Most AI incidents are data incidents first. Teams often focus on model architecture while under-investing in data contracts, monitoring, and ownership.
A minimum control baseline
1. Data contracts on critical interfaces
Define schema and semantic expectations between upstream and downstream systems.
- required fields and allowed values
- null handling and late-arriving data rules
- versioning policy for breaking changes
2. Freshness and completeness SLAs
For every high-impact workflow, define expected update windows and completeness thresholds.
Without SLA-based monitoring, teams discover failures only after business impact appears.
3. Quality tests in CI/CD and runtime
Pre-release and runtime checks should cover:
- schema validation
- distribution drift detection
- label and feature integrity checks
4. Ownership and escalation
Every critical dataset needs an explicit owner, backup owner, and incident path.
Unowned data quality alerts quickly become ignored noise.
Data quality metrics that matter
- freshness lag by dataset tier
- failed contract checks per release
- percentage of model runs with full feature availability
- time to detect and time to recover for data incidents
60-day rollout approach
- Week 1-2: identify top decision workflows and critical datasets.
- Week 3-4: implement contracts and freshness monitoring for tier-1 data.
- Week 5-8: add drift checks, incident runbooks, and reporting cadence.
This is enough to move from reactive data firefighting to controlled operations.