Data Quality Scorecard: [Dataset / Table Name]
Assessed by: [Name]
Date: [YYYY-MM-DD]
Table / dataset: [schema.table_name or file path]
Pipeline / source: [data source and ingestion frequency]
Assessment scope: [full table / sample of N rows / specific date range]
Quality Dimension Scores
Rate each dimension 0–10 based on check results. See references/quality_dimensions.md for scoring guide.
| Dimension | Weight | Score (0–10) | Weighted | Key Issues |
|---|---|---|---|---|
| Completeness | 20% | |||
| Accuracy | 20% | |||
| Consistency | 20% | |||
| Timeliness | 15% | |||
| Uniqueness | 15% | |||
| Validity | 10% | |||
| Overall | 100% | /10 |
Overall verdict: PASS (≥ 7.0) / CONDITIONAL (5.0–6.9) / FAIL (< 5.0)
Critical Findings (must fix before production use)
| # | Dimension | Finding | Rows affected | Impact |
|---|---|---|---|---|
| 1 |
High Severity (fix within 5 business days)
| # | Dimension | Finding | Rows affected | Impact |
|---|---|---|---|---|
| 1 |
Medium / Low (document and monitor)
| # | Dimension | Finding | Notes |
|---|---|---|---|
| 1 |
Checks Performed
| Check | Script | Result | Details |
|---|---|---|---|
| Null audit | null_counter.py |
PASS / FAIL | N columns exceed threshold |
| Duplicate detection | duplicate_finder.py |
PASS / FAIL | N duplicate rows found |
| Referential integrity | referential_integrity.py |
PASS / FAIL | N orphan records |
| Value range validation | value_range_validator.py |
PASS / FAIL | N rule violations |
| Freshness | freshness_check.py |
PASS / FAIL | Lag: Xh (SLA: Yh) |
Sign-off
Approved for use in: [dashboards / reports / ML features / all uses / none — pending fix]
Approver: [Name]
Review date: [Next audit scheduled for YYYY-MM-DD or "on next pipeline update"]
Action items:
| Action | Owner | Due date | Status |
|---|---|---|---|
| Fix [issue] | [team] | [date] | Open |