Data observability is the ability to understand the health and performance of the data within an organizations systems. It is a process and set of practices that aim to help data teams monitor the quality, reliability, and delivery of data and identify issues that need to be addressed. Data observability covers an umbrella of activities and technologies that allow organizations to identify, troubleshoot, and resolve data issues in near real-time.

Data observability is important for data operations and the industry as it supports data quality and makes DataOps as a practice possible. It enables organizations to proactively identify data errors, pipeline issues, and locate the source of inconsistencies to strengthen data quality over time. Data observability is a continuation of the observability tradition in IT, with a focus on the needs of modern data-driven digital enterprises.

Key features of data observability include metrics, logs, and traces. Data observability platforms help organizations to discover, triage, and resolve real-time data issues using telemetry data like logs, metrics, and traces. Most data platforms operate on key areas of data observability such as data platform service monitoring, data pipeline performance monitoring, data quality monitoring, data lineage, and data discovery.

Even the best observability tools can fall short without insight into the full data pipeline and all the software, servers, databases, and applications involved. Therefore, it is important to eliminate data silos and integrate all the systems across an organization into the data observability software.

In summary, data observability is a critical concern for data teams, and it refers to the monitoring, tracking, and triaging of incidents to prevent downtime. It is a process and set of practices that aim to help data teams monitor the quality, reliability, and delivery of data and identify issues that need to be addressed. Key features of data observability include metrics, logs, and traces, and it is important to eliminate data silos and integrate all the systems across an organization into the data observability software.