In the first two articles in this series, I’ve touched on how to intentionally frame the identity of a data literacy program and the general personas you will be serving with your program. For a final thought on how to improve data literacy in your organization, I’d like to wrap up with how to connect the functional whys and the technical hows of making data literacy efforts actionable. 

Here are some best practices I like to follow:

  1. Spotlight people already modeling strong data literacy in the organization. There's nothing like a good vignette or a showcase of someone who is already modeling a strong data literacy mindset, language and skills, to draw the contrast to inspire others. This can be a leader opening their meeting by reviewing metrics with a live dashboard and discussing potential what-if scenarios. Maybe it’s a story about how someone on the front lines is cognizant and mindful of the data they're entering. Anything that highlights how people understand and appreciate their role in strengthening data quality is valuable. 
     
  2. Make the most of self-paced courses and resources available. These can touch on data storytelling, on correlation and causality, or any topics that will help to set a foundation and encourage learning. My message is, leverage the heck out of what's out there, and don't waste your precious resources on things like describing the basics of correlation versus causality, or what is a T-test, or what makes up different types of data sets. Most of these offerings are free and online. At some point, of course, you’ll need to start customizing your program and making it contextual to your organization.
     
  3. Reach out to your learning and development organization. I currently work with data literacy leads from about 35 organizations, and have noticed that the CDO (or equivalent) usually is the primary champion for a data literacy program. If they’re done well, these programs often are designed and executed in partnership with the learning and development organization, and with citizen teams or communities of practice from different business units. 
     
  4. When in doubt, staff your data literacy lead internally. In some scenarios, the communications manager, training manager, data governance manager, or even the CDO may lead a data literacy program initially. But this is not sustainable since he or she already is wearing another hat. For some companies, a new role such as data literacy lead or manager is created, and due to cultural sensitivities, it is highly recommended that you exhaust internal candidates before hiring outside. This is also an exceptional professional opportunity for someone- maybe someone who is a flight risk due to lack of challenge, or someone who had initiated similar efforts in a local setting.
     
  5. Establish a baseline. Any good data literacy program should fit within the broader view of a CDO’s strategy and agenda, and this is where data and analytics maturity models tend to live. People always want to know where they are in an initiative and want to establish a baseline. A good analytics and data maturity model should look at key elements like the platform, the organization, the governance, and the culture. It's important to include culture, because if data quality, self-service and modernizing the platform aren’t going on in concert with the culture work, you're not going to get very far. For data literacy programs specifically, you should also establish your initial baseline – we do this with every client at The Data Lodge using our Data Literacy Program Maturity Model and Assessment.

There are many avenues you can take when it comes to creating a plan on how to improve data literacy in your organization. But ultimately, whatever brand you give your program, whoever you’re targeting, and however you’re helping people to learn the basic vocabularies and dialects of this language, be clear, be intentional, stay open, and stay curious. People will have a reaction to any term you use to describe what I call data literacy, so have your own conviction on why you picked the one you did. And whenever you are acting in your capacity as a data leader, make your program clear, make it compelling, make it relevant, and make it personal.