How Is Data Literacy Changing in an Artificial Intelligence Landscape

October 11, 2023

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At the beginning of the COVID-19 Pandemic, seeing the dashboard from Johns Hopkins, that we are all so familiar with now, was exciting.  The thrill of all the information being presented in one place,  and realizing we live in a world where data are so easily available and accessible. Long gone are the days of requesting data and waiting weeks or months to receive a CD in the mail, or maybe not being able to obtain information at all because it was off limits to external users or not in a useful format. 

I have been thinking about how I live and breathe data, constantly looking at visualizations with a critical eye. What about everyone else without critical data skills?? What does the average person see when they look at dashboards? What do they believe about COVID? How did our data visualizations affect how we managed the Pandemic?

We create data. 

As geographers and spatial data scientists, we steep ourselves in information.  We are a legion of people who have made it our life’s work to understand and use data wisely. Geospatial professionals are trained to use data appropriately and to know how to create visualizations that are meaningful. I recently read an article in Time Magazine about data literacy and national security. The author says that for most Americans’ data literacy skills are overall poor. As I read this, I kept thinking back to that COVID dashboard and how it could have been misinterpreted.

When I taught GIS, I would always take some time to talk to my students about how critical thinking skills are important when deciding which datasets to choose, how to combine them, and what kinds of analysis make sense (and what to do when they don’t). I never felt like I had enough time or the right framework to have a deep and meaningful discussion about how to make good or better choices when it came to spatial analysis and visualization. It always seemed like there were other priorities and most students just wanted to know how to make a map. I always worried that I should have spent more time focusing on what I now realize was my real desire, help people become more data literate.

What is Data Literacy?

Almost every book or article that I’ve come across recently about data literacy starts out by defining the term in the same or very similar way. Data Literacy is the ability to explore, understand, and communicate with data. My preference is the definitions that  add in a bit about doing the above ethically, but there doesn’t seem to be any confusion or discussion about what data literacy is. 

Can you read, write, and communicate data in context? 
Can you explain why one analysis is appropriate for one application, but not cannot be used for another? 

What is Next?

In the State of Data Literacy, Jonathan Cornelissen says, “Organizations are drowning in data but starving for insights.” I look at the various places that I’ve worked over my career, and I think that this has long been a problem. It didn’t start with the Pandemic, but it most certainly became exponentially worse. With the advent of “big data,” we have to spend more effort in our schools and in our work organizations on ensuring that our peers and our executives are not only aware of the potential pitfalls of using (and misusing) information, but also about the reasons why we make poor decisions with data.

As we enter this age of Artificial Intelligence (AI), data literacy is going to become more and more important. That old adage of “garbage in, garbage out,” has a slightly more terrifying tone as we increasingly use AI to help us make decisions. We must think about who is creating data, why, and how it will be used. AI is being used for everything from meal planning to map-making and spatial location intelligence with the results only being as good as the input. We have to train our geospatial staff to be what data literacy expert Ben Jones calls “highly data literate” people.


The geospatial community is well-suited to be a space of change and continued growth of a data-driven culture highly data literate people. Data are part of our everyday conversations, and we can model what data literacy can look like beyond our field. As leaders, we should ensure that:

  • our organizations develop data literacy programs and ensure that staff have standardized knowledge around data and data concepts;
  • we practice what we preach and use our skills to communicate with data using best practices;
  • develop fluency in speaking about data and data literacy

None of us know everything there is to know about data, so let’s take the opportunity to be good models of data literate analysts. Start with an assessment like Ben Jones’ 17 Traits Self-Assessment and do what Alli Torban recommends in her podcast Data Viz today: learn some new skills to develop your fluency and data literacy. The more we practice data literacy skills ourselves and advocate for data literacy skills for others in our field and beyond, we will create a more informed, successful, and resilient data-smart society!


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