Data Quality Assessment
1. Data Overview
Describe the data source, date range, total row count, and a brief summary of what the data contains. List the key columns and their types.
| Field | Value |
|---|---|
| Source | |
| Date range | |
| Row count | |
| Key columns |
2. Quality Issue Classification
For each quality issue discovered during profiling, classify it and assess its business impact.
Categories:
- Fixable -- can be resolved with a clear transformation (e.g., standardize casing, parse dates)
- Flaggable -- requires investigation or a decision before proceeding (e.g., unexpected patterns, potential data entry issues)
- Blocking -- prevents reliable analysis until resolved (e.g., primary key violations, impossible values in critical fields)
| Issue | Category | Evidence | Business Impact |
|---|---|---|---|
3. Business Impact Summary
Translate the technical findings into business terms. What do these quality issues mean for the analysis? Which findings affect which metrics? Write 2-3 paragraphs that a non-technical stakeholder could understand.
4. Recommendations
Prioritized list of actions. For each recommendation, state what to do, why it matters, and whether it should be done before or during the analysis.