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People often say you can't manage what you can't measure. This statement applies to the software development lifecycle as well. It's essential to capture the pipeline telemetry data to calculate critical metrics, such as change lead time.
In pipeline observability, conditions such as failed builds and excessive lead time can be observed and acted upon automatically. With automated data, metrics such as mean time to resolve and DORA metrics lead time for changes, deployment frequency, and change failure rate are readily available in several dimensions, including application/service, platform service/pipeline, and technology and ownership.
In addition to metrics, logs and traces of pipeline runs can help debug erroneous pipelines or identify time-consuming hotspots that can be optimized for more efficient pipelines.