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Audit your data across completeness, accuracy, consistency, and timeliness dimensions. Emphasis: data quality checklist · data audit checklist. Ship smallest slice that proves the riskiest assumption.
🇨🇦 Canada · CAD · 2026-03-26, 4:40:24 p.m.
Educational / planning output only — not professional tax, legal, or investment advice.
Audit your dataset across 5 quality dimensions: Completeness, Accuracy, Consistency, Timeliness, and Uniqueness — 3 checks per dimension.
Completeness
All required fields are populated with no nulls
Record counts match expected source volumes
No truncated or partially loaded records exist
Accuracy
Field values match validated reference data
Numeric values are within expected business ranges
Date fields contain valid, logical dates (no future birthdates, etc.)
Consistency
Same entity has consistent values across systems
Lookup/enum values conform to defined code lists
Naming conventions are applied uniformly across datasets
Timeliness
Data is updated within the agreed SLA (e.g., daily/hourly)
No stale records older than the defined refresh window
Pipeline run timestamps are logged and monitored
Uniqueness
Primary keys are unique with no duplicates
Deduplication logic has been applied to merged datasets
No duplicate customer/entity records exist in master data
DATA QUALITY AUDIT REPORT ================================================== Date: 3/26/2026 Score: 0 / 15 passing COMPLETENESS (0/3 passing) ---------------------------------------- [ N/A] All required fields are populated with no nulls [ N/A] Record counts match expected source volumes [ N/A] No truncated or partially loaded records exist ACCURACY (0/3 passing) ---------------------------------------- [ N/A] Field values match validated reference data [ N/A] Numeric values are within expected business ranges [ N/A] Date fields contain valid, logical dates (no future birthdates, etc.) CONSISTENCY (0/3 passing) ---------------------------------------- [ N/A] Same entity has consistent values across systems [ N/A] Lookup/enum values conform to defined code lists [ N/A] Naming conventions are applied uniformly across datasets TIMELINESS (0/3 passing) ---------------------------------------- [ N/A] Data is updated within the agreed SLA (e.g., daily/hourly) [ N/A] No stale records older than the defined refresh window [ N/A] Pipeline run timestamps are logged and monitored UNIQUENESS (0/3 passing) ---------------------------------------- [ N/A] Primary keys are unique with no duplicates [ N/A] Deduplication logic has been applied to merged datasets [ N/A] No duplicate customer/entity records exist in master data SUMMARY ---------------------------------------- Total Checks: 15 Passing: 0 Failing: 0 N/A: 15
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