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Paul Laughlin


What Analysts Can Learn From Storytelling With Data

A tale of effective communication

Published: Wednesday, June 10, 2020 - 12:03

One of the most practical data visualization books for my clients is Storytelling With Data (Wiley, 2015). So, this is a longer-than-usual book review of this modern classic.

I say that because it is not just accessible for those with no background in data visualization. The book also focuses on the kind of charts most analysts produce. You won’t find admonitions to keep updated with the latest packages for R or Python. You won’t be overwhelmed by award-winning data-viz art.

But before I rush in with too much enthusiasm, let me tell you a little about the author. Cole Nussbaumer Knaflic started her data viz career at Google. There she honed her craft and transformed the company’s People Operations with incisive charts. She has since gone on to found Storytelling with Data, her own training and consulting business.

Practical data visualization, not aiming to impress

What makes Cole such a refreshing voice in the data viz community is her focus on more effective basic charts—the type of charts most analysts will be asked to produce for their business day in day out. Her style and guidance really suit businesses, with a focus on the key message, not the data artist.

So, what is so helpful about this book? Why do I recommend it so readily? The first reason is the clear structure. Having attended one of Cole’s training courses, I can confirm that she also trains analysts using this same framework.

A structured approach to storytelling with data

In the book’s introduction, after highlighting the problem (too much #BadDataViz), Cole lays out her approach in six steps:
1. Understand the context
2. Choose an appropriate visual display
3. Eliminate clutter
4. Focus attention where you want it
5. Think like a designer
6. Tell a story

That workflow also provides the structure for the majority of the rest of this book. There are a few helpful additions that I will mention, but the main learning from this book is how to apply those six steps.

The importance of context

In the first formal chapter, Cole directs analysts to better understand their audience. To be able to answer the who, what, and how of their communication challenge.

There are useful points about the different approaches to use for exploratory vs. explanatory analysis (with the latter being the focus). She also emphasizes the importance of Socratic questioning to get at the real need.

She closes this capture by sharing three

very practical tools:
Three-minute story: Can you summarize your message down to a paragraph that you could speak in three minutes?
Big idea: Can you boil that message down to just one sentence? It should be the most important thing for your audience to remember afterward.
Storyboarding: The idea of using mini Post-it notes as a way of structuring your presentation (more on this later, but the low-tech nature of this approach matters).

Choosing an effective visualization

In this chapter, Cole presents the 12 different approaches she most commonly uses. It is a really helpful chapter in terms of encouraging analysts to understand when it is appropriate to use different basic charts. Well worth reading, even if you think you already understand them.

Her common options are:
• Simple text (e.g., if you really have just one or two numbers)
• Table
• Heat map
• Scatter plot
• Line chart
• Slope graph
• Vertical bar (column) chart
• Horizontal bar chart
• Stacked column chart
• Stacked bar chart
• Waterfall (bridge) chart
• Square area

As well as including some interesting data-viz history for a number of these charts, she makes some important points. She substantiates these with positive and negative examples to show how they can be misused.

There are also lots of great small details (as Andy Kirk is fond of sharing). For instance, how to judge the appropriate width for column charts. Cole also touches on ethics and Edward Tufte’s principles. Plus, of course, a much-needed critique of why pie and doughnut charts do not work well.

Clutter is your enemy

Once you have seen any of Cole’s finished charts, you will be struck by the clean, efficient minimalism of her style. In this chapter, she explains the reason for her intentional use of white space and decluttering what is too often left on charts just because it’s a default in the software used.

By considering the cognitive load for the reader and the Gestalt principles of visual perception, we are led through how to make visualization easier. Our reader/viewer is able to see what is most important and not get distracted. She helps data viz designers consider where eyes go when first seeing a chart.

The biggest benefit is something that Cole is great at giving in this book: a worked example. She takes a fairly standard-looking line chart produced by Excel and shows step by step how she can improve it by decluttering.

Focus your audience’s attention

Up until this point, a number of the examples have looked a bit dull. You will have noticed that Cole has a preference for pale gray as a default chart color, but that is just to start with a clearer “canvas.” In this chapter, she explains how color and other changes can help you control focus.

She explains how you see with your brain. The different roles of your eyes, iconic, and short- and long-term memory. These help explain why some features of a visualization can be preattentive. She goes on to explain that these preattentive attributes are tools for you to use to draw attention to what matters.

In addition to the judicious use of an accent color hue (to stand out against white and gray), other preattentive attributes to consider in text and charts are:
• Orientation
• Shape
• Line length
• Line width
• Size
• Curvature
• Added marks
• Enclosure
• Intensity (e.g., bold text)
• Spatial position
• Motion

Cole usefully shows how these features can be used not just in the visual elements of your chart but also your text.

Think like a designer

For anyone else who has attending Cole’s training course, this chapter is a bonus. It includes content that there is not sufficient time to include in that course. Here she helps the reader think like a designer, for instance, about how form follows function.

This includes considering affordances. How can you use your means of drawing focus to make it obvious how to use your chart? How can you create a clear visual hierarchy? She also prompts us to consider accessibility (not only for the visually impaired or color blind), including how to simplify and provide annotations when and where needed.

Cole also touches on aesthetics and returns to how Gestalt principles help explain why one chart may look pleasing to the eye when another jars.

This is all supported by the next chapter, titled “Dissecting model visuals.” This helps you understand, via five positive case studies, the design decisions involved in producing the final chart.

Lessons in storytelling (not just visuals)

Another strength in Cole’s approach is she is not just teaching data visualization. She also understands the importance of these charts sitting within a clear and persuasive story.

In this chapter, she explores the “magic of story” and how to construct the compelling elements you need. She follows Aristotle’s framework in identifying the need for:
• A beginning (setting the stage)
• The middle (the dramatic tension)
• The end (the solution/call to action)

She includes a number of helpful narrative tricks for analysts to use when storyboarding a compelling story. These include effective use of repetition as well as decoding which order works best (e.g., horizontal or vertical logic, or reverse storyboarding). Once again, those Post-it notes (one per slide with only the headline written on them) help you play around with options.

Further support to pull it all together

Beyond the above walk-through of Cole’s six steps there are two more useful chapters to close this book.

In chapter eight, Cole provides a detailed walk-through of each of the six steps, this time using a different context and a grouped column chart in need of improvement. This really brings the steps to life so readers can judge if they would make the same decisions at each point.

The final chapter includes even more case studies. Each one addresses a potential issue that analysts may face when trying to apply the theory in this book. For example, there are worked examples for using a dark background, using animation, and avoiding spaghetti graphs.

How are you developing your data visualization skills?

So, in summary, I highly recommend this book to any analyst seeking to develop better charts. It offers a ton of good sense and worked examples to apply in practice.

Beyond that, visit the Storytelling with Data (SWD) community. There you’ll find exercises like monthly challenges and spaces where people can share their answers or makeovers to each month’s challenge.

If you aren’t able to attend an in-person training course but are looking to devote some time to work through exercises, her next book may help. To help those who want more practical exercises to learn by doing, Cole has now published Let’s Practice (WIley, 2019). It is like an expanded version of this book with even more exercises for you to, well, practice.

First published April 21, 2020, on the Customer Insight Leader blog.


About The Author

Paul Laughlin’s picture

Paul Laughlin

Paul Laughlin is a speaker, writer, blogger, CustomerInsightLeader.com enthusiast, and the founder and managing director of Laughlin Consultancy.


Line and Bar Charts Won't Tell Quality Improvement Stories

Line and bar charts with trendlines usually fail to illustrate improvement because they do not have a goodness of fit metric above 50%. Line and bar charts are the "Dumb and Dumber" of charts. To tell Six Sigma improvement projects you will need smart charts--charts that went to college and took statistics.

Quality Improvement Stories can be told easily:

  1. Control chart of current performance (also used in Control phase of DMAIC)
  2. One or more Pareto charts of types of defects (or histogram of deviation)
  3. Ishikawa/Fishbone Diagram to determine root causes
  4. Countermeasures
  5. Action Plan

Control charts will be required to sustain (i.e., control) the performance after improvement. Otherwise, the improvement decays over time.

Download my free Lean Six Sigma Action Plan to understand quality improvement and problem solving with charts and data.