Comparison
Data-quality firewall vs data observability
Data observability detects anomalies after data has moved through pipelines and warehouses. A data-quality firewall governs operational records before they become trusted CRM, ERP, or AI state.
What observability does well
- Monitors pipeline freshness, volume, and schema drift
- Alerts data teams when warehouse or analytics layers break
- Supports root-cause analysis across ETL and dbt jobs
Where Refinery fits
- Governs individual records on live business paths before writeback
- Blocks, normalizes, or escalates risky CRM/ERP records
- Verifies approved writebacks and produces receipts
When to choose each
| Scenario | Observability | Refinery |
|---|---|---|
| Warehouse freshness alerts | Strong fit | Complementary |
| CRM duplicate before ERP sync | Detects late | Govern before sync |
| AI copilot context quality | Limited | AI Context Guard path |
FAQ
Can we use both?
Yes. Observability for pipeline health; Refinery for operational record trust on business paths.
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