Data & AI Foundations
Data and analytics capabilities depend on reliable data foundations. In large industrial organisations, operational data often exists across multiple systems with inconsistent structures, ownership models, and governance practices.
My work in enterprise data leadership focused on building the governance frameworks, data architecture, and operational processes required to transform fragmented data environments into scalable foundations for analytics and AI.
Case Studies
Selected examples illustrating the development of data foundations and analytics capabilities for industrial operations.
Enterprise Data Foundation
Problem
Operational and engineering data across global operations existed in disconnected systems with inconsistent structures and limited governance.
Approach
Defined and implemented a division-wide data strategy covering data governance, architecture, ownership models, and automated data pipelines. Established standards for data capture, structuring, access control, and quality across distributed teams.
Impact
Established structured data environments enabling consistent access to operational data for analytics, machine learning workflows, and digital platforms.
Applied AI in Engineering Operations
Problem
Operational teams had access to large volumes of data but lacked analytical tools capable of converting it into actionable operational insights.
Approach
Led a multidisciplinary data science team developing machine-learning workflows designed to improve reliability and operational performance. Implemented agile delivery practices and close collaboration with engineering teams to ensure models addressed real operational needs.
Impact
Delivered machine-learning workflows used by operational teams to support reliability and performance decisions.