Earlier this year, I took a course on data visualization, and I have to be honest – I was blown away. Coming from a technical background, my friends and I focused more on data transformation, imputation, and forming hypotheses for statistical tests than on how to present our findings in slides or papers. We didn’t really see the importance of presentation and often relied on default visualizations from libraries like Plotly and Pandas without much thought. Looking back, I realize I took for granted that everyone thought like me—the “natural” way.

However, there’s much more to it. Job descriptions for data-related roles now often emphasize “using data to tell a story” or “data storytelling,” whether for upper management or clients. Even though most companies use standard visualization tools like Tableau or PowerBI, designing clear and powerful visualizations is crucial for effective storytelling.

There are many principles involved in designing the right type of chart, color schemes, and interactivity. However, today I want to explore a concept that fascinated me: “Eyes Beat Memory.”

This concept was introduced by Tamara Munzner, a professor at the University of British Columbia. She suggested that it’s easier to compare two plots when you can view them side-by-side, rather than remembering one while looking at the other. In her slides, this principle became the idea of external cognition vs. internal memory, implying that we process multiple pieces of information more effectively when we see them simultaneously, rather than recalling past information while absorbing new data.

This principle changed how I analyze and evaluate visualizations in daily life. I used to admire the effort programmers put into animated time series data, knowing how long it took to create those animations. Although they looked impressive, I now understand that animations might not always be the best choice. When users watch an animated time series, they must actively recall previous frames for comparison. If the data changes significantly over time, it becomes challenging to track variations across frames.

Similarly, this principle also impacts interactivity. In data analysis, we use various dashboard applications to make comparisons and draw conclusions. With “Eyes Beat Memory” in mind, consider these two design options for a visualization system:

Design A: One large window displays a chart of a data subset filtered by user-selected conditions via checkboxes.

Design B: A large window with an option to add multiple small chart panels, each based on specific conditions.

As you might have guessed, Design B is the better choice. While it depends on the data—like comparing overlapping line charts versus separate ones—Design B empowers users to decide. It avoids system limitations that prevent side-by-side comparisons, making it easier to analyze data effectively.

Charts are from https://blog.datawrapper.de/dualaxis/

In conclusion, understanding the importance of visual storytelling and the “Eyes Beat Memory” principle has transformed my data visualization approach. It highlights the need for thoughtful design choices that prioritize user comprehension and ease of comparison. By embracing these insights, we can create more effective and engaging visualizations, ultimately enhancing our ability to communicate complex data stories.