Data Insights

Data Insights

New Ways to Visualize and Make Sense of Data

1st Edition - November 13, 2012

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  • Author: Hunter Whitney
  • Paperback ISBN: 9780123877932
  • eBook ISBN: 9780123877949

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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.

Key Features

  • 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


Technical marketing professionals and researchers, content strategists, web and product designers, computer professionals and researchers that need to present data in an easy to understand way

Table of Contents

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

Product details

  • No. of pages: 320
  • Language: English
  • Copyright: © Morgan Kaufmann 2012
  • Published: November 13, 2012
  • Imprint: Morgan Kaufmann
  • Paperback ISBN: 9780123877932
  • eBook ISBN: 9780123877949

About the Author

Hunter Whitney

Hunter Whitney
Hunter Whitney is a User Experience (UX) Designer who has helped create useful and usable interface designs for clients in areas ranging from bioscience and medicine to information technology and marine biology. In addition to his UX work, he has written numerous articles about a range of subjects, including data visualization, for various online and print publications. His aim is to encourage conversations among people with diverse skills and perspectives about presenting data in ways that are more widely accessible and engaging. He received dual bachelor’s degrees—one in English Literature from UCLA and the other in Biology from UCSC—and has completed post-graduate neuropsychology research at UCLA. The combination of these multidisciplinary studies reflects his longstanding interest in the intersection between the humanities and the sciences (

Affiliations and Expertise

User Experience (UX) Designer who has helped create useful and usable interface designs for clients in areas ranging from bioscience and medicine to information technology and marine biology

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