This service helps organizations evaluate their current state of data management capabilities, identify quick wins, and design a scalable, phased roadmap for AI-ready data ecosystems. Using a capability heatmap across governance, quality, cataloging, lineage, and privacy, clients gain a clear view of strengths, weaknesses, and next steps to mature their data management practices.

A structured assessment across five domains to baseline maturity:

• Governance: Policies, stewardship, accountability, and decision rights.
• Quality: Standards, controls, monitoring, and remediation processes.
• Catalog: Data discovery, metadata management, accessibility, and self-service.
• Lineage: End-to-end visibility of data flow, transformation tracking, and auditability.
• Privacy: Compliance with regulations, anonymization, consent management, and ethical AI data usage.

Each capability is scored on a maturity scale (e.g., Initial → Developing → Established → Advanced → Leading).

Early-stage, high-impact improvements identified during the assessment:

• Establishing a minimum viable governance model (data owners, stewards, policies).
• Deploying an initial cataloging tool for critical datasets.
• Implementing data quality dashboards for priority data domains.
• Conducting a lineage proof-of-concept for one AI training dataset.
• Strengthening privacy controls for sensitive data.

A prioritized, pragmatic plan aligned with business objectives:

• Phase 1 (0–3 months): Set foundations
  
  • Define governance framework, assign data roles.
  • Stand up catalog pilot and basic quality monitoring.
  • Establish compliance checks for sensitive data.

• Phase 2 (3–9 months): Scale capabilities
  
  • Expand catalog coverage and lineage visibility.
  • Implement AI-data quality rules and monitoring.
  • Strengthen privacy automation (masking, consent).
• Phase 3 (9–18 months): Optimize for AI
  
  • Advanced lineage for model explainability.
  • AI-focused data governance (bias detection, fairness monitoring).
  • Predictive data quality using ML-driven anomaly detection.

• Capability maturity heatmap (visual, by domain).
• Findings & insights report highlighting key gaps.
• Quick win recommendation pack.
• Phased roadmap (foundation, scale, optimization).
• Executive summary presentation for leadership buy-in.

• Clear visibility of current AI data management maturity.
• Actionable roadmap to accelerate AI readiness.
• Quick wins to demonstrate value and momentum.
• Reduced risk of regulatory or ethical AI failures.
• Scalable approach that evolves with organizational priorities.