Some might argue these are one and the same, but I think there’s an important distinction to be made here.
Take this quote from data viz legend Hans Rosling as a starting point:“Most of us need to listen to the music to understand how beautiful it is.
But often that’s how we present statistics: we just show the notes, we don’t play the music.
”— Hans RoslingHere’s how I like to think of it: good data literacy is understanding the notes.
It’s knowing the difference between a mean and median, and when to use one over the other.
It’s being aware of when to draw conclusions from a study or when to be skeptical of research with small samples sizes.
But visual literacy is the music.
It’s the strategic connecting of notes to form a melody, which repeats or evolves to form a chorus.
Thinking about visual literacy (and the visual literacy of your audience) allows you to start connecting the notes of data into a cohesive song.
And as this helpful Wikipedia entry states, it effectively allows you to “make meaning from information”.
Data builds to insight, which can form a narrative.
And narratives are what build to make a data story.
Not an actual song.
Pardon my very quick mockup of the sheet music analogy.
Truth and BeautyHow we connect and visualize data to convey information is an essential task for any data viz professional.
Which means making some hard decisions.
At this point, we’ve nearly reached the age-old debate in the field: simplicity or complexity?.Simply show the data or tell a story?.Exhibit raw information or allow the user to explore?We’ve reached the spectrum of Truth and Beauty.
The spectrum diagram might not be the best here, to be fair.
Great data visualizations can capture both truth and beauty, striking the right balance between information and aesthetic intrigue.
But I still find that thinking about truth and beauty as a spectrum to be a helpful starting place when thinking about data, especially when working with an audience that might not be as comfortable working with data.
Data literacy is concerned with understanding the data itself.
Visual literacy is primarily concerned with the presentation of the data as information.
Here’s another example of how they’re different: good data literacy allows you to select the right chart for your data, the one that is most accurate and informative.
Data literacy hates the risk of misinterpretation, and in a pinch, will always sacrifice visual flair for the sake of simplicity.
Think bar charts, not radials.
If you follow data viz on Twitter, think Edward Tufte and Stephen Few.
Visual literacy includes this eye for detail, but goes a step further by asking a simple but perhaps discouraging question: will my audience even care?Here are some other questions that might come up when thinking about visual literacy in data viz:Is this chart visually interesting enough?Will people want to interact with it and explore further?Is it memorable?Is it beautiful?Is the meaning clear?.If not, is more information intuitive enough to find?Data literacy is an essential starting point.
But let’s be real: most people don’t want to look at a page full of bar charts.
With more data available than ever before, the world is suffering from insight-fatigue, staring at dashboards day after day of red and green indicators showing positive and negative performance.
Just look at this generic business guy.
He’s been staring at 3d pie charts and bar graphs all day.
One of the things I love most about data visualization is that it continually challenges this status quo.
It invites users to explore the data for themselves.
Sometimes the quick, glanceable takeaway is what you need in the end.
But if we reduce data visualization to this narrow subset of the field, then we lose it’s potential for exploration and discovery altogether.
Data viz practitioners need to go a step beyond data literacy in order to keep advancing the field.
We need to be obsessed with things like color theory, the feelings of certain shapes, the reading patterns of web users and the spreading sense of data fatigue among end users.
Visual literacy is one way to think about this essential set of skills.
It’s the way we keep data visualization interesting, exciting and accessible for everyone.
If this sort of thing fascinates you as much as it does for me, here’s some additional recommended reading:Visual Literacy in the Age of Big DataODI: data literacy will help solve world’s biggest challengesHow to be an educated consumer of infographicsNew kinds of literacy, and the world of visual informationI’m always up for discussion!.Leave your thoughts in the comments or find me on Twitter.
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