Data Observability

A practical example

Example: a nightly load completes successfully but delivers half the usual rows; a volume monitor should flag the anomaly and identify affected downstream dashboards before morning reviews.

What to evaluate before investing

• Check which monitor types are included out of the box: freshness, volume, schema, distribution and null-rate checks each catch different failures.
• Ask how anomaly thresholds are set, whether they learn per-column baselines or need manual ranges that go stale.
• Verify incident routing: can alerts reach the owning team with lineage context, or do they land in a generic channel?

Limitations and tradeoffs

Observability reduces detection time but does not prevent failures; teams still need ownership, runbooks and incident process, or alerts become noise nobody acts on.