Project Context As part of its ERP renewal, a major European pharmaceutical laboratory faced critical issues in data quality and structure. Errors and inefficiencies caused by uncontrolled data represented a significant cost to the organization, directly impacting processes and performance. Objectives The main objective was to implement a robust data governance framework to: Secure the quality of data used in Data & AI projects Structure and document datasets to facilitate exploration and reuse Ensure regulatory compliance and reliability of business analyses Mission Duration A long-term assignment with a dedicated technical consultant to support the group’s ongoing Data maturity improvement. Implementation Agaetis deployed a progressive and pragmatic approach: Analysis and identification: Mapping article data and identifying quality gaps Challenge and structuring: Controlling data transformation flows and clarifying processes Documentation: Creation of data catalogs and traceability for datasets Annotation & labeling: Integration of labeling processes to improve the performance of future AI models Continuous support: Knowledge transfer and awareness sessions for business teams Results Achieved Improved data quality Cost optimization Sustainable structuring: Strong foundations established for future Data & AI projects Guaranteed compliance: Better control of data in line with pharmaceutical regulatory requirements Key Success Factors Expertise in Data Governance applied to the pharmaceutical sector Pragmatic approach focused on measurable results and cost efficiency Tailored support with a dedicated technical consultant Close collaboration with business teams to ensure adoption  And You? Are you wondering about: Bringing your data under control to strengthen your Data & AI projects? Improving the quality of your critical datasets? Integrating governance processes to meet regulatory requirements? 👉 Contact our experts to turn your data into a true strategic asset.