Data Versioning Intelligence
The silent data change, caught.
Every structural check is green — schema, freshness, row count, nulls — and the business number is still wrong. DVI detects the semantic change, attributes it to the deploy, and maps the blast radius.
The pipeline
How it works
Profile
Snapshot each column before and after a change — distributions, not just schemas.
Detect
Signature detectors flag semantic shifts a structural check can never see.
Rank
Corroborate symptoms against the lineage and rank the most likely root cause.
Blast radius
Project the change onto downstream assets and the external consumers at risk.
What DVI sees
The signatures
Value substitution
A category is silently renamed — "UK" → "United Kingdom".
Distribution shift
A numeric column drifts beyond its historical spread.
Cardinality change
The set of distinct values quietly grows or collapses.
Format drift
Casing or formatting mutates under a refactor.
Evidence
Measured, not marketed
- 100%
- Injected recall
- 0
- Real-vs-real false positives
- 0.047
- Out-of-fold ECE
Validated on 53,940 rows of real data. Detection is deterministic — an LLM may narrate evidence, but never decides whether something changed.
Add DVI to your pipeline.
One command, deterministic detection, a pull-request comment when a number silently moves.
$ pip install dvi