Storytelling with Data — Part 2. Cognitive Load and Charts Best…
By: John Ostrowski
Tags: Data Science, Data Storytelling, Design Thinking, Data Visualization, Storytelling For Business

Choosing an effective visual

On his book “The Effective Manager” Peter Druker states the importance of learning about effectiveness, part of the discussion surrounds the fact that knowing what the right looks like is one thing, executing it right is a completely different story.

The same applies to data visualization, analysts know the best practices for when using bar charts or scatterplots, but every now and then we see some “food charts” wandering around.

The bottom line of this section for me is that: Taking the time to understand how to display the information efficiently for your public needs to be an active part of visualization planning. In most cases, this step is neglected and ends up passively by accepting the “recommended vizz” from our visualization tools, not anymore.

No graph for just a few numbers

Bar chart for a small data set

We see bar charts like this one in many meetings. It does convey some basic information about this poor manufacturing process, but which story is being told here? What is the unique point that we want our audience to know or take action? Is it the focus on the bad parts increase or on the delivery issue since the necessary batch was 45 units?

Of course, this graph lacks context and this is why we have such questions. However, back to the main point, is this bar chart really necessary to communicate the data? The takeaway is that for small sets, focus on the message you want to inform, save people's time and cognitive load and use simple text with single numbers. In other words, the same graph could've been summarized as:

Concise and direct to the point. This design reduces what Cole calls cognitive load, which is described in more detail in the next chapter. It is a fact though, that the highlighted number communicates the intended message while the underlying text provides the necessary context.

Pro tip: The importance of the grey text when using single numbers goes even further, especially when using a percentage change. From Wheelan on Naked Statistics, percentage changes are always relative to something else, so it is essential to understand what this something else is in order to have a complete picture. In practical terms, a 100% increase in the price of a water bottle is harsh but your wallet will survive. A bonus of 1% on Jeff Bezos' net worth, however, would keep one wondering what to do with such amount of money.

In terms of tools, whenever I need to summarize a chart into a single number I switch to Google Slides, where you can play better with text styles.

Lines for time series and variations

Line graphs are mostly used for continuous data presented in two forms, either you have x-axis as a period of time (date type variable) or as a before and after comparison.

Pre-attentive attributes determine a lot the focus of a line graph. Check the example below from a Google Analytics report.

Google Analytics Audience Overview report, a typical time series plot.

The "good outlier" on January 2nd is spotted with ease, even having no labeled data points. If this is aligned with the context, let's say it is more important the trend than the underlying numbers, this configuration is good to go. However, labeling specific data points such as maximum and minimum are good strategies to bring attention to a different story. Those strategies are discussed in more depth in Part 3.

In my opinion, inclination charts should be part of the analyst toolbox. They present, in a very clear and straightforward way variation between events. In a recent project, I used an inclination chart to present keyword position variation on Google SERP.

Inclination graph for presenting before and after events

Once again, since this was used in a real use case I used all pre-attentive techniques to tell my story. Note that the focus of the presentation surrounded the keywords that went to position one, however, there's a second story to be told in the same chart regarding keywords which shown bad performance.

Seaborn has a beautiful API for time series plots, but in most cases, Google Sheets for plotting and Google Slides for commenting will do the job, this was the exact process for generating the inclination graph on the example above.

Bar charts to keep it simple

Bar charts are one of the best and preferable visualizations due to its simplicity and familiarity. Analysts might argue here that simplicity is not appealing, but when it comes to storytelling with data showing off intricate visualizations and flexing chart customization coding muscles is not really the point.

As storytellers we want our information to be understood, that's it. Therefore, using a visual that requires less cognitive processing helps to direct the public’s attention and willpower to understand the insights being conveyed.

From my experience and confirming Cole’s notes, horizontal bars are the easiest to represent categorical data, especially when having long labels. Horizontal bars follow the same logic of reading, the eyes scroll from left to right, starting on labels and reaching the data, which is the most intuitive way to read information.