How to Implement Data Quality Checks
Build automated data quality monitoring that catches issues before they impact downstream analytics and decisions.
What You'll Learn
This intermediate-level guide walks you through how to implement data quality checks step by step. Estimated time: 10 min.
Step 1: Define quality dimensions
Establish quality criteria for completeness, accuracy, consistency, timeliness, and uniqueness of your critical data.
Step 2: Implement automated checks
Write SQL or Python-based data quality tests using dbt tests, Great Expectations, or custom validation scripts.
Step 3: Set up freshness monitoring
Track when each table was last updated and alert when data exceeds its expected freshness SLA.
Step 4: Build quality dashboards
Create a data quality scorecard that shows overall health, trend of quality metrics, and drill-downs into specific issues.
Step 5: Establish remediation workflows
Define processes for investigating quality alerts, fixing root causes, and communicating data quality issues to stakeholders.
Frequently Asked Questions
What data quality tools should I use?▾
dbt tests for warehouse-based checks, Great Expectations for Python pipelines, Monte Carlo or Anomalo for automated data observability.
What quality checks should I start with?▾
Start with row count validation, null checks on critical fields, uniqueness constraints, referential integrity, and freshness monitoring.
How do I handle data quality alerts?▾
Route alerts to data owners, provide context on impact and potential causes, and track resolution time. Distinguish between blocking and informational alerts.