Until the last 15 years or so, data literacy was about being able to comprehend data given to you. If you could understand the data tables in an Excel report at face value, for example, you were data literate. 

More recently, data literacy is also about being able to interact with data, use it to form opinions, and work with it. 

Both examples use what I call “prior data,” data that already existed. 

The future of data literacy, I think, is about being able to implement AI into your decision-making based on data models. AI-driven decisions help you because you don't have the information yet, but there are scientific models that help you hazard a best guess and be able to use the results in order to be a little bit ahead of a curve rather than working from behind.

Getting from static data tables to predictive algorithms doesn’t happen overnight. Here are a few steps that data leaders should consider to improve data literacy to be the kind that creates differentiation today.

Define a common language of data
Data leaders need to ensure that teams are working from a common language and a single source of truth that people can relate to. With a common language, you can share that information across teams. This substantially raises efficiency, limits confusion, increases confidence, and helps to dissolve silos where people don’t communicate. Which means they can’t possibly collaborate.

If your teams are data literate, they will be more interested in producing something interactive that drives future activity versus a rearview mirror artifact. All you can say with a document of that kind is, “Well, there's not much I can do about it right now.”

Democratize reporting
A clear sign of a weak or absent data culture is that a single team focuses solely on creating reports. They aren't working with subject matter experts, they don’t really understand the business, and they simply push out basic data. This is why static reports sent out via email tend to have a ten percent click rate. 

By improving data literacy skills and training more people in data visualization, the output becomes far more interactive and therefore interesting. You’re likely to see a much higher participation rate as well as more people collaborating and working together to use the data. The people creating data products understand the business, and therefore the business users understand the value of the data products. 

Another dividend is that you will tend to see the good, bad and ugly in the data. I worked in the public and private sector, in some cases, the unspoken assumption from management was that they wanted to see good news. That meant that any report always led with good news, and the negative news often was thrown into a table where they wouldn’t look, or wouldn’t get to as they scanned. 

This is why it’s so important that you have a team of people that go beyond simply churning out reports. People who share data products need to be vested in what they do, using accurate data and giving you something you can use to take action. Otherwise, you’re just putting yourself in a deficit.

Push past AI anxiety
To move beyond rear view mirror reporting, you have to get on board with AI. Because my current company rightly believes in the power of AI to supplement human insight, we’re working with Carnegie Mellon to build an AI academy. This is a data literacy and AI literacy program for all employees. Training every employee on these topics has a powerful impact because it provides an opportunity for colleagues who haven’t even worked with data to understand what's going on and how to use it. It also gets them thinking about potential use cases that could benefit their teams or teams across the organization.

As with any literacy and training program, it’s your responsibility as a data leader to follow through and to choose use cases that are relevant to your colleagues. Without ongoing monitoring or feedback, training will be seen as a one -time event and be placed on the back burner. You need to be committed to developing a team, pushing forward, and recognizing those that are participating more fluidly. 

Letting personal growth flourish
One of the best outcomes of growing true data literacy is that you often will see a colleague doing something else in another department. Then they uncover a passion relating to working with data or data visualization. Within months they’ve progressed to become a product owner. They may change their entire career trajectory and be a lot happier about it. 

So take the time to provide incentives for those colleagues that are really taking data seriously and working to improve their data literacy. Give them ongoing support to make sure that their inspiration. And remember that as a data leader, you really take on the role of trainer and mentor once the official literacy program is over.