Enterprise Data Strategy Development
Developed a comprehensive enterprise Data Strategy covering governance, architecture, maturity, operating model, tooling, and multi-year roadmap aligned with business and regulatory objectives.
Problem
The organization lacked a unified data direction. Data was siloed across departments, quality was inconsistent, and reporting relied heavily on manual processes. No structured approach existed for governance, ownership, or lifecycle management.
Approach
Conducted a full current-state maturity assessment across data governance, architecture, quality, security, and analytics. Benchmarked against global frameworks such as DAMA-DMBOK and CDO best practices. Identified key business use cases requiring data modernization (AI initiatives, analytics, OT/IIoT integration, etc.). Defined future-state data platform, governance structure, and cataloging strategy. Built a multi-year roadmap segmented into foundation, enablement, and acceleration phases.
Solution
Delivered an enterprise-wide Data Strategy combining: Data Governance Framework (roles, processes, data ownership model) Data Architecture Blueprint (lakehouse, ingestion, pipelines, storage tiers, metadata, quality) Data Platform Tooling Recommendations (Data Lake, Warehouse, Catalog, Quality tools) Roadmap & KPI Framework AI readiness model enabling RAG, automation, and predictive analytics Operational Technology (OT/IIoT) ingestion model to integrate factory data sources
Impact
Established a single strategic direction for data management. Improved data governance maturity and regulatory readiness. Reduced reporting exceptions by 40% through better structure and ownership. Enabled scalable AI roadmap and advanced analytics initiatives. Provided an implementation roadmap that guided budgeting, planning, and prioritization for 2025–2027.