Role-Based Data Literacy Map
A practical map of what data literacy should actually look like at each level and function of your organization.
One-size-fits-all data training rarely sticks.
A frontline manager and a finance analyst don't need the same data skills. Mapping literacy by role — rather than running the same generic course for everyone — is what makes a program actually change behavior.
One-size-fits-all training gets low completion and lower retention
No shared definition of what "data literate" means for a given role
Hard to prioritize which roles need investment first
Segments, competencies, and a target for each.
A structured map for defining what data literacy actually means, role by role, and where the current gaps sit.
Role Segments
Grouping roles by how they actually use data, not by department.
Core Competencies
The specific skills that matter for each segment — not a generic list.
Proficiency Levels
What baseline, working, and advanced actually look like in practice.
Gap Identification
Where current skills fall short of the target, segment by segment.
Learning Pathways
What to invest in first, sequenced by segment and impact.
| Role segment | Data foundations | Analysis | AI fluency |
|---|---|---|---|
| Frontline manager | |||
| Finance analyst | |||
| Product leader | |||
| Executive team |
Built for the people who own this problem
Designing a training program that fits how each role actually works.
Wanting a structured, role-based starting point.
Aligning learning investment with real skill gaps.
Sponsoring their team's data literacy development.
Three steps to put it to work
Segment your roles
Group roles into three to five segments based on how they use data.
Define the target
Set the target proficiency level for each segment and competency.
Prioritize the gap
Identify the highest-priority gap to close first, and start there.
Build a data literacy plan that fits each role.
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