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Storytelling with Data: A Data Visualization Guide for Business Professionals

Stop drowning in spreadsheets and start building high-impact charts that drive faster decision-making.

(DA-STORY.AE1) / ISBN : 979-8-90059-106-3
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1

Introduction

  • We aren’t naturally good at storytelling with data
  • Who this course is written for
  • How I learned to tell stories with data
  • How you’ll learn to tell stories with data: 6 lessons
  • Illustrative examples span many industries
  • Lessons are not tool specific
  • How this course is organized
2

The Importance Of Context

  • Exploratory vs. explanatory analysis
  • Who, what, and how
  • Who
  • What
  • How
  • Who, what, and how: illustrated by example
  • Consulting for context: questions to ask
  • The 3-minute story & Big Idea
  • Storyboarding
  • In closing
3

Choosing An Effective Visual

  • Simple text
  • Tables
  • Graphs
  • Points
  • Lines
  • Bars
  • Area
  • Other types of graphs
  • To be avoided
  • In closing
4

Clutter Is Your Enemy!

  • Cognitive load
  • Clutter
  • Gestalt principles of visual perception
  • Lack of visual order
  • Non-strategic use of contrast
  • Decluttering: step-by-step
  • In closing
5

Focus Your Audience’s Attention

  • You see with your brain
  • A brief lesson on memory
  • Preattentive attributes signal where to look
  • Size
  • Color
  • Position on page
  • In closing
6

Think Like A Designer

  • Affordances
  • Accessibility
  • Aesthetics
  • Acceptance
  • In closing
7

Dissecting Model Visuals

  • Model visual #1: line graph
  • Model visual #2: annotated line graph with forecast
  • Model visual #3: 100% stacked bars
  • Model visual #4: leveraging positive and negative stacked bars
  • Model visual #5: horizontal stacked bars
  • In closing
8

Lessons In Storytelling

  • The magic of story
  • Constructing the story
  • The narrative structure
  • The power of repetition
  • Tactics to help ensure that your story is clear
  • In closing
9

Pulling It All Together

  • Lesson 1: understand the context
  • Lesson 2: choose an appropriate display
  • Lesson 3: eliminate clutter
  • Lesson 4: draw attention where you want your audience to focus
  • Lesson 5: think like a designer
  • Lesson 6: tell a story
  • In closing
10

Case Studies

  • CASE STUDY 1: Color considerations with a dark background
  • CASE STUDY 2: Leveraging animation in the visuals you present
  • CASE STUDY 3: Logic in order
  • CASE STUDY 4: Strategies for avoiding the spaghetti graph
  • CASE STUDY 5: Alternatives to pies
  • In closing
11

Final Thoughts

  • Where to go from here
  • Building storytelling with data competency in your team or organization
  • Recap: a quick look at all we’ve learned
  • In closing

1

The Importance Of Context

  • Storytelling at the Cafe
2

Choosing An Effective Visual

  • Choosing an Effective Visual
  • Creating and Interpreting a Scatterplot
  • Creating a Line Graph and a Slope Graph
  • Creating a Bar Chart and a Waterfall Chart
3

Clutter Is Your Enemy!

  • Reducing Visual Clutter
4

Focus Your Audience’s Attention

  • Guiding Attention with Visual Signals
5

Think Like A Designer

  • Designing Effective Data Visualizations
6

Dissecting Model Visuals

  • Creating a Storytelling Line Graph
  • Creating an Annotated Line Graph with a Forecast
  • Creating a 100% Stacked Bar Chart
  • Creating a Positive and Negative Stacked Bar Chart
  • Creating a Horizontal Stacked Bar Chart
7

Lessons In Storytelling

  • Turning Data into Storytelling
8

Pulling It All Together

  • Choosing and Simplifying Visuals
9

Case Studies

  • Storytelling with Data
10

Final Thoughts

  • Building Storytelling Capability

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No, the lessons focus on universal principles. You won't master Tableau or Power BI here, but you'll know what good looks like.

Immediate changes in your approach are possible. Real mastery takes consistent practice; it's not a quick fix for deep-seated habits.

The design principles are practical, not artistic. It's about making data consumable, not about creating pretty pictures.

The core ideas apply broadly. However, specific examples lean towards business scenarios, so direct translation might need some thought.

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