Data observability is the practice of continuously monitoring the health of data as it moves through pipelines and systems. It helps teams detect issues such as missing records, unexpected changes in volume, broken pipelines, stale datasets, or unusual values. Rather than checking data only after a problem appears, observability provides visibility into what is happening across the data environment.
For example, an analytics team may use data observability to identify when a daily sales pipeline stops delivering records or when a sudden drop in transaction volume affects a dashboard. By tracing problems to their source, teams can resolve issues before unreliable data reaches downstream users or models. Related concepts include data quality, data lineage, data pipelines, and data monitoring.
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