Data is everywhere and more and more businesses are realizing the benefit of harnessing them to develop critical insights and stay competitive. With companies investing in data science teams and roles, there is already a good understanding of why data literacy is important but visual literacy is a term that might be less well-known.
So what exactly is visual literacy and how does it impact data analysis and visualization? It can seem like they’re one in the same but there are important distinctions that can help us better understand the ecosystem of data analysis and visualization.
What Do We Mean by Visual Literacy?
The term “visual literacy” refers to a set of abilities to find, interpret, evaluate, use, and create images and visual media. It’s a concept with application traditionally in art and design, but it is much broader. Visual literacy is concerned with language, conversation, and interaction, and in the context of data, it is concerned with how visual mechanisms work with data. For example, how a user understands and interprets a chart that shows sales performance over the last six months is related to visual literacy. How that chart is presented for ease of understanding and memorability is the role visual literacy plays in the effectiveness of any data visualization.
How Does Visual Literacy Affect Data Viz and Storytelling?
The omnipresence of data has changed the way people work and is often recognized as critical to future generations’ democracy, but there is a crucial distinction to make when it comes to visual literacy and understanding how data visualization works. If you think of data as a piece of music, strong data literacy requires the ability to read notes to understand the whole piece. It has to do with understanding when to utilize a mean versus a median or determining when to trust a study’s results and when to disbelieve small-sample size research.
On the other hand, visual literacy is the music. It is demonstrated by a tune that repeats or develops into a chorus. When adding data notes into a song, consider your audience’s visual literacy to create meaningful narratives. And meaningful narratives are the building blocks of a data story.
Show the Data or Tell a Story?
The ways in which we connect and visualize data to convey information is an essential task for any data visualization professional and can result in some difficult choices: do we want simplicity or complexity? Do we want to craft a narrative? Or is it worth it to display raw data and allow consumers to explore on their own? Do you go for simplicity or complexity? Do you show the data or tell a story? Do you display raw data or take the user on a journey?
Ultimately, significant data visualizations must be both accurate and appealing and strike a balance between being informative and being aesthetically appealing. Visual literacy can help us strike that perfect balance of helpful and easy to view.
Data Literacy Vs. Visual Literacy
In today’s world, many of us suffer from information and insight fatigue, and are inundated by things like dashboards cluttered with red and green indicators of excellent and negative performance.That’s where data literacy comes into play; it’s the ability to interpret and understand data itself. Good data literacy allows you to select the right chart for your data and typically places an importance on clarity above aesthetic; rather than radials, it chooses bars.
Visual literacy is concerned with the presentation of data as information. It requires attention to detail but is also concerned with whether or not your audience cares about the data you’re showing them. Simultaneously, visual literacy is concerned with how the work is remembered, the story it tells, and whether or not it’s meaningful, instructive, and significant.
A few other questions that might come up when thinking about visual literacy in data visualization, include:
-
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?
Conclusion
For the art and science of data analytics and visualization to progress, we must transcend data literacy. Data literacy is an essential start, but users don’t want to look at just bar graphs and pie charts. We must pay attention to color theory, forms, the reading habits of internet consumers, and the rising sense of data fatigue among end-users, just to name a few factors. Visual literacy must be learned, practiced, and perfected to ensure that data visualization remains relevant and useful for everyone.
Interested in learning how data storytelling could help your business? Reach out to Gemini today! We believe that data is a powerful storyteller and with our expertise, you can improve your business’ capability to build and comprehend even the most complex data associations, connections, and relationships.
