Defining Data Literacy for the Enterprise
Wikipedia defines data literacy as “the ability to read, understand, create and communicate data as information.” It’s as good a definition as any, but what I find striking about it is the number of action verbs. Being data literate, in other words, requires you to do a lot of things.
However, when it comes to enterprise data literacy, I feel there needs to be some additional context. Where I think it’s important to draw a distinction is that everyone in an enterprise isn’t required to master all these tasks to be data literate. Those of us with data or business intelligence in our title are more likely to be charged with creating and communicating data, whereas colleagues who deal more directly with customers need to read and understand data.
The Venn diagram of data literacy
If you imagine data literacy as a Venn diagram, data professionals in one circle need to be able to communicate data and a message in such a way that we make it easier for users in the overlapping circle to understand what’s there, and to feel assured enough in what the data is saying to be able to tell a story with that data as a subject matter expert. I often find that data literacy discussions can be very much focused on one of those circles and not the other. What’s typically implied is that the data literacy conversation is about the data professional.
This may be because people find it easier to define data literacy by ticking off a list of data-related competencies. But the onus is on us as data leaders to be equally interested in those who don’t hold the data roles, those who need to understand almost by necessity to be successful in their work. These users must interpret and use and understand and work with data because of the way their role has progressed or because that’s simply what is now required of them.
The story’s the thing
Our clients make decisions based on how we present the data, so being literate as a data leader means being able to find and tell stories with data that help end-users to make sound decisions. In JLL’s world, these decisions could result in new buildings, different workplace patterns, or new sustainability strategies, to name a few areas.
Another way to think about data storytelling comes from Dr. Selena Fisk’s book I’m Not a Numbers Person, where she notes that making data-driven decisions requires “rigorous and authentic engagement in data storytelling.” That resonated with me because so many of us still work remotely, but we still need to have compelling conversations with external or internal customers. A facility with data is what gives you a level of confidence, understanding and communication so any stakeholder involved in a decision can then make a data-informed decision.
Pushing for enterprise data literacy
Data literacy never springs into being organically at a company. Ideally you have a Chief Data Officer and data literacy is their passion and they sponsor the effort from the top all the way down. Very often, though, this isn't the case. It’s more a function of middle data leaders like me pushing up to the senior ranks and down to the front lines at the same time. We want to spread the message and help people with data literacy, but we also need to reach VPs and senior managers to say, “We really need to implement this right across the organization, all the way up to the board.”
As you make your case, always be prepared to offer KPIs. With something as foundational as data literacy, these could be:
- Percentage of department employees trained
- Percentage of employee engagement in ongoing learning
- Certifications in specific data analytics tools
- Increase in usership and adoption metrics
Ultimately, the aim of data literacy at the enterprise level is to push beyond today’s status quo. That may require that data people or senior data people become data evangelists. By taking a more holistic view of the definition of data literacy and how it works, we can all move beyond seeing the artifact as proof. By that I mean, it’s great to have data analysts who can produce dashboards with all the bells and whistles that look beautiful and clearly show talent, experience and analysis level, but unless they can be read and understood and communicated to the intended audience (usually those of us without data in our titles), they probably won’t have the impact they should.