# Data observability · Glossary
The ability to monitor the health of data as it moves through pipelines, detecting problems such as late arrivals, unexpected volume changes, schema changes and distribution shifts.
[Glossary](/glossary) · Data governance and quality

# Data observability

     The ability to monitor the health of data as it moves through pipelines, detecting problems such as late arrivals, unexpected volume changes, schema changes and distribution shifts. It applies the monitoring discipline of software operations to data itself.

## Why it matters

     Data failures are often silent: a pipeline completes successfully but loads half the usual rows, or a source system starts sending nulls. Without observability, the first person to notice is usually a senior stakeholder looking at a wrong number.

     Good observability links alerts to lineage and ownership, so the right team is told what broke, which downstream reports and models are affected, and whether they should be paused.

## In practice

     For example, a subscription e-commerce business might monitor the freshness and row counts of its daily orders table. When a payment provider’s export arrives late, an alert pauses the morning revenue dashboard refresh and tells the finance analytics owner, instead of publishing an incomplete figure.

## Where Rodan fits

     Rodan instruments pipelines with freshness, volume and quality checks in [Data and Analytics Engineering](https://rodan.io/data-analytics-engineering) work, so data platforms can be operated by the client’s own team.

## Related terms

- [Data quality](/glossary/data-quality)

- [Data pipeline](/glossary/data-pipeline)

- [Data lineage](/glossary#data-lineage)

- [AI observability](/glossary#ai-observability)

- [Anomaly detection](/glossary/anomaly-detection)

- [Data contract](/glossary/data-contract)

- [Data drift](/glossary/data-drift)
HTML: https://rodan.io/glossary/data-observability