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The Visual Imperative

Book Description

Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today’s most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too.

The world of data is changing fast. And, it’s becoming more visual.

Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets.



  • Discusses several years of in-depth industry research and presents vendor tools, approaches, and methodologies in discovery, visualization, and visual analytics
  • Provides practicable and use case-based experience from advisory work with Fortune 100 and 500 companies across multiple verticals
  • Presents the next-generation of visual discovery, data storytelling, and the Five Steps to Data Storytelling with Visualization
  • Explains the Convergence of Visual Analytics and Visual discovery, including how to use tools such as R in statistical and analytic modeling
  • Covers emerging technologies such as streaming visualization in the IOT (Internet of Things) and streaming animation

Table of Contents

  1. Cover
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Foreword
  7. Introduction
  8. About the Author
  9. Acknowledgments
  10. Part I: How Data is Driving Business Disruption, Transformation, and Reinvention
    1. Introduction
    2. Chapter 1: Separating leaders from laggards
      1. Abstract
      2. 1.1. An era of disruption, transformation, and reinvention
      3. 1.2. Characteristics of data-centric companies
      4. 1.3. Examples of data-driven companies
      5. 1.4. From the data to the dashboard
      6. 1.5. Navigating this book
    3. Chapter 2: Improved agility and insights through (visual) discovery
      1. Abstract
      2. 2.1. The discovery imperative
      3. 2.2. Business intelligence versus data discovery
      4. 2.3. The business impact of the discovery culture
      5. 2.4. The role of friction in discovery
      6. 2.5. The four forms of discovery
      7. 2.6. SQL: The language of discovery
      8. 2.7. Discovery in the days to come
    4. Chapter 3: From self-service to self-sufficiency
      1. Abstract
      2. 3.1. From self-service to self-sufficiency
      3. 3.2. Discovery capabilities for self-sufficiency
      4. 3.3. (Information) enablement technology
      5. 3.4. The data scientist: the sexiest of them all
      6. 3.5. Moving forward
    5. Chapter 4: Navigating ethics in the big data democracy
      1. Abstract
      2. 4.1. Introduction
      3. 4.2. Aftershocks of the big data revolution
      4. 4.3. Big data’s personal impact
      5. 4.4. Exercise: ethical data scenarios
      6. 4.5. Data ethics in the news
      7. 4.6. The data visualization hippocratic oath
      8. 4.7. Ethics requires leadership
      9. 4.8. Closing thoughts
    6. Chapter 5: The data science education and leadership landscape
      1. Abstract
      2. 5.1. Introduction
      3. 5.2. The new data science student
      4. 5.3. The new digital native
      5. 5.4. Addressing the gender gap—women in IT
      6. 5.5. New roles for new leaders
      7. 5.6. The steps beyond
  11. Part II: Communicating Data Visually
    1. Introduction
    2. Chapter 6: Visual communication and literacy
      1. Abstract
      2. 6.1. The history of visual communication
      3. 6.2. Visual memorability and retention
      4. 6.3. Extending visual literacy to visual data literacy
      5. 6.4. From communication to conversation
    3. Chapter 7: Visual storytelling with data
      1. Abstract
      2. 7.1. The storytelling paradigm: defining the data story
      3. 7.2. A brief bit of story psychology
      4. 7.3. The data narrative framework
      5. 7.4. Five steps to data storytelling with visualization
      6. 7.5. Storytelling reconsidered, the probability problem
      7. 7.6. Storytelling’s secret ingredient: the audience
    4. Chapter 8: The importance of visual design
      1. Abstract
      2. 8.1. Data visualization is a beautiful science
      3. 8.2. Key cognitive elements in visualization
      4. 8.3. Color 101
      5. 8.4. Visual design building blocks
      6. 8.5. Visualization constraints
    5. Chapter 9: The data visualization continuum
      1. Abstract
      2. 9.1. Data visualization definition
      3. 9.2. The power of viz
      4. 9.3. To explain or explore—or both?
      5. 9.4. The rise of the infographic
      6. 9.5. Choosing the chart: a quick diagnostic
      7. 9.6. Data visualization golden rules
      8. 9.7. Conclusions
  12. Part III: Building the Foundation for Data Visualization
    1. Introduction
    2. Chapter 10: Architecting for discovery
      1. Abstract
      2. 10.1. Key information challenges for data discovery
      3. 10.2. Tackling today’s information challenges
      4. 10.3. Designing for frictionless
      5. 10.4. Enabling governed data discovery
      6. 10.5. Conclusions
      7. 10.6. Anatomy of a visual discovery application
      8. 10.7. The convergence of visual analytics and visual discovery
    3. Chapter 11: Data visualization as a core competency
      1. Abstract
      2. 11.1. Data visualization business benefits and risks
      3. 11.2. Introducing the data visualization competency center
      4. 11.3. Tools for the DVCC
      5. 11.4. Socializing the DVCC
      6. 11.5. The implementation roadmap
      7. 11.6. Four tips to DVCC success
    4. Chapter 12: Visual discovery by design
      1. Abstract
      2. 12.1. Data discovery by design
      3. 12.2. Leveraging visualization for assessable, meaningful, actionable
      4. 12.3. Three key points to a useful data visualization
    5. Chapter 13: The need for visualization in the Internet of Things
      1. Abstract
      2. 13.1. Understanding “data in motion”
      3. 13.2. The internet of things primer
      4. 13.3. The art of animation
      5. 13.4. Human centered design in mobile(first) strategies
      6. 13.5. Interaction for playful data visualization
  13. Appendix
  14. Subject Index