By Robyn Defelice
I got into L&D for the empowerment of the learner, the creativity, the human side of the work, not spreadsheets. If someone had told me twenty years ago that I’d be passionate about data, I would have asked you what flavor your Kool-Aid was. But the work demanded more, and being good with numbers turned out to be how I got there. It allowed me to become more efficient, more savvy, more capable of stretching into challenges I didn’t see coming as an L&D professional. Turns out, “being good with numbers” was data literacy. I just didn’t know it.
What data literacy even is and why is it valuable at this juncture with AI being the “thing du jour” for L&D? Because every demand the industry is placing on you right now requires more than being good with numbers.
- Proving your value isn’t a math problem, it’s knowing what story to tell or present with the data and who you’re telling it to.
- Being strategic means interpreting, prioritizing, filtering the noise from your data.
- Showing impact requires connecting what happened to why it matters using data.
- Making informed decisions means knowing what data to collect, what to ignore, and recognizing your own bias in the process.
That’s data literacy. And while AI is reshaping how we work with data, it doesn’t remove the need to know what questions to ask, what to trust, and what to do with the answers. So how do you get started (or know you are on the right track) to being (becoming) data literate?
First by recognizing what literacy in this context means. Being data literate does not mean just reading the numbers. It’s the mindset to see data in your work, the skill set to do something meaningful with it, and the tool set to support both. We’ll explore what that looks like below and continue in a series of articles, each one building on the last.
Pantry Basics: The Shared Language of Data Literacy
Data literacy begins with language.
Think of it like cooking. Before you make a meal, it helps to check the kitchen. What ingredients do you have? What equipment are you working with? How much time do you have? Is it just you or do you have help? Some of us check every time. Some of us wing it and figure it out along the way.
In L&D, the winging-it approach tends to win out. We take on projects, commit to timelines, and promise deliverables, sometimes without a clear picture of what we’re actually working with. Data literacy starts with that inventory. Know your kitchen. You make fewer trips to the grocery store when you do.
And kitchens rarely come with labels on everything (my dream one does). You need to know the difference between salt and sugar before you start seasoning.
Same principles apply here: Shared terminology helps you think more clearly, communicate more precisely, and make better decisions. Whether this is new territory or familiar ground, getting comfortable with the language is what lets you use it with confidence. These aren’t the only terms you’ll encounter, but they’re where it starts. Each one connects to work you’re likely already doing.
| Term | What It Means | L&D Example |
| Data (Quantitative & Qualitative) | Raw facts, numbers, or responses. No meaning on its own until it’s processed. | Completion rates sitting in your LMS. A stack of survey responses you haven’t reviewed yet. |
| Information | Data that’s been analyzed, contextualized, and connected to a purpose. | “Completion dropped 20% after we shortened the module from 60 to 30 minutes.” Now you know something. |
| Quantitative Data | Anything you can count or measure numerically. | Assessment scores, time-to-completion, cost per learner, number of training hours delivered. |
| Qualitative Data | Descriptive, interpretive data. Often used to tell the why behind the numbers. | Open-ended survey responses, post-training interview feedback, focus group interviews. |
| Empirical Data | A method of gathering data through direct observation, experience, or experiments. Can be quantitative, qualitative, or both. | Tracking how long your team’s design-to-delivery cycle actually takes versus what’s assumed. |
| Aggregate Data | Data combined into summaries: totals, averages, and overall counts. | “500 employees completed training.” Reads solid as a snapshot, until you break it down and find one department completed at 95% while another sat at 40%. |
| Objectivity vs. Subjectivity | Objective data isn’t shaped by interpretation. Subjective data is shaped by the perspective of the person doing the interpretation. | A completion rate is objective. A satisfaction rating is subjective (e.g. on a scale of 1 – 5). Neither is better, but knowing which you’re working with changes how you use it. Objective data can become subjective when you start interpreting what it means. An 80% completion rate is a number. Whether that’s “good” or “a problem” depends on who’s reading it and what they expected. |
| Bias | Assumptions baked into how you design, collect, or interpret data. | Asking “describe a time training fell short” assumes a negative experience. Interpreting low completion as “learners aren’t motivated” without confirming that is why completion was low is also biased. |
| Actionable Insights | A finding from your data that directly suggests a course of action. Not just interesting to know, but clear enough to act on. | 67% of new hires didn’t feel confident after onboarding. Most cited lack of shadowing time with others in the same role. That insight gives L&D something specific to bring to HR and managers: How do we build in more exposure opportunities? |
These terms work together. Data becomes information through analysis. The types of data you collect, quantitative, qualitative, or both, and how you gather them, including empirical methods, set the stage for that analysis. Aggregate data is often where the work stops, but it’s rarely where the insight lives. Whether what you’re looking at is objective or subjective changes how you interpret it and how much weight you give it. Bias can influence any part of the process, from how you design the collection tool (a survey, an interview, a feedback form), to how the data is gathered, to how you read the results. The goal across all of it is actionable insights, findings clear enough to drive a decision.
Checking Your Data Literacy Pantry
Take stock of what’s already in front of you: That project you’re in the middle of, that report you just pulled, that survey you’re about to send. Answer these questions to assess where you are in gathering, understanding, and using data that you already have at hand (or can obtain easily):
- What data do I regularly touch or produce?
- Is it data or has it become information? If it’s just data, figure out what would turn it into information.
- What type is it: quantitative, qualitative, or both? If it’s only one, consider what the other could tell you.
- Am I only looking at aggregate, or have I broken it down? If it’s all totals, try breaking one thing down and see what shows up.
- Is there bias in how it was collected or how I’m reading it? If you spot it, rethink the question.
- Is any of it leading to an actionable insight, or is it just sitting on the shelf? If nothing is leading to a decision, ask why you’re still collecting it.
Data literacy isn’t a destination. It’s a practice, and practices only improve when you know what you’re working with. Take the inventory. Start with one item. See what it tells you about how you’re actually working with data versus how you think you are. And once you’ve done that, the next question becomes: where do I actually stand? In the next article we’ll get into baselines and benchmarks and why knowing the difference matters as you begin setting data goals.
Explore Ways You Can Use Data to Show Your Impact
Join us at the Communicating L&D’s Impact online conference, April 15–16. You’ll learn to identify critical metrics and indicators, show connections between learning initiatives and the organizational results they drive, and explore tools and strategies that will help you improve alignment and communicate your results. Register today!
Image credit: fcafotodigital
