Architecture Lens

Trusted AI depends on product-grade data

AI systems inherit the quality, ownership, access, and context problems of the data below them. Treating key datasets as products gives engineering and business teams a shared way to define purpose, consumers, quality thresholds, lineage, retention, and support expectations.

  • Give every critical dataset an owner, consumer definition, freshness target, and quality contract.
  • Connect catalog metadata, semantic definitions, feature sources, retrieval indexes, and reports.
  • Monitor data quality as an operational signal, not a periodic cleanup activity.
Trusted data product chain
Sources

Operational systems, documents, events, and external data.

Contracts

Ownership, quality, retention, and access rules.

Products

Curated datasets, semantic models, and retrieval indexes.

Consumers

Analytics, AI workflows, apps, and decision teams.

Original InSkyto diagram informed by NIST AI RMF trustworthiness concepts.

References

NIST AI Risk Management Framework

Delivery Pattern

Design for reconciliation and lineage

Modernization work should include parallel validation, reconciliation reports, lineage views, and signoff steps so stakeholders can trust new analytics and AI outputs before old processes are retired.

Operating Model

Data quality needs a response path

Quality alerts are only useful when teams know who investigates, how business impact is assessed, what gets paused, and how corrected data is republished.