Data quality and data observability are two closely related concepts that work together to ensure the accuracy, reliability, and trustworthiness of data. Data quality describes the condition of data relative to its intended use or organizational standards, while data observability provides visibility into the health and performance of data pipelines and systems in real-time. By combining these two approaches, organizations can proactively manage data quality, detect anomalies, and surface alerts to ensure reliable data systems at scale. Key differences between data quality and data observability include that data quality is a goal, while data observability is a method; data quality is broader in scope, encompassing everything from data entry standards to cleaning and governance; and data observability is proactive, continuous, and focuses on monitoring key indicators such as data freshness, volume, and schema changes. By implementing best practices for combining observability with data quality efforts, including defining what "good data" means upfront, deploying observability on key data pipelines first, integrating alerts into workflows, continuing to implement data testing and governance, and leveraging observability insights for root cause analysis and prevention, organizations can build a comprehensive approach to ensuring high data quality and observability.