As you scroll through this newsletter, you’ll see three images. For each, answer the following question: what do you see first? What’s the first thing you notice without giving it any deliberate thought?
Ready?
Here’s the first one.
And the second.
And the final one.
What were your answers? In the first two images, I bet your eyes were drawn to Nemo (the clownfish) and the green traffic light. As for the third one, I don’t expect a consensus: perhaps you first saw the yellow fish, the big brown fish, or the plant.
How do I know that? Well, this is how our brain works. It has two modes of functioning: system 1 and system 2. System 1—as explained brilliantly in the book Thinking, Fast and Slow by Daniel Kahneman—is the one that is quick on its feet. We notice certain elements of our environment and act upon them without giving it a think. Not even for two seconds.
In data visualisation, we call these tunable features. Tunable features are design choices in your graphics that draw the readers’ attention to key information immediately. Below are a few examples (this is by no means an exhaustive list; if you’d like to see one, I suggest the Data Visualisation Handbook).
Simple enough, right? You define what’s the most important and then highlight it. While there are many ways to create tunable features, the one I see the most often is colour. Colour can be very effective but also very tricky. That’s why I’m constantly on the lookout for tunable elements of other shapes and forms. Below are three recent examples I encountered or designed myself:
1) Colour and size on the Child Marriage Data portal:
2) Colour and outline in this article by the NYT:
3) Text weight and size in this report by HRFN:
If this was the first time you heard about tunable features, welcome to the world of subtle but masterful data design. It’s an easy trick to help you go from an exploratory chart to an explanatory one.
If you’ve known about this trick for ages, how do you use it? What elements do you highlight in your charts to attract attention?
Let us know! 🐡
See you next week,
—Evelina
Data storytelling training 📆
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