Let's try a thought exercise: imagine you’re reading a book but you don’t understand words or phrases. “Stop right there,” you’re thinking. “Why would I bother reading if I didn’t understand any of it?”
Well, let’s talk about that same idea applied to data. There are people who look at data every day to guide their decision-making, make a point, or come up with ideas. But many of these people never really learned how to interpret data--they never became data literate.
If entrusting data analysis and interpretation to those who don’t know how to do it sounds a bit dangerous, it can be. Data is heady stuff, and it only gets headier as we get more and more of it. So let’s talk about this data literacy thing and how you can encourage and help those you work with to develop it.
The TL;DR on the D-LTR
We know, we know: D-LTR doesn’t really work as a new, hip acronym for data literacy --we just really wanted the anagram.
Anyways, the smart folks at MIT broke down data literacy into four pieces for us:
- Reading data: Understanding what data is and what aspects of the world it represents
- Working with data: Creating, acquiring, cleaning, and managing data
- Analyzing data: Filtering, sorting, aggregating, comparing, and performing other analytical operations
- Arguing with data: Using data to support a larger story to communicate to an audience
Those are four fairly essential skills in the big data world we live in. So when only 33% of employees and 24% of business decision-makers feel confident in their data literacy skills, you know you want to reverse that needle.
Becoming data literate
Now that we know exactly what data literacy is, we can talk about how to get there. Here are two ways to help your team and coworkers become data literate:
Make data available
You can’t expect people to become good at reading if there isn’t anything to read. Make your organization’s data available in a data analysis or BI tool and expose it to your team.
Communicate with data and ask for the same
When you communicate decisions, show data that supports your “why,” like statistics or graphs. And ask the same of your team when someone else brings a new idea to the table. What analysis was done? What’s driving the decision outside of a gut feel or ad hoc conversations?
Like a bonsai tree, Rome, or any other good things, data literacy isn’t built in a day. It’s done gradually over time. But if you can give your team the tools to read, work with, analyze, and argue with data and encourage them to use those tools, you’ll be on your way.
