Communicating with Data

Table of contents

1. Preface
   1. Why I Wrote This Book
   2. Who Is This Book For?
   3. How the Book Is Organized
   4. Conventions Used in This Book
   5. O’Reilly Online Learning
   6. How to Contact Us
   7. Acknowledgments
2. I. Communication and Data
3. 1. Communication
   1. What Is Communication?
      1. The Communication Process
      2. Getting Through to Your Audience: Context and Noise
      3. Don’t Forget About Memory
   2. Why Visualize Data?
      1. Pre-Attentive Attributes in Action
      2. Unique Considerations
   3. Summary
4. 2. Data
   1. What Is Data?
      1. Key Features of Data
      2. Rows and Columns
      3. Data Types
   2. How Is Data Created?
      1. Where Is Data Created?
      2. Should You Trust Your Data?
   3. Data as a Resource
      1. Files
      2. Databases, Data Servers, and Lakes
      3. Application Programming Interfaces
      4. Data Security and Ethics
   4. Easy or Hard? The “Right” Data Structure
      1. The Shape of Data
      2. Cleaning Data
   5. The “Right” Data
      1. Requirement Gathering
      2. Use of the Data
   6. Summary
5. II. The Elements of Data Visualization
6. 3. Visualizing Data
   1. Tables
      1. How to Read Tables
      2. How to Optimize Tables
      3. When You Might Not Use Tables
   2. Bar Charts
      1. How to Read Bar Charts
      2. How to Optimize Bar Charts
      3. When You Might Not Want to Use Bar Charts
   3. Line Charts
      1. How to Read Line Charts
      2. How to Optimize Line Charts
      3. When You Might Not Use Line Charts
   4. Summary
7. 4. Visualizing Data Differently
   1. Chart Types: Scatterplots
      1. How to Read Scatterplots
      2. How to Optimize Scatterplots
      3. When to Avoid Scatterplots
   2. Chart Types: Maps
      1. How to Read Maps
      2. How to Optimize Maps
      3. When to Avoid Maps
   3. Chart Types: Part-to-Whole
      1. How to Read Part-to-Whole Charts
      2. When to Use Part-to-Whole Charts
      3. When to Avoid Part-to-Whole Charts
   4. Summary
8. 5. Visual Elements
   1. Color
      1. Types of Color Palettes
      2. Choosing the “Right” Color
      3. Avoiding Unnecessary Use of Color: Double Encoding
   2. Size and Shape
      1. Themed Charts
      2. Size and Shape Challenges
   3. Multiple Axes
   4. Reference Lines/Bands
      1. Reference Lines
      2. Reference Bands
   5. Totals/Summaries
      1. Totals in Tables
      2. Totals in Charts
   6. Summary
9. 6. Visual Context
   1. Titles
      1. Main Title
      2. Subtitles, Standfirsts, and Chart Titles
   2. Text and Annotations
      1. Annotations
      2. Text Boxes
      3. Text Formatting
   3. Contextual Numbers
   4. Legends
      1. Shape Legends
      2. Color Legends
      3. Size Legends
   5. Iconography and Visual Cues
      1. Thematic Iconography
      2. Audience Guidance
   6. Background and Positioning
      1. The Z Pattern
      2. Whitespace
   7. Interactivity
      1. Tooltips
      2. Interactions
   8. Summary
10. 7. The Medium for the Message: Complex and Interactive Data Communication
    1. Explanatory Communications
       1. Gathering Requirements
       2. Updating Data in Explanatory Views
       3. So What?
    2. Exploratory Communications
       1. Gathering Requirements
       2. Flexibility and Flow
    3. Methods: Dashboards
       1. Monitoring Conditions
       2. Facilitating Understanding
    4. Methods: Infographics
    5. Methods: Slide Presentations
    6. Methods: Notes and Emails
    7. Summary
11. III. Deploying Data Communication in the Workplace
12. 8. Implementation Strategies for Your Workplace
    1. Tables Versus Pretty Pictures
       1. Data Culture
       2. Data Literacy
       3. Improving the Visualization Mix
    2. Static Versus Interactive
       1. Let’s Talk About PowerPoint
       2. More Than Just PowerPoint
       3. Interactive User Experience
    3. Centralized Versus Decentralized Data Teams
       1. The Data Team
       2. Data Sources
       3. Reporting
       4. Pooling Data Expertise
       5. Self-Service
    4. Live Versus Extracted Data
       1. Live Data
       2. Extracted Data Sets
    5. Standardization Versus Innovation
       1. Importance of Standardization
       2. Importance of Innovation
    6. Reporting Versus Analytics
       1. Reporting: Mass Production
       2. Analytics: Flexibility but Uncertainty
    7. Finding the “Perfect” Balance
    8. Summary
13. 9. Tailoring Your Work to Specific Departments
    1. The Executive Team
    2. Finance
    3. Human Resources
    4. Operations
    5. Marketing
    6. Sales
    7. Information Technology
    8. Summary
14. 10. Next Steps
    1. Step 1: Get Inspired
    2. Step 2: Practice
    3. Step 3: Keep Reading
15. Index