Astronomer emphasizes the importance of continuous data quality checks to ensure the reliability of data-driven insights and decisions, utilizing Apache Airflow within its data platform. The company's approach involves orchestrating data flows from ingestion to dashboards using Airflow DAGs, with a focus on timely alerts for data errors and maintaining data integrity through structured checks. Their system features a standalone data-quality DAG for high-impact pipelines and integrated checks during table creation, leveraging SQL operators to facilitate these tasks. By implementing a robust architecture, Astronomer addresses potential data issues proactively, using alerts to notify the team of data anomalies and ensuring that data quality is maintained consistently. This setup not only enhances observability and trust in dashboards and models but also allows for iterative improvements and the integration of domain knowledge into data quality processes. The company shares its journey to encourage other organizations to adopt similar practices for reliable data governance.