Defining Data Literacy

If you’ve been following the Alexandria Archive Institute (AAI) you may have noticed we use some academic jargon. We try not to, but it’s unavoidable considering what we do.

One phrase you’ve probably seen a few times is ‘data literacy’. At first, data literacy seems pretty easy. You’re literate about data, right? However, what does ‘data literacy’ actually mean, and why have a whole program dedicated to it?

First, let’s talk about data. What are data? Data are a collection of observations to answer or address a particular problem. As we always need more than one observation to answer a question, data are plural (also because the term ‘data’ comes from Latin).

Second, let’s talk about literacy. Typically, we think about literacy as related to writing and reading. Some people, back in the day, decided to put symbols together that indicated particular sounds. Then, when we recognize those sounds, they give us a particular meaning that we’ve internalized through experience. However, literacy isn’t limited to books or writing, at least not since the 1950s.

Today, we talk about being socially or visually literate. So what does literacy actually mean? Previously, we defined literacy as involving

“…a continuum of learning in enabling individuals to achieve their goals, to develop their knowledge and potential, and to participate fully in their community and wider society.”

Based on this definition, literacy might have more to do with the ability to understand a thing, such as technology, culture, or the law; with enough knowledge to use that medium accurately to communicate and to participate in a community.

Now that we have an understanding of what data and literacy are, how do we use those to define data literacy?

Well, Rahul Bhargava and colleagues think about data literacy as

“…equipping individuals to understand the underlying principles and challenges of data”.

This means that to be data literate is to understand what the guiding rules of data are and data’s unique problems. These involve teaching an appreciation of how to use data to empower others, to think critically, and the process of developing an argument rather than how to use a specific technology.

The process of becoming data literate takes time because like learning to read or write, data are their own language. However, once we’ve become comfortable communicating through data, we can do really cool things. And that ability to do cool things can help create data inclusion, which is the ability to use data literacy as a means for empowerment and community engagement.

These skills are important because data literacy is increasingly important in daily life. In the last year, many folks have spent lots of time looking at bar charts and line graphs. However, we couldn’t understand what those images meant without data literacy.

And our ability to understand data principles and limits allows us to get what we really want, which is information. Data with meaning. So we could extend Bhargava and colleagues’ definition to be: the ability to create accurate information from data. So putting all these together, AAI defines data literacy as

“…equipping individuals to understand the underlying principles and challenges of data and create information from those data”.