A strong strategy creates clarity on priorities, investment sequencing, and measurable outcomes. Use this roadmap to align business, data, and AI decisions across leadership and delivery teams.

Phase 1: Discover and Align

• Define the top 3 to 5 business outcomes your data program must improve.
• Map the decisions and workflows that drive those outcomes.
• Inventory key data domains, critical systems, and current pain points.
• Identify regulatory and privacy requirements that shape design choices.

Phase 2: Assess and Prioritize

• Evaluate data maturity across governance, quality, architecture, and AI readiness.
• Prioritize use cases by value, feasibility, and risk.
• Quantify expected ROI and establish baseline metrics.

Phase 3: Design the Target State

• Define the target data platform architecture and operating model.
• Establish governance, stewardship, and data quality controls.
• Set AI risk management and model lifecycle standards.

Phase 4: Deliver and Scale

• Deliver 1 to 2 high-impact use cases end-to-end.
• Operationalize analytics and AI with monitoring and retraining.
• Expand to additional domains using reusable patterns and templates.

Deliverables You Should Expect

• A value-based portfolio of use cases with business KPIs
• A target-state architecture and migration roadmap
• A data governance and stewardship model
• An AI risk management and MLOps operating model

CTA

Ready to move from strategy to measurable impact?

• Request a readiness assessment
• Schedule a strategy call

Sources

• DAMA-DMBOK Overview
• NIST AI Risk Management Framework (AI RMF 1.0)
• NIST Privacy Framework
• AWS Well-Architected Framework