Governing Data for Value: A Case Study on Implementing a Federated Data Governance Model in a Logistics Enterprise to Enable AI-driven Business Outcomes

Effective data governance (DG) is vital for analytics and AI, yet implementing it in traditional enterprises is challenging. This single-case study examines a German Mittelstand logistics firm’s multi-year DG program, covering a federated DG model, an Azure-based Data & AI platform, and literacy initiatives. We identify success factors (integrated strategy, literacy investment, clear roles including Master Data Managers and Global Data Architects) and challenges (legacy integration, cultural adaptation, master data management). DG enabled measurable AI value: route optimization saved ~€150,000 per site annually; carrier-claims automation handles ~10,000 claims/month with ~70% automation; ~150 employees gained foundational AI/Power BI skills. The journey was non-linear with setbacks. We offer actionable guidance on linking DG to AI business value and extend DG theory by modeling AI value as a reinforcing feedback loop, elevating platform governance within the domain scope, and specifying AI‑specific procedural mechanisms.