I’m currently working with a Fortune 500 global company that’s committed to fostering a data-informed, insight-driven culture. One division of the company started off with a data literacy program, then a few months later another division hired an amazing person who prefers the term data fluency. So, let’s analyze the differences of data literacy vs. data fluency.

Is one group right and the other wrong? 

When it comes to talking about the way we teach people to use data more effectively, we seem to be spoiled for choice. Are we offering them the ability to achieve data literacy, data fluency, data acumen, information literacy, analytics capability, data proficiency, digital dexterity, or some other label for this beloved topic? Software companies, universities, consultancies, and course and assessment providers all seem to have a slightly different take. Is this an issue? 

I believe this is an important discussion, because words matter depending on what we’re trying to solve for.  

In my work at The Data Lodge, I support individuals in companies and government agencies around the world to build out data literacy programs. A lot of work goes into creating the case for change, so clarity of purpose is critical. How should we then refer to what we’re building, and what do these choices mean?

The love-hate of data literacy

Many people I work with say that data literacy is “exactly what we need,” “such a missing link,” and “the root of so many issues and missed opportunities.” I also hear that “Fostering a data culture starts with language.” So the idea of literacy brings immediate appeal. 

For a smaller subset, the term can’t help but provoke a self-defensive reaction. “Are you implying that I’m data illiterate?” is one response we often hear, as well as, “Excuse me, I have a PhD in data science. This clearly doesn't apply to me.” As someone with an MS in Applied Mathematics, I'm all in on data literacy, though I also empathize with people who have an equally visceral but opposite reaction, such as “Um, I’m not a data person,” or “I’m not a quant.” 

Consistency, clarity and continuity

As leaders, I believe it’s incumbent upon us to lean into these reactions. Culture change starts with engagement and engagement starts with words and meaning. If you’re too loose with your terms and use a lot of synonyms, you’re essentially leaving it to your stakeholders to reconcile them. This can create unnecessary confusion and become a turnoff from the very start. So consistency matters.

Context also matters. By that I mean picking an umbrella term that's wide enough, but also doesn't include everything under the sun. You should appeal to something that just makes sense, so people can make it personal by relating it to something they know. Finally, clarity and continuity of the terms we use are important for program identity and branding, so that we create ongoing resonance. Eventually, these terms become woven into the fabric of adjacent topics, like the interdependent areas of data governance and self-service enablement.

So why am I a proponent of data literacy? 

  • Data literacy is about helping to upskill the workforce as part of a broader digital change effort. Data, data capabilities and data skills are therefore more relevant. It's not just a specialty practice anymore. 
  • Data literacy unlocks collaboration. We’re looking for ways to tap the unrealized collaboration among different participants in the data and analytics world. Data hoarding happens when there is insufficient understanding or appreciation of how that data can be used. And lack of collaboration comes down to distrust, which itself is a product of lack of understanding and often lack of a shared language.  
  • Data literacy leads to confident self-service. We will never free up the data and analytics professional talent we need to address the toughest problems we face as organizations if everyone is codependent on them. You have to enable self-service by lifting data literacy. 

It’s important to add that from a CDO perspective, you’re doing these things to unleash and ensure the realization of the value of your investments in data and analytics platforms, governing and creating certified data sets, and driving augmented and automated decision making.  

But why did I intentionally set anchor with the term “data literacy” in contrast to other options (fluency, acumen, etc.)? It’s simple: Data literacy is the lowest common denominator. 

More than a name: a mindset

Data literacy is a term and a goal, but what we’re solving for as data leaders is not just a level of acumen we can create by arriving at a shared language. It’s so much more than just words or concepts. 

  • It's about skills and behaviors. 
  • It’s about how we help people think more critically. 
  • It’s about developing a work skill set but also a life one. 
  • It's about engaging more collaboratively through effective data storytelling. 
  • It's about taking action and applying data in constructive ways.

Data literacy also lives at the intersection of three major imperatives currently at play in many organizations: the data and analytics agenda, which is about trusted data, platform modernization and enablement; the agenda to transform the digital nature of the organization (including initiatives about process automation and IoT, agile, and design thinking); and workforce transformation (i.e., Future of Work).

So, whether you prefer the term data literate or data fluent, remember that building data skills and confidence lives at an intersection. And this intersection means you have sets of stakeholders, human beings who are striving to foster the future of work and the abilities needed to bring it into practice.

In the next part of this series, I’ll discuss the three major personas we must keep in mind as we create programs that drive impact – whether you call them data literacy, fluency, proficiency, or something else entirely.