Data Storytelling 101: The Magic of Pre-attentive Attributes
What is Pre-attentive Attributes?
Imagine you’re looking for your friend in a crowded park. You don’t have to stare at every single person; instead, your eyes automatically pick out things that stand out. A bright red shirt, a person much taller than everyone else, or someone waving their hand — these things grab your attention instantly.
Pre-attentive attributes are basically the first things your eyes notice without you even trying. They’re like little flags waving at your brain, saying “Look here!”
Let’s prove its power through a simple demonstration. Observe how quickly you can identify and count the number of 8s in the following sequence.
The answer is 9. The lack of visual cues made it difficult to find the number 8. You had to scan through the sequence manually, which was really time-consuming.
Now, let’s try adding a single change on the sequence and you will notice a big difference.
Notice how quickly you can identify the 8s without focusing on each number individually? This is the magic of pre-attentive attributes!
In data visualization, it’s the same idea. We want people to be able to quickly understand the most important information in a chart or graph, not get bogged down in all the details. That’s where preattentive attributes come in. They act like little flags, grabbing attention and helping people see the key message right away.
Pre-attentive Attributes in Graphs
Imagine graphs without visual aids — just like counting 8s! It wouldn’t be very efficient. Let’s explore a case study. Suppose you want to compare resolution times for technical support tickets: those that violated the service level agreement (SLA) and those that didn’t.
In this case study, I want to shift my audience’s attention to tickets with SLA violations, particularly those where the volume is highest. To achieve this, we can de-emphasize other elements and visually elevate the data point we want to highlight.
While data labels can be helpful, including them for every point can clutter the chart. Since our primary goal is to identify peak violation times, we can focus on highlighting these high values.
Conclusion
Pre-attentive attributes are visual features we grasp instantly, bypassing the need for conscious attention. They guide your audience’s attention and establish a clear visual hierarchy, effectively leading them through the information you want to communicate in the desired order.
They are particularly useful in explanatory analysis, where the goal is to share insights and conclusions with your audience. In contrast, exploratory analysis focuses on uncovering patterns and relationships within the data itself, where visual aids might be less reliant on preattentive attributes.
Thank you for reading!
I hope this case study provided valuable insights. If you have any questions, feel free to reach out.
For those interested in exploring more data storytelling and data visualization content, I consistently create such content on my Patreon page.
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