What we check
- Duplicate learners, courses and enrollments
- Missing or conflicting completion dates
- Completions recorded against a course version that did not exist at the time
- Orphaned enrollments with no learner or no course
- Scores outside the scale of the assessment
- Required fields absent from the source export
- Records that changed between two extractions of the same period
Evidence, not just counts
Each issue links to the affected records, the source values and the rule that fired. Your team can resolve, accept or annotate an issue, and the decision is recorded. A quality score per dataset is available to anyone building on it.
Quality gates
Promotion from staging into the governed layer can be gated on quality thresholds you define, so a bad export never silently overwrites good history.
Continuous monitoring
For ongoing connections, checks run on every extraction and trends are visible over time, which is how a change in an upstream system gets caught the day it happens rather than at the next audit.
Ready to see your learning data in one place?
Tell us which systems you run and we will show how the platform connects, normalizes and preserves their records.