Data Products: Ownership, SLAs, and Contracts
Cross-Industry • ~6 min read • Updated Aug 15, 2025
Context
Organizations aiming to scale AI cannot rely on ad-hoc tables and undocumented feeds. Treating data as a product — with explicit ownership, measurable service levels, and contractual boundaries — ensures trust, reliability, and accountability across teams.
Core Framework
Shifting to data products involves three structural pillars:
- Ownership: Each product has a named owner with budget and delivery accountability.
- Service-Level Objectives (SLOs): Availability, latency, freshness, and quality targets must be explicit and measured.
- Contracts: Defined schema, semantics, and change management rules protect downstream consumers from breaking changes.
Recommended Actions
- Define Data Domains: Group sources into logical products with clear boundaries.
- Appoint Product Owners: Assign leaders with both technical fluency and business accountability.
- Set Measurable SLOs: Publish baseline metrics and escalation paths when breached.
- Implement Data Contracts: Enforce schema validation and provide deprecation notices for changes.
Common Pitfalls
- Ambiguous ownership leading to orphaned datasets.
- Vague SLOs without automated monitoring.
- Breaking schema changes without notice, eroding trust.
Quick Win Checklist
- Assign an owner to your top 5 critical datasets.
- Define and publish 3–5 SLOs per product.
- Establish a lightweight contract review board.
Closing
Data products formalize the relationship between producers and consumers, reducing friction, increasing quality, and accelerating AI adoption. When ownership, SLOs, and contracts are explicit, AI teams can innovate on stable, trustworthy foundations.