In the digital age, data is the new currency. However, amassing heaps of data means nothing if it doesn't lead to actionable insights. It's not enough to just present numbers; to truly resonate with an audience, data needs a narrative. Data Storytelling and Translation bridges the chasm between numbers and narratives. Learn the intricacies of translating raw data into compelling stories that captivate, inform, and inspire action. The book covers proven frameworks for converting data into compelling narratives, strategies to tailor data stories to different audiences, techniques to avoid common pitfalls and biases in data representation, the balance between aesthetics and accuracy in data visualization, and uses real-world case studies illustrating the power of effective data storytelling. Whether you're a data scientist, business analyst, student or a decision-maker, this book offers the tools to articulate the true value of your data.
FEATURES
Covers topics such as translating metrics, identifying your audience, designing your data story, and more
Combines the topics of data translating and data storytelling into a single forum that are critical to effective data presentations
Includes companion files for both educators and team leaders with slides and videos
Author(s): David Mathias
Publisher: Mercury Learning and Information
Year: 2023
Language: English
Pages: 207
Cover
Title
Copyrightpage
Contents
Prologue
Acknowledgments
Chapter 1 The Age of the Data Translator
Curiosity
Empathy
Trustworthiness
Who is This Book For?
How This Book is Laid Out and What to Expect
Chapter 2 All Decisions Start With People
Start Understanding People by Looking Inward First
Understanding Incentives and Biases
Understanding, Engaging, and Communicatin With Your Customer
Sample Survey from Talent Management to Hiring Manager Customer
References
Chapter 3 Start With Good Questions and Great Listening
The Importance of Good Questions
The Definition of a Good Question
How to Ask Good Questions
Asking the Right Customer
Create the Right Setting
Asking in the Right Time and Place
Defuse With Your Questions
Body Language and Tone
Listening: Being Heard by Being a Great Listener
Focus
Understand
Respond
References
Chapter 4 Being Fluent in the Language of Data
Everything Starts With Understanding the Data
Structured versus Unstructured Data
Categorical versus Numerical Data
Clean versus Messy Data
Statistics Is the Language of Understanding Data
Descriptive Statistics
Central Tendency
Variability
Correlation
Inferential Statistics
The Superpower of Analytics and Data Science
Types of Analytics
Foundational Analytics Concepts
Artificial Intelligence
Machine Learning
Specific versus General Artificial Intelligence
Classification versus Regression
Supervised versus Unsupervised Learning
It’s All About the Data
Data and Analytics as Services
The Tension Between Transparency and Performance
Perpetual Model Bias
References
Chapter 5 Identify, Understand, and Frame Problems
Identifying Problems Means Understanding Pain
Problem-Ask-Value Framework
Understand the Problem
Understand the Question
Understand the Value
Reframing Problems
References
Chapter 6 Simplifying Insights Through Metrics and Objectives
What Is the Purpose?
Communicate Priorities
Align People and Processes
Show Progress
Motivate Behavior
Define Expectations
Reduce Uncertainty
Leading and Lagging Metrics
Efficiency, Effectiveness, and Outcome Metrics
Upward and Downward Metrics
Who is the Audience?
Sales Activity Metric Example
Customer Experience Metric Example
How Do You Communicate?
Initial Communication
Ongoing Communication
Who Is the Target?
Accuracy versus Precision
Operationalizing Metrics
References
Chapter 7 Painting Your Data Story
Data Story Canvas Introduction
Data Story Topic
Delivering Your Data Story
The Audience
The Existing Narrative
What They Need to Know
The Hook
Keep: Holding Their Attention
Compel: The Call to Action
The Data Source
The Tradeoffs
Your Confidence
Data Story Canvas Example
Chapter 8 everaging Visuals to Share
Insights and Compel Action
The Purpose of Data Visualization
Exploratory Data Visualization
Data Visualization as Storytelling
Principles of Good Data Visualization
Picking the Right Chart
Tables Are Not Evil
Harnessing the Power of Size, Angle, and Position
Size
Angle
Position
The Power of Color
Color Usage
Context Correct Color
Color Consistency
Number of Different Colors
Intensity of Color
Categorical versus Continuous Color
Colorblind-Friendly
Text in a Data Visualization
Summaries
Titles
Legends
Axis Labels
Data Labels
Annotations
Consistency
Format
Source
Trends and References
Don’t Overdo It
Gestalt Principles
Moving Beyond Design and Communicating Data Visualizations
Prioritize the Meaning
Ask Questions to Engage
Get Second and Third Opinions
Avoid Check-the-Box Visualizations
Layer Your Visualization
Show Your Work and Get Detailed
Build Trust Through Data Visualization
References
Chapter 9 Leveraging Dashboards
in Your Communication
Dashboard Best Practices
Provide the What, Why, and Now What
Be Consistent
Follow the Z-Pattern
Balance Interactivity
Don’t Shy Away From Text
Make Sure the Data Source is Obvious
Defaults Matter
Dashboard Lifecycle
Beginning
Middle
End
Dashboards and Storytelling
References
Chapter 10 Communicating Your Data Story
An Introduction to the Data Story Checklist
Be Authentically You
Test and Verify
Be Vulnerable
Eliminate Roadblocks in Advance
Engage Often and Early
Be Transparent and Ethical
Be Confident and Humble
Be Prepared to Improvise
Lead With a Story Backed by Data and Visuals
Consider the Right Person
Data Story Checklist
Developing Your Communication Skills
Meetup Groups / Professional Association
Contributing Author
Improvisational Theater
Toastmasters International
Conclusion
References
Epilogue
Top 20 Podcasts for Data Translators
Top 20 Books for Data Translators
Index
