Data Literacy: One Question at a Time

For many of us, when the term “data” enters the conversation, it creates distance. We either find ourselves requesting it from others or consuming it passively when it arrives. That mindset—”I request” or “I consume”—can make us feel like bystanders. But here’s the truth: You’re already more than that. You’re a partner in your data.

And becoming a true partner doesn’t require a new title or a technical background; it just requires a mind shift. Data literacy isn’t about becoming fluent in formulas or dashboards. It’s about recognizing that you belong in the conversation. That your questions, your insights, and your curiosity are vital.

Start seeing ‘pieces’

When working with learning data, think of it as searching for one specific building brick in a disorganized pile, without knowing exactly which you’re looking for. At first glance, everything blends: rows of learning records, seminar participation, and engagement metrics.

But as you build your data literacy, your “data lens,” things begin to shift. You stop seeing bricks—and start seeing pieces. That specific brick you’re looking for suddenly pops out. You recognize its shape, its function, its potential.

That’s what insight feels like. You stop shifting and start assembling, giving your work a competitive edge—while others continue staring into a sea of chaotic blocks.

Clarifying the language & shifting the lens

Before we move on, let’s pause and define a few terms—starting with the word “data” itself.

In the learning space, terms like data, data literacy, and data governance aren’t just buzzwords from the IT department. They’re tools for seeing and doing your work differently. This Data Literacy Glossary can help you understand these tools. 

• Data is simply information. It can be a number, a label, a comment, a timestamp, information from an existing report, anything you collect to understand what’s happening.
• Raw Data is unfiltered information, straight from the source. Think: LMS exports, survey responses, unstructured logs. It hasn’t been cleaned or formatted yet. Even processed reports used as inputs for new insights can be considered raw data.
• Processed Data is organized and usable. It’s what you see in dashboards and reports, cleaned up, labeled, and easier to interpret.
• Data Literacy & Data Governance mean being able to read and understand the data, knowing where it comes from, and ensuring it’s accurate, reliable, and accessible.

A brief note on scope: The data landscape, like the learning realm, involves multiple levels of governance related to quality, privacy, and compliance. While this article doesn’t dive deeply into these technical aspects, it’s worth acknowledging their importance. My focus here is on bridging the data literacy gap, helping you move past intimidating terminology to leverage your existing skills for better insights. For those interested in the technical concepts of data governance, that’s a worthy topic for future exploration.

While there are plenty more terms you could learn, just grasp these for now. This isn’t about memorization. It’s about seeing reports or dashboards as a starting point for new data exploration. When you start seeing these tools and metrics as potential raw material for discovery, everything changes.

Starting points

After I connected my reports, I started to see how alone they told one story, but together they told me a new story, a more complex story. That’s when my Aha! moment happened. I realized that my dashboards and reports weren’t the answers; they were my starting point. This allowed me to expand my literacy and knowledge and to start asking better questions that connected learners to skills, learning activities to business outcomes, action mapping to objectives, and learning tasks to strategic goals.

And here’s why that shift is so urgent: Nearly 68% of enterprise data remains unused for analytics (Seagate/IDC, 2024). In higher education, while organizations are collecting information using data information systems, 62% are missing vital opportunities by not utilizing that data effectively or at all. (G2, 2024).

“If we want to change outcomes, we need to change our relationship with data. That starts with going from the Data Requestor/Consumer to a Data Partner.” — Bobby Deibler, Learning 2024 Conference, December 2024

Becoming a partner

So, what does it mean to go from a consumer of data to a partner?

It starts with the mindset shift we expressed above. While consumers wait for reports to arrive in their inbox, partners ask for the right data, explore patterns, and push for deeper understanding. They send back reports that answer the right questions; they are subject matter experts at the design table with IT.

You already know how to do this; you do it with your client subject matter experts. You don’t expect them to hand you everything on day one. You engage them, ask questions, clarify meaning, and co-create learning experiences.

It’s time to do the same with your data. Here’s are simple steps to get you started:

1. Document your reports: Create a simple inventory of all the learning reports you use. List each report’s name, purpose, and key metrics. Think of this as creating a map of your data landscape that helps you see how different reports connect.
2. Identify your data sources: For each piece of data, trace its origin and destination. Where does this information come from initially (upstream)? Who collects it? Where does it travel next (downstream)? Understanding this reveals opportunities for connection and exposes potential data quality issues at their source.
3. Know your data types: Recognize the difference between categories (departments, job roles), measurements (completion rates, assessment scores), timestamps, and text responses. These span both quantitative data (like measurements and timestamps) and qualitative data (like open-text responses). Each type requires different analysis approaches and has unique strengths and limitations. While these are some of the most common, they represent just part of the broader data landscape.
4. Question your data: For each field, ask: What should this be telling me? What might it be hiding? Is it capturing what we think it’s capturing? These questions transform passive report reading into active data exploration.

The more you engage with your data, even at a basic level, the more confident you’ll become. You’ll stop waiting for answers and start spotting your own insights.

Now if you look back at your pile of building bricks, you’ll be quicker to pick out your brick.

Suddenly, you’re seeing connections across surveys, courses, and learner behaviors that you would have never noticed before.

As you request more reports, you begin asking more impactful questions. A report crosses your desk, and you find yourself thinking, “Can I get that broken out by department?” When you do, a pattern emerges: IT has lower completion rates compared to HR. Curious, you dig deeper and discover that IT teams often learn just in time when a problem arises, while departments like HR tend to follow more traditional, compliance- driven learning models.

You’re no longer a passive recipient. You’re helping shape the narrative.

You become someone who helps facilitate and push data literacy into the parts of the organization that need it most — where data is consumed, acted on, and brought to life.

Moving From Knowledge to Action

Building data literacy is a journey, not a destination. Here are actionable steps to take over the next month:

Set your baseline

Before you begin let’s take time to check in. What’s your skill level?

In the article’s resources, you will find a Self-Assessment matrix to help you with your self-assessment score.

After reviewing the Data Literacy Self-Assessment Scale, where do you feel you fall? There is no right or wrong answer, and this is a score for you to monitor.

Week 1: Documentation

• Document one critical report that impacts your learning decisions
• Identify all fields, their meanings, and their origins
• Create a simple data dictionary for these reports
• Schedule a meeting with the reports owner or creator to validate your understanding

Week 2: Connection

• Reach out to your data team (or whoever handles analytics in your organization)
• Share your data dictionary and ask for feedback
• Inquire about other data sources that might complement these reports
• Establish a regular check-in schedule with your data partners

Week 3: Expansion

• Add a second report to your documentation
• Look for connection points between your two documented reports
• Begin sketching what a combined view might look like
• Identify gaps in your understanding and list questions to explore

Week 4: Application

• Start asking one new question each time you review your reports
• Share one insight with a stakeholder for feedback
• Skill Review—Take a look back at the skill assessment you took prior to Week 1. How has your “data lens” changed? Have you noticed any new skills?