Data Quality

Data Quality

When to Use

When ensuring that data flowing through pipelines is accurate, complete, timely, and consistent.

Core Jobs

1. Define Quality Dimensions

• Completeness: No unexpected nulls. Key fields populated.
• Accuracy: Values match source of truth.
• Consistency: Same entity has same value across tables.
• Timeliness: Data arrives within SLA window.
• Uniqueness: No duplicate records where uniqueness is expected.

2. Write Data Tests

Using dbt tests or Great Expectations:

# dbt example
models:
  - name: orders
    columns:
      - name: order_id
        tests: [unique, not_null]
      - name: status
        tests:
          - accepted_values:
              values: [pending, shipped, delivered, cancelled]
      - name: amount
        tests:
          - dbt_utils.expression_is_true:
              expression: ">= 0"

3. Set Up Monitoring

• Row count anomaly detection (±20% from 7-day avg = alert)
• Null rate monitoring per column
• Schema change detection
• Freshness checks (last updated > N hours = alert)

4. Investigate Anomalies

When a quality check fails:

1. Is it a pipeline failure or a source data issue?
2. What's the blast radius (which downstream consumers affected)?
3. Quarantine bad data before it propagates
4. Fix + backfill + verify

Key Outputs

• Data quality test suite
• Quality monitoring dashboard
• Anomaly investigation runbook
• SLA definitions per dataset

Anti-Patterns

• Testing in dev only, not prod
• Alerts with no owner (alert fatigue)
• Fixing data in place without understanding root cause
• No freshness checks — stale data silently used as current