The COVID-19 pandemic revealed many harsh realities that people worldwide are continuing to grapple with. One of these revelations is that many people do not understand how to interpret and communicate with data effectively — in other words, a large majority of people are not data literate. Research from Exasol confirms this data literacy deficit, reporting that only 41% of 16 to 21-year-olds consider themselves to be data literate.
In my view, COVID-19 may be the most illustrative example ever of why data literacy is necessary. Misinformation and misinterpretation of data were a core issue affecting health decision-making during the pandemic. As someone who (quite literally) wrote the book about data literacy and helped pioneer the field, I believe it’s worthwhile to investigate the data literacy gaps that COVID-19 exposed so that the public can be better informed when making decisions about their health — and about nearly everything else. Here’s my take on how COVID-19 exposed data literacy readiness.
The Vital Signs of Data Literacy
At its most basic level, data literacy is about being able to look critically at data, make decisions with data, and communicate with data. During the COVID-19 pandemic, there has been an immense amount of data conveyed to the public through a variety of channels — some of that information has been accurate, and some has not. A survey from Kaiser Health showed that 78% percent of adults surveyed had been exposed to false data about COVID-19 at some point during the pandemic.
So where is the confusion between factual data and COVID-19 misinformation occurring? COVID-19 illuminated for the world multiple areas of data literacy that were needed. First, I believe that media sources need to be better equipped to communicate critically with data. During the height of the pandemic, media from both sides of the political aisle cherry-picked the data that they chose to highlight. For example, research from the New York Times showed that U.S. media as a whole had a bias towards highlighting negative COVID-19 data, skewing the accuracy of COVID-19 coverage.
Additionally, roughly half of Americans make decisions and form opinions about the pandemic based on what they see on social media, according to research from Pew. Social media is one of the most prolific ways that COVID-19 misinformation is spread, making its viability as a news source a cause of concern.
News outlets and social media cannot bear the blame for data literacy issues, though — data literacy is highly individualized, and each person should take responsibility for their own data literacy journey. Being unable to decipher the information and data that comes in front of you and to understand how to read the small print has real material consequences, as the pandemic has shown.
Data, the Organism
Throughout the COVID-19 pandemic, information and data has changed and mutated at a rapid pace. This is normal — data is always subject to change. As anyone who is data fluent would tell you, data must be treated like a living organism — growing, evolving, and constantly in flux.
COVID-19 showed that data and analytics are iterative. As more information during the pandemic came out, previous facts had to be updated and adjusted to fit emerging research. For example, discourse around whether the public should wear masks has evolved throughout the pandemic. Early in the pandemic, the public was discouraged from buying and wearing masks for a variety of reasons. However, data surrounding mask effectiveness quickly changed and became a center point of public health messaging around preventing the spread of COVID-19. The iterative nature of data within the pandemic doesn’t mean that data and research were “wrong” before being “corrected,” it simply means that as new data filtered in, scientific conclusions and public communication needed to be adjusted accordingly.
For individuals and organizations seeking to become more data literate, a healthy dose of skepticism and introspection are both key to a holistic understanding of data. First, data skepticism is wonderful because you’re questioning the things that are put in front of you rather than accepting them at face value. Implementing data skepticism may look like identifying the primary source of the data, interrogating potential bias stemming from politics and funding, and looking up resources to help you interpret the more technical aspects of the data.
The second thing we can do is acknowledge our own biases that we bring to the table when interpreting data. We need to be ruthless about identifying and rooting out our own confirmation bias (i.e. agreeing with data simply because it supports our already held worldview). Searching for “truth” within data relies on this healthy skepticism coupled with the ability to look at data objectively.
Curiosity Is the Cure
I’ve just spent some time illuminating the data literacy issues that COVID-19 exposed… So how do we fix them? In my view, the answer is much simpler than becoming an expert in statistics or reading every research article ever published. It starts with curiosity.
We need to develop more curiosity than we ever have before. Curiosity differs from skepticism in that it is a proactive rather than a reactive attitude to data literacy. It involves embodying a learner’s mindset and seeking out data to help you make sense of the world. A posture of curiosity towards data allows people to ask the right questions of data so that they can get to the core of what it’s communicating. This is where upskilling your data literacy knowledge comes in. While nobody needs to be a data scientist to be data literate, seeking out resources such as online learning, curated presentations and data literacy “boot camps” can be excellent ways to develop deeper data literacy skills.
In the era of “flash” news cycles and endless social media content, data is inevitable. It is incumbent upon individuals and organizations to become data literate as quickly as possible. Developing a curiosity about data interpretation, communication, and decision-making has never been more important than it is today.
