When you hear the term “literacy,” your mind might immediately jump to phonics flashcards and handwriting worksheets. Or maybe you think of a financial literacy podcast you listen to for investing tips and retirement planning. But what about the term “data literacy”?

If this term elicits some brow-furrowing, you’re note alone. When we conduct workshops and ask the question, “How many of you have heard the term ‘data literacy,’” about half of the hands go up.

Many of us use data and the skills required to work with it effectively but don’t even know a term exists that assembles these skills data analysis, data visualization, designing research questions—into one tidy package.

People in the “data field” use “data literacy” as a handy term to refer to skills that we need to accurately utilize data at all levels, from data planning and collection, to data cleaning and analysis, to communicating and using results from data analysis.

Some data folks use the term “data literacy” to refer to fairly advanced skills that only a small portion of the population will ever possess. While we agree with elements of these kinds of definition, we have a slightly different perspective.

Here at CTData, we believe data literacy is an important and accessible set of skills that every person needs to possess.

To us, data literacy is similar to language literacy. You don’t need to be able to write a poem in iambic pentameter to possess language literacy. Skill levels fall on a continuum—you can have a basic grasp of a language or be a Poet Laureate. Both exist on the same spectrum; one requires a more specialized focus on a particular subject.

Likewise, we believe data literacy does not require advanced data skills but rather reflects the accessible fundamentals for those who might not consider themselves “data people.”

The definition of data literacy that we use here at CTData, and which permeates all of our educational activities, is this one:

Data literacy is the ability to systematically and ethically ask and answer real-world questions with data. This includes:

Collecting & finding data
Critiquing & interpreting data (being a critical consumer)
Analyzing and applying data

If you don’t work with data regularly you may be asking: what does this data literacy definition actually mean?

This definition integrates many concepts: systematically, ethically, collecting data, interpreting data, etc. And each of these concepts contains a vast range of skills and knowledge.

I often find myself explaining data literacy in this way:

You and I come across data as part of our daily lives. We may be listening to the news and hear data points discussed as part of the story, or we might be considering hitting “share” on a shocking data visualization on social media.

No matter the context, we need to be able to quickly assess the quality of the question being asked, the quality of the data source, the quality of the data analysis, and whether the conclusion(s) being drawn are valid.

This is a lot to think about in the 10 seconds before hitting “share” on social media.

Of course, there is much more advanced data literacy, akin to the Poet Laureate. Data literacy is also the ability to develop complex research questions with large datasets and then craft them into an interactive data visualization.

If the term data literacy is new to you, or you’d like to more deeply understand the definition, we have updated our very first training, previously known as Data Basics, and now called the Basics of Data Literacy. We also hold training on a wide range of other data skills important for data literacy as part of CTData Academy.

During this session we walk through the definition and talk about how you can apply these skills in your professional and personal life. As we line up additional sessions of this workshop, you’ll find them on our website.

So why does any of this matter? In Part 2 of this brief series, we’ll explain more what the different aspects of our definition mean.

In the meantime, you can Tweet to us and share why data literacy matters to you.

Data Literacy: The Skills You Need for Data Success