Bayer Crop Science EMEA Product Supply is expanding self-service business intelligence, data democratization, and domain-oriented data products. These ambitions require a governed data foundation that supports shared meaning, appropriate access, traceability, accountability, and reuse. The thesis examined how the current governance environment can be strengthened and how governed data can support readiness for high-impact AI opportunities.

An applied qualitative case study combined a targeted literature review and selected practices from COBIT 2019 and DAMA-DMBOK with internal documents, stakeholder interviews, a written stakeholder response, and external expert interviews. The evidence was analyzed thematically and used to prioritize governance needs, derive requirements, and develop a context-specific framework and phased implementation approach.

Existing governance mechanisms, platforms, and expertise provide a strong starting point. They can create greater value when connected through clear accountability, decision rights, repeatable processes, authoritative information, and monitoring. The proposed framework combines a lean federated operating model with clear roles, an end-to-end governance lifecycle, and a governed data foundation for definitions, metadata, quality, lineage, access, issue management, and responsible contacts. It also establishes minimum standards for reusable data products and proportionate pathways for self-service analytics. A phased roadmap begins with management sponsorship and a focused pilot before expanding proven practices. For AI initiatives, the framework supports use-case-specific assessments of whether a defined dataset is data-ready, conditionally data-ready, or not data-ready. These statuses guide further action but do not replace model validation, legal, privacy, security, or AI governance. This gives EMEA Product Supply a practical basis for scaling trusted analytics and evaluating data readiness for AI opportunities.