New
Data Science: The Hard Parts
Techniques for Thinking Analytically and Solving Real Data Problems
2023
View →
Pages
347
Published
2025
Data Analytics
✨ New
Storytelling with Data
A Practical Guide to Communicating Effectively with Data Visualizations and Charts
Turn raw numbers into clear, persuasive visuals that make your audience act — not just nod.
Most data presentations fail not because of bad data, but because the charts are cluttered, the story is buried, and the audience leaves confused. Storytelling with Data by Cole Nussbaumer Knaflic teaches you to choose the right chart, strip away visual noise, and build a narrative that drives decisions. Whether you work in analytics, finance, marketing, or operations, this book gives you a repeatable process for turning any dataset into a clear, compelling visual argument.
About this book
You have done the analysis. The numbers are solid. But when you present them, the room glazes over, the decision stalls, and your work gets ignored. The problem is almost never the data — it is how the data is shown.
Storytelling with Data gives you a disciplined, practical process for turning spreadsheets and query results into visuals that communicate a single, unmistakable point. Cole Nussbaumer Knaflic draws on years of experience teaching data visualization at Google and beyond to show you exactly where most charts go wrong — and how to fix them.
The book is built around a repeatable six-part framework: understand the context, choose the right chart type, eliminate visual clutter, direct attention where it matters, think like a designer, and tell a story. Each principle is illustrated with real before-and-after makeovers of actual business charts, so you can see the transformation, not just read about it.
You will learn why bar charts are almost always the right default, why pie charts rarely are, and how to use preattentive attributes — color, size, position — to make your most important number impossible to miss. You will also learn how to frame data in narrative terms: context, tension, resolution. That structure is what separates a dashboard your boss closes immediately from one that drives a budget decision.
- Choose the chart type that matches your comparison, not just your software's defaults
- Declutter any visual by applying a clear hierarchy of what to remove first
- Use color sparingly and purposefully to highlight a single insight
- Write titles and annotations that tell the audience what to conclude, not just what to see
- Adapt your communication style to the audience, from executive summary to detailed analyst report
At 347 pages, the book is dense with worked examples but never academic. Every chapter ends with exercises you can apply to your own data immediately. If you produce charts for any business audience — in a slide deck, a report, a dashboard, or an email — this book will change how you work.
🎯 What you'll learn
- Identify the right chart type for any comparison, distribution, relationship, or composition question
- Strip visual clutter from any chart using a clear, prioritized removal process
- Direct audience attention using preattentive attributes — color, size, and spatial position — without adding decoration
- Write chart titles and callout annotations that state the conclusion, not just the variable
- Structure a data presentation as a narrative with context, tension, and resolution
- Tailor the level of detail and chart complexity to a given audience and communication format
- Rebuild real-world charts from scratch using before-and-after makeovers as a learning template
👤 Who is this book for?
- Data analysts who produce technically correct charts that consistently fail to persuade stakeholders
- Business intelligence and reporting professionals who want a structured method for designing dashboards that get read
- Finance, marketing, or operations professionals who present data regularly but have never had formal training in visualization
- Managers and consultants who need to communicate findings to executives and need their visuals to carry the argument
- Students and early-career analysts building a foundation in data communication before picking up a BI tool
Table of contents
-
01
The Importance of Context
Learn why understanding your audience, their expectations, and the decision they need to make is the essential first step before touching any chart. You will define the context for a real dataset before moving on.
-
02
Choosing an Effective Visual
Survey the core chart types — bar, line, scatter, table, and more — and develop a decision framework for matching the right visual to the type of comparison your data needs to make.
-
03
Visual Clutter Is Your Enemy
Apply the concept of cognitive load to your charts and work through a systematic process for removing gridlines, borders, data labels, and decorative elements that slow your audience down.
-
04
Focus Your Audience's Attention
Use preattentive attributes — including color, weight, size, and position — to make a single number or trend the unavoidable focal point of any chart without adding visual complexity.
-
05
Think Like a Designer
Adopt four design principles — affordance, accessibility, aesthetics, and acceptance — and apply them to evaluate and improve chart layouts you have produced in the past.
-
06
Dissecting Model Visualizations
Analyze a set of exemplary real-world charts to extract the specific choices that make them work, building a vocabulary you can apply deliberately to your own output.
-
07
Lessons in Storytelling
Frame data findings as a three-act narrative structure and practice writing the setup, conflict, and resolution that transforms a slide of numbers into a persuasive argument.
-
08
Pulling It All Together
Walk through a complete end-to-end makeover of a poorly designed business presentation, applying every principle from the previous chapters to produce a finished, presentation-ready deck.
Frequently asked questions
Do I need programming or statistics experience to get value from this book?
No. The book focuses entirely on visual communication and design thinking, not code or math. You need only basic familiarity with charts — the kind anyone picks up from working with spreadsheets.
Does the book cover specific tools like Tableau, Power BI, or Excel?
The principles are tool-agnostic. Knaflic deliberately avoids tying examples to a single platform so the lessons apply whether you use Excel, Tableau, Python, or any other charting environment.
Is this book suitable for complete beginners to data analysis?
Yes, though it is focused on communication rather than analysis. If you are brand new to working with data, you will still benefit, but you may want to pair it with an introductory analytics resource for the full picture.
Are there exercises or practice datasets included?
Yes. Each chapter includes exercises tied to the concepts covered, and the book directs readers to companion resources online for practice datasets and additional worked examples.
How is this edition different from the original 2015 release?
This 2025 edition reflects updated examples, revised chart makeovers, and new material aligned with how data communication has evolved in modern business environments.
Who is this book not for?
It is not for engineers or scientists who need to build interactive data products or learn a visualization library. It is a communication and design book, not a software or programming manual.
You might also like
New
New
Fundamentals of Data Engineering
A practical guide to the complete data engineering lifecycle, from ingestion to serving
by Joe Reis, Matt Housley
2022
View →
New
Data Analysis with Python and PySpark
A hands-on guide to scalable data analytics using Python and PySpark
2022
View →
New