Communicating with Data: Making Your Case With Data

Length: 300 pages
Edition: 1
Language: English
Publisher: O'Reilly Media
Publication Date: 2022-01-18
ISBN-10: 1098101855
ISBN-13: 9781098101855
Sales Rank: #0 (See Top 100 Books)

Data is a fantastic raw resource for powering change in an organization, but all too often the people working in those organizations don’t have the necessary skills to communicate with data effectively. With this practical book, subject matter experts will learn ways to develop strong, persuasive points when presenting data to different groups in their organizations.

Author Carl Allchin shows anyone how to find data sources and develop data analytics, and teaches those with more data expertise how to visualize data to convey findings to key business leaders more effectively. Once your business and data experts both possess the skills to work with data and interpret its significance, you can deal with questions and challenges in departments across your organization.

Learn the fundamental data skills required to work with data
Use data visualization to influence change in your organization
Learn how to apply data techniques to effectively work with data end to end
Understand how to communicate data points clearly and persuasively
Appreciate why different stakeholders often have divergent needs and views
Create a playbook for using data with different departments

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