18 July 2019
8 min
Industrial Data Contracts: The Missing Layer in Most AI Projects
Most ML failures are data interface failures. Contracts prevent silent breaks and enable scale.
DataGovernanceIndustry 4.0MLOps
Most ML failures are data interface failures. Contracts prevent silent breaks and enable scale.
Framework
- Schema: types, allowed categories, ranges
- Units: explicit measurement units and conversions
- Time: timezone, frequency, gaps policy, ordering
- Versioning: contract versions and change management
- Validation: automated checks in pipelines
Pitfalls
- Mixed units across sites (°C vs K) without detection
- Timestamp drift and timezone mismatches
- Hidden leakage in aggregates computed after the event
Portfolio deliverables
- Contract file (YAML/JSON) + validation rules
- Data quality dashboard (null-rate, drift)
- Incident playbook for contract violations
Good practice
Ship a baseline + monitoring first. Then iterate with evidence.
FAQ
Do we need contracts if we have a data warehouse?
Yes. Warehouses store data; contracts enforce meaning and stability.
Where do contracts live?
Next to the pipeline code and monitored in production (as tests + dashboards).
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