Here’s a question for you- when did you learn to read and write? Odds are it was one of the first things you remember learning, and that makes sense: learning to read and write is foundational for all other knowledge you acquired.

Literacy. We usually think of it as the ability to read and write, but in reality the term is much broader than that. Beverly Moss, in her work “Literacy Across Communities” writes “There is still much discussion and disagreement on definitions of literacy…” and further notes that, despite the many disagreements, the field has landed on a definition which “links literacy to a complex web or network of social practices.”

Essentially, our definition of literacy is expanding, and to that list we would like to add what we believe is the most important kind of literacy anyone can have in the modern world. Data literacy.

Back in 2009, Kirk Borne and his associates wrote a paper called “The Revolution in Astronomy Education: Data Science for the Masses.” You can see the original work on Kirk’s website, Rocket-Powered Data Science, or do what we did and follow the links he provides which brings you to the paper itself.

In this paper he cites a now-familiar problem: information is growing exponentially. He writes that “with this increased vastness of information, there is a growing gap between our awareness of that information and our understanding of it.”

Gartner backs him up, we’ve recently cited the fact they discovered that 85% of fortune 500 companies aren’t using data analytics to any competitive advantage.

Kirk is coming at this topic with his unique background in mind, that of an astrophysicist. But he speaks of data more broadly, rightly noting that “the growth of data volumes in nearly all scientific disciplines, business sectors, and federal agencies is reaching historic proportions.”

While his entire paper is definitely worth the read for any data-minded person out there, we found his call for “human computation” especially interesting.

According to Borne (quoting L. von Ahne), human computation deals with “tasks like image recognition that are trivial for humans, but which continue to challenge even the most sophisticated computer programs.” Borne goes on to say that “computational thinking refers to a new kind of literacy, akin to math or cultural literacy… computational thinking addresses the paradox of machine intelligence: knowing which stacks are best assigned to computers, and which are best assigned to humans.”

We’re going to agree with Kirk and say that we imagine a day, not too far from now, where data literacy is on-par with the ability to read and write. And his work leads us to another question: “where does data visualization fit into all of this?”


Data Literacy Needs A Language

Borne alluded to it when he was talking about computational thinking. It’s about knowing what tasks are best delegated to a machine and which are best left in human hands. And if you noticed something else, the definition of computational thinking he provided even said that one of those tasks is image recognition.

We have written plenty about the speed, power and accuracy of the human visual system. What we didn’t realize until we read Borne was that data visualization can be a key player in a future workforce of trained, data-literate people.

We think of it this way: you know how to read and write because you speak a language: in this case, english. You know the codes, the letters, the words, the sentence structure. It’s been taught to you since you were born and it allows you to communicate.

The problem with data literacy is that a very small subset of us speak the language. We weren’t brought up in it, we didn’t hear it every day. Small wonder then that so few us are fluent.

But that’s why VisualCue created our visual language. It’s science: everyone can understand pictures. Icons are a universal language, that’s why anyone, no matter what language they speak, can understand them. That way computers can do what they do best: gather data, and humans can do what we do best: spot patterns in pictures.

And if data literacy is really going to be as important as reading and writing in the future then we’d better have a language everyone can learn. You can read our research on data visualization using visual languages here.

So how does one become data literate? The same way you become literate in any language: learn to speak, read and write. Intuitive data visualization just helps you get there faster.

Data literacy is a cause we care about. We believe that data can be used to improve, no matter what field you’re working in. That’s why we’re working hard to make data transparent enough so that anyone can understand and use it. Just our small part in the big data picture.

Until next time,

The VisualCrew