Decision criterion
Quality measured after the fact fixes nothing.
Most organizations buy a data quality tool, plug it in downstream of the warehouse, and discover anomalies the next day in a report nobody acts on. beVault reverses the logic: rules live inside the platform, run during loading, and make reliability visible the moment the figure is produced.
- Rules executed with each load, not in a separate batch
- Quality score linked to full lineage
- Exceptions routed to an identified owner
- No additional licenses or integrations
The problem
Why data quality projects run out of steam
It's almost never a tooling problem: it's a problem of position in the chain.
Detection too late
The anomaly is found after publication. The business has already seen the wrong figure, and trust is lost faster than it's rebuilt.
Loss of lineage
The external tool sees a table, not a satellite fed by three sources. It's impossible to pinpoint the real origin of the defect.
Duplicated rules
The same checks end up rewritten in the ETL, in the quality tool and in the reports — with three different results.
No owner
An anomaly report with no resolution workflow produces no correction. Quality stays a metric, never an action.
Comparison
Integrated Quality or Separate Tool
Compare on equal terms, including integration and run — that's where the gap widens.
| Critère | beVault (native) | Outil de qualité séparé |
|---|---|---|
| Control moment | During Data Vault loading | Post-publication, in batch |
| Granular lineage | From source field to mart | Limited to Observed Tables |
| Anomaly management | Exception workflow with an owner and follow-up | Report for manual exploitation |
| Integration cost | None: same metamodel | Connectors, synchronization, maintenance |
| Additional License Required | None — included in beVault | Yes — third-party license required |
The Data Vault principle
Never reject, always qualify
The Raw Vault keeps the data exactly as it arrived, even if it's wrong. That's what lets you, two years later, reconstruct what the source system stated at the time — and prove the correction happened downstream, in a documented, versioned rule.
Next step