# What is data observability? Definition and Examples | Silicon

URL: https://meetsilicon.com/glossary/data-observability/

> Data observability is the practice of monitoring data pipelines and tables for freshness, volume, schema and quality issues so problems are found before users notice.

Observability tools usually watch tables and pipelines. Analytics reliability extends this to the metrics and dashboards people actually use.
## The usual signals
Observability tools typically watch five things: freshness (is the data recent), volume (did the expected number of rows arrive), schema (did the structure change), distribution (are values in their usual range) and lineage (what depends on what).
## Where it stops
Most observability tools watch tables and pipelines and alert data engineers. They usually do not know which dashboards, metrics and saved queries sit on top of a table, so an alert says a table changed without saying which reports are affected or who should be told.
## How Silicon handles it
Silicon focuses on the analytics side, meaning the metrics, dashboards and answers people use. It checks changes before they merge and alerts in Slack or Microsoft Teams with the content each change affects. Teams that need deep pipeline monitoring often use it alongside a dedicated observability tool.
FAQ
## Data observability questions
### Is data observability the same as data quality? +
Data quality is the goal. Observability is the monitoring that tells you when quality slips.
### Do small data teams need data observability? +
Usually the basics, meaning freshness checks and dbt tests on key models. Full observability platforms pay off once there are many pipelines and owners.
## Related
- Data freshness ->
- Schema drift ->
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