storytelling with data
a data visualization guide
for business professionals
cole nussbaumer knaflic
Cover image: Cole Nussbaumer Knaflic
Cover design: Wiley
Copyright © 2015 by Cole Nussbaumer Knaflic. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:
ISBN 9781119002253 (Paperback)
ISBN 9781119002260 (ePDF)
ISBN 9781119002062 (ePub)
To Randolph
Contents
Foreword Note
Acknowledgments
About the Author
Introduction Bad graphs are everywhere
We aren’t naturally good at storytelling with data
Who this book 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 book is organized
Chapter 1 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
Chapter 2 choosing an effective visual Simple text
Tables
Graphs
Points
Lines
Bars
Area
Other types of graphs
To be avoided
In closing
Chapter 3 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
Chapter 4 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
Chapter 5 think like a designer Affordances
Accessibility
Aesthetics
Acceptance
In closing
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
Chapter 7 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
Chapter 8 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
Chapter 9 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
Chapter 10 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
Bibliography
Index
EULA
List of Illustrations
Introduction FIGURE 0.1 A sampling of ineffective graphs
FIGURE 0.2 Example 1 (before): showing data
FIGURE 0.3 Example 1 (after): storytelling with data
FIGURE 0.4 Example 2 (before): showing data
FIGURE 0.5 Example 2 (after): storytelling with data
FIGURE 0.6 Example 3 (before): showing data
FIGURE 0.7 Example 3 (after): storytelling with data
Chapter 1 Figure 1.1 Communication mechanism continuum
Figure 1.2 Example storyboard
Chapter 2 Figure 2.1 The visuals I use most
Figure 2.2 Stay-at-home moms original graph
Figure 2.3 Stay-at-home moms simple text makeover
Figure 2.4 Table borders
Figure 2.5 Two views of the same data
Figure 2.6 Scatterplot
Figure 2.7 Modified scatterplot
Figure 2.8 Line graphs
Figure 2.9 Showing average within a range in a line graph
Figure 2.10 Slopegraph
Figure 2.11 Modified slopegraph
Figure 2.12 Fox News bar chart
Figure 2.13 Bar charts must have a zero baseline
Figure 2.14 Bar width
Figure 2.15 Bar charts
Figure 2.16 Comparing series with stacked bar charts
Figure 2.17 Waterfall chart
Figure 2.18 Horizontal bar charts
Figure 2.19 100% st
acked horizontal bar chart
Figure 2.20 Square area graph
Figure 2.21 Pie chart
Figure 2.22 Pie chart with labeled segments
Figure 2.23 An alternative to the pie chart
Figure 2.24 Donut chart
Figure 2.25 3D column chart
Figure 2.26 Secondary y-axis
Figure 2.27 Strategies for avoiding a secondary y-axis
Chapter 3 Figure 3.1 Gestalt principle of proximity
Figure 3.3 Gestalt principle of similarity
Figure 3.4 You see rows due to similarity of color
Figure 3.5 Gestalt principle of enclosure
Figure 3.6 The shaded area separates the forecast from actual data
Figure 3.7 Gestalt principle of closure
Figure 3.8 The graph still appears complete without the border and background shading
Figure 3.9 Gestalt principle of continuity
Figure 3.10 Graph with y-axis line removed
Figure 3.11 Gestalt principle of connection
Figure 3.12 Lines connect the dots
Figure 3.13 Summary of survey feedback
Figure 3.14 Revamped summary of survey feedback
Figure 3.15 Original graph
Figure 3.16 Revamped graph, using contrast strategically
Figure 3.17 Original graph
Figure 3.18 Remove chart border
Figure 3.19 Remove gridlines
Figure 3.20 Remove data markers
Figure 3.21 Clean up axis labels
Figure 3.22 Label data directly
Figure 3.23 Leverage consistent color
Figure 3.24 Before-and-after
Chapter 4 Figure 4.1 A simplified picture of how you see
Figure 4.2 Count the 3s example
Figure 4.3 Count the 3s example with preattentive attributes
Figure 4.4 Preattentive attributes
Figure 4.5 Preattentive attributes in text
Figure 4.6 Preattentive attributes can help create a visual hierarchy of information
Figure 4.7 Original graph, no preattentive attributes
Figure 4.8 Leverage color to draw attention
Figure 4.9 Create a visual hierarchy of information
Figure 4.10 Let’s revisit the ticket example
Figure 4.11 First, push everything to the background
Figure 4.12 Make the data stand out
Figure 4.13 Too many data labels feels cluttered
Figure 4.14 Data labels used sparingly help draw attention
Figure 4.15 Use color sparingly
Figure 4.16 Color options with brand color
Figure 4.17 The zigzag “z” of taking in information on a screen or page
Chapter 5 Figure 5.1 OXO kitchen gadgets
Figure 5.2 Pew Research Center original graph
Figure 5.3 Highlight the important stuff
Figure 5.4 Eliminate distractions
Figure 5.5 Before-and-after
Figure 5.6 Clear visual hierarchy of information
Figure 5.7 Words used wisely
Figure 5.8 Let’s revisit the ticket example
Figure 5.9 Use words to make the graph accessible
Figure 5.10 Add action title and annotation
Figure 5.11 Method liquid dishwashing soap
Figure 5.12 Unaesthetic design
Figure 5.13 Aesthetic design
Chapter 6 Figure 6.1 Line graph
Figure 6.2 Annotated line graph with forecast
Figure 6.3 100% stacked bars
Figure 6.4 Leveraging positive and negative stacked bars
Figure 6.5 Horizontal stacked bars
Chapter 7 Figure 7.1 Bing, bang, bongo
Figure 7.2 Horizontal logic
Figure 7.3 Vertical logic
Figure 7.4 Reverse storyboarding
Figure 7.5 A fresh perspective
Chapter 8 Figure 8.1 Original visual
Figure 8.2 Remove the variance in color
Figure 8.3 Emphasize 2010 forward
Figure 8.4 Change to line graph
Figure 8.5 Single line graph for all products
Figure 8.6 Eliminate clutter
Figure 8.7 Focus the audience’s attention
Figure 8.8 Refocus the audience’s attention
Figure 8.9 Refocus the audience’s attention again
Figure 8.10 Add text and align elements
Figure 8.11
Figure 8.12
Figure 8.13
Figure 8.14
Figure 8.15
Figure 8.16
Figure 8.17
Figure 8.18
Figure 8.19
Figure 8.20 Before-and-after
Chapter 9 Figure 9.1 Simple graph on white, blue, and black background
Figure 9.2 Initial makeover on white background
Figure 9.3 Remake on dark background
Figure 9.4 Original graph
Figure 9.5
Figure 9.6
Figure 9.7
Figure 9.8
Figure 9.9
Figure 9.10
Figure 9.11
Figure 9.12 User satisfaction, original graph
Figure 9.13 Highlight the positive story
Figure 9.14 Highlight dissatisfaction
Figure 9.15 Focus on unused features
Figure 9.16 Set up the graph
Figure 9.17 Satisfaction
Figure 9.18 Dissatisfaction
Figure 9.19 Unused features
Figure 9.20 Comprehensive visual
Figure 9.21 The spaghetti graph
Figure 9.22 Emphasize a single line
Figure 9.23 Emphasize another single line
Figure 9.24 Pull the lines apart vertically
Figure 9.25 Pull the lines apart horizontally
Figure 9.26 Combined approach, with vertical separation
Figure 9.27 Combined approach, with horizontal separation
Figure 9.28 Original visual
Figure 9.29 Show the numbers directly
Figure 9.30 Simple bar graph
Figure 9.31 100% stacked horizontal bar graph
Figure 9.32 Slopegraph
foreword
“Power Corrupts. PowerPoint Corrupts Absolutely.”
—Edward Tufte, Yale Professor Emeritus1
We’ve all been victims of bad slideware. Hit-and-run presentations that leave us staggering from a maelstrom of fonts, colors, bullets, and highlights. Infographics that fail to be informative and are only graphic in the same sense that violence can be graphic. Charts and tables in the press that mislead and confuse.
It’s too easy today to generate tables, charts, graphs. I can imagine some old-timer (maybe it’s me?) harrumphing over my shoulder that in his day they’d do illustrations by hand, which meant you had to think before committing pen to paper.
Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. The more information you’re dealing with, the more difficult it is to filter down to the most important bits.
Enter Cole Nussbaumer Knaflic.
I met Cole in late 2007. I’d been recruited by Google the year before to create the “People Operations” team, responsible for finding, keeping, and delighting the folks at Google. Shortly after joining I decided we needed a People Analytics team, with a mandate to make sure we innovated as much on the people side as we did on the product side. Cole became an early and critical member of that team, acting as a conduit between the Analytics team and other parts of Google.
Cole always had a knack for clarity.
She was given some of our messiest messages—such as what exactly makes one manager great and another crummy—and distilled them into crisp, pleasing imagery that told an irrefutable story. Her messages of “don’t be a data fashion victim” (i.e., lose the fancy clipart, graphics and fonts—focus on the message) and “simple beats sexy” (i.e., the
point is to clearly tell a story, not to make a pretty chart) were powerful guides.
We put Cole on the road, teaching her own data visualization course over 50 times in the ensuing six years, before she decided to strike out on her own on a self-proclaimed mission to “rid the world of bad PowerPoint slides.” And if you think that’s not a big issue, a Google search of “powerpoint kills” returns almost half a million hits!
In Storytelling with Data, Cole has created an of-the-moment complement to the work of data visualization pioneers like Edward Tufte. She’s worked at and with some of the most data-driven organizations on the planet as well as some of the most mission-driven, data-free institutions. In both cases, she’s helped sharpen their messages, and their thinking.
She’s written a fun, accessible, and eminently practical guide to extracting the signal from the noise, and for making all of us better at getting our voices heard.
And that’s kind of the whole point, isn’t it?
Laszlo Bock
SVP of People Operations, Google, Inc.
and author of Work Rules!
May 2015
Note
1 Tufte, Edward R. ‘PowerPoint Is Evil.’ Wired Magazine, www.wired.com/wired/archive/11.09/ppt2.html, September 2003.
Acknowledgments
About the Author
Cole Nussbaumer Knaflic tells stories with data. She specializes in the effective display of quantitative information and writes the popular blog storytellingwithdata.com. Her well-regarded workshops and presentations are highly sought after by data-minded individuals, companies, and philanthropic organizations all over the world.
Her unique talent was honed over the past decade through analytical roles in banking, private equity, and most recently as a manager on the Google People Analytics team. At Google, she used a data-driven approach to inform innovative people programs and management practices, ensuring that Google attracted, developed, and retained great talent and that the organization was best aligned to meet business needs. Cole traveled to Google offices throughout the United States and Europe to teach the course she developed on data visualization. She has also acted as an adjunct faculty member at the Maryland Institute College of Art (MICA), where she taught Introduction to Information Visualization.