Storytelling with Data Read online

  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.

  No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at www.wiley.com/go/permissions.

  Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

  For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002.

  Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

  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

  Chapter 6 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

  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 3.2 You see columns and rows, simply due to dot spacing

  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.