Data literacy vs. data fluency, focus on what matters

Oct 2, 2022

Organizations across the world are talking about how they must become more data-driven. The skills of their workforce to use their data for decision making is key to win customers, work more efficiently and ultimately business survival.

That’s why many organizations put data upskilling high on their agendas. For example, ING describes data fluency as one of their Big 6 capabilities and is unleashing a data revolution with their data literacy program.

Did you spot the difference? Where ING is focussing on data fluency, is improving data literacy skills. So how is data literacy different from data fluency?

Data literacy vs. data fluency

In foreign language, literacy is the ability to read and write, while fluency is the ability to speak or write a language easily, well, and quickly.

Similarly, you can look at data literacy as the ability to read, write, communicate, and reason with data; data fluency is the ability to do the same but then more easily and accurately. In other words: data fluency goes beyond the skill level of data literacy.

So is ING aiming higher with their program than is doing? It’s not the name of the program, but the skills and the levels that matter.

The skills that matter

When designing your L&D program, it starts with determining what capabilities your organization needs to outperform competition. With this you can decide the data skills your employees need to be successful in their jobs. Different data personas need different skill tracks. For example, a track for a Data Consumer or Data Explorer looks entirely different from that of a Data Analyst.

What is the same is that every data persona needs both soft and hard skills. Writing data is mainly focussed on hard skills in coding and non-coding tools like SQL and Tableau. Communicating with data is all about soft skills – asking the right questions and communicating insights from data using narratives and visualizations.

Data literacy assessment

You can have different levels for different soft and hard skills. For example, skill levels in data can be Basic, Advanced, or Expert. Reading data on a basic level could be interpreting a bar chart, while drawing conclusions about causation from an experiment could be on an expert level.

You can use data literacy assessments to assess the level of your employees. With this you can get a structured overview of the skill gaps in your organization and assign courses, materials and projects to employees to grow their skills.

But what if your employees are on expert level on the data skills your organization needs? Did you achieve data fluency?

Always be learning

We are living in a fast-paced world. If your organization is data fluent today, it might not be anymore tomorrow. The key is to recognize that data upskilling is a continuous process, your employees should always be learning.

Instead of worrying whether to call your L&D program a data literacy or a data fluency program, think about how you can keep raising the bar in data upskilling at your organization. And if you don’t have an upskilling program yet: learn from organizations like Airbnb and Uber on how to get started.