other emerging approaches can make data meaningful and therefore useful. It also discusses some fundamental ideas and basic questions in the data lifecycle; the process of interactions between people, data, and displays that lead to better questions and more useful answers; and the fundamentals, origins, and purposes of the basic building blocks that are used in data visualization. The reader is introduced to tried and true approaches to understanding users in the context of user interface design, how communications can get distorted, and how data visualization is related to thinking machines. Finally, the book looks at the future of data visualization by assessing its strengths and weaknesses. Case studies from business analytics, healthcare, network monitoring, security, and games, among others, as well as illustrations, thought-provoking quotes, and real-world examples are included.
This book will prove useful to computer professionals, technical marketing professionals, content strategists, Web and product designers, and researchers.
  • Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data
  • Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations
  • Makes the case that data visualization is not just about technology; it also involves a deeply human process

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Preface
    1. Sandboxes and Museum Cases
  7. Acknowledgments
  8. About the Author
  9. Chapter 1. From Terabytes to Insights
    1. Introduction: A Grander View
    2. Things That Make Us Smarter: How Thoughtful Visualizations can make our Lives Better
    3. Don’t Be Afraid of the Chart
    4. PEER AT the World of Data
    5. From Data to Wisdom
    6. A Day with Data
    7. The Torrents and Trickles of Data: Observations from Statistician John Bosley
    8. Cascades of Confusion
    9. The Data Lifecycle
    10. “Just the Facts”: What are Data and Metadata?
    11. What to Leave in and What to Leave Out: A Conversation with Journalism Professor and Tech Entrepreneur, Len Sellers
    12. What Counts?
    13. The Ripple Effect
    14. Attributes of Data Visualization
    15. Diving into the Well of Machine Data with Splunk Cio Doug Harr
    16. Deep Simplicity (Complex Data in Simple Forms)
    17. What’s New?!
  10. Chapter 2. A More Beautiful Question
    1. The Art of Inquiry
    2. Good Question
    3. Washington, D.C.’s 1100 Points of Light
    4. Twenty Questions
    5. Patterns, Contexts, and Questions
    6. Designing Software for When You Don’t Know What You Don’t Know
    7. The Questions within a Question
    8. Knowing What You’ve Got
    9. Questions and Metadata
    10. Quick Questions
    11. Finding “Personal Bests” (Based on Work by Information Scientist, Dan Gillman)
    12. Lowering the “Curiosity Tax” for Businesses
    13. Asking Good Questions
    14. Approaching Data with a Beginner’s Mind
    15. TMD (Too Much Data)?
    16. A More Beautiful Question
  11. Chapter 3. Winning Combinations: Working with the Ingredients of Data Visualization
    1. Just the Right Mix
    2. Counter intelligence: Figuring Out what to do with Many Ingredients
    3. Setting the Table
    4. Part One: Selecting, Storing, and Combining the Ingredients of Data
    5. Part Two: Fitting Data Types with Visual Forms
    6. Color!
    7. Part Three: Putting Together Different Kinds of Visualizations
    8. A Winning Collaboration on a Small Scale
    9. Conclusion
  12. Chapter 4. Pathways, Purposes, and Points of View
    1. Along These Lines…
    2. Following Strands of Data
    3. On the Road Again
    4. Tangible and Intangible Pathways
    5. Pathway and Process
    6. Finding Rare Birds in Dense Jungles: A Packing List
    7. Time Travel, Tracks, and Wakes: Visualizing Flux and Data
    8. Data and the Narrative Path
    9. Crossing Points and Graph Visualizations
    10. Bridges, Networks, and Roles
    11. Dots with Advanced Degrees
    12. More than Dots on a Map (Perspectives from Ushahidi’s Patrick Meier)
    13. More than Just Data Points
    14. Points to Remember
  13. Chapter 5. Views You Can Use
    1. Getting Through
    2. Perceiving the Gray Areas
    3. It All Depends on Your Perspective
    4. Data Models versus User Models
    5. Enabling versus Imposing Mental Models
    6. User Experience Design and Making Sense of Data
    7. Can you spare some change?
    8. Learned Interfaces versus Intuitive Interfaces
    9. T2—Technology × Training
    10. Left to Your Own Devices
    11. Baseballs, Bassoons, and Virtuosity
    12. Sound Advice from HCI and Shneiderman’s “Golden Rules”
    13. Usability and Data Visualization
    14. Looking at User-Driven Design With Tom Sawyer Software Ceo Brendan Madden
    15. A Look at the Landscape: Color and Composition With Artist Walt Bartman
    16. In Review...
  14. Chapter 6. Thinking … Machines
    1. The Yin-Yang of Data Analysis
    2. Scaling-Up Analysis: Knowledge, Data Mining, Machine Learning
    3. Entering the Mine
    4. Manual and Automatic
    5. Automated Observations and Human Hypotheses
    6. Black Boxes, Toy Boxes, and Out of the Box
    7. Gut-Checks and Algorithms
    8. Consternation About Correlation and Causation
    9. The Shape of Things to Come?
    10. Knowing How to Fold ’Em
    11. Multiple Intelligences and Data Visualization
    12. Questions, Ideas, Insights
    13. Humans, Computers, and Collaborations
    14. The Sum of the Parts
  15. Chapter 7. Hindsight, Foresight, and Insight
    1. Part 1—Adapting to Data
    2. Keeping Pace
    3. Part 2—New Dimensions in Data Visualization
    4. Part 3—Real Time
    5. The Elephant in the Room
    6. Border Crossings...
  16. Resources
  17. References
  18. Index