North*Star© Pillar
Data Governance & ML Ops
Trusted data pipelines, model lifecycle controls, and MLOps practices that keep AI safe and production ready.
What this pillar delivers
Adaptive governance and MLOps standards and architecture that ensure data is trusted, secure, and accessible, and that models are reliable, observable, and operated with discipline.
Enterprise Data Governance Architecture
- Define enterprise data governance operating model
- Establish data domains, ownership, and policies
- Standardize metadata, glossaries, and lineage
- Implement access controls and data classification
Data Stewardship & Accountability
- Stand up stewardship roles and accountability model
- Operationalize data council and decision rights
- Track issues, exceptions, and remediation
- Embed stewardship into change and release processes
Data Quality & Monitoring
- Define data quality dimensions and thresholds
- Automate quality checks across pipelines
- Monitor freshness, completeness, and drift
- Surface data quality KPIs to executive dashboards
MLOps Standards & Architecture
- Reference architecture for model lifecycle
- CI/CD for data, features, and models
- Model registry, versioning, and approval workflows
- Production monitoring for performance, drift, and bias
AI is only as trustworthy as the data it learns from.
Lineage, quality scoring, and MLOps controls embedded in every pipeline, so models stay accurate, explainable, and audit-ready in production.
Source: McKinsey & Oxford, Delivering large-scale IT projects
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