Data Quality & Integration for AI

Why It Matters

AI quality follows data quality. Poor contracts, missing validation, or weak observability lead to hallucinations and broken processes.

Foundations

• Data contracts with schemas, owners, SLAs.
• Validation rules at ingest; anomaly/drift detection.
• Observability: metrics/traces/logs tied to business KPIs.
• Versioning: datasets, prompts/policies, and models tracked.

Integration Patterns

• API-first; avoid uncontrolled file drops.
• Eventing for change propagation; idempotent retries.
• Role-based access and masking; audit trails on reads/writes.

Delivery Pattern

• Discovery (1–2 w): source inventory, quality baseline, gaps.
• Pilot (4–8 w): one AI workflow with validation and observability.
• Scale: shared data platform, contract registry, governance cadence.

Outcomes to Aim For

• Fewer data incidents; faster detection/resolution.
• Better model performance; lower rework cost.
• Trustworthy AI outputs for business users.