The core premise of data democratization is that every one of us can use, interpret, and work with data to make organizational decisions. Yet a found only 20 percent of business leaders consider themselves data literate and less than 25 percent feel proficient at working with data. Those are surprising numbers because these are people on the front lines of business using data to make decisions and drive future strategy.
As we shift from a world where IT departments solely managed data to our current reality where people who aren’t used to working with data must incorporate those skills, we face a significant, urgent, and daunting challenge around equipping workers with the right tools. Consequently, a debate has begun around when and where we need to introduce data literacy and data science programs throughout the educational process (such as by Joseph E. Aoun, which I recently read on this topic).
(Almost) everything you need to know about data literacy you learned in school
It’s a common fear that being data-literate requires extensive data training. While some jobs indeed demand advanced skills in statistics or algorithm design, most of the fundamental skills needed for working with data are already taught as part of a typical K–12 education—we just don’t necessarily recognize them as such. But in fact, the same skills required to be data literate are developed in classes from music to chemistry to math to history. These include numeracy, ordering, and sequencing facts by dates, reading symbols or patterns, and looking at relationships for possible cause and effect. We learn to write essays to argue a position based on these facts, or learn to follow sequences to play a song. These skills also form the foundation of being able to interpret data.
Unfortunately, many people feel they aren’t sufficiently skilled to ask questions or have nuanced conversations about data in a workplace setting, particularly when confronted by the jargon of business or IT. That’s fundamentally wrong. What trips people up most when working with data tends not to be a lack of skills, but a lack of domain knowledge (or how to apply their skills to that domain), a lack of confidence, or time pressure (where deadlines are prioritized over examining, evaluating, and questioning the data or the conclusions). When we’re in school, we learn the terminology for different domains of knowledge as we encounter them and how to apply our skills to each new domain. Our requirements within an organization for data literacy are no different.