Architecting Multi-Layered Semantic Models in Power BI for Enterprise-Scale Insights

Enterprises increasingly rely on data-driven decision-making, yet traditional reporting approaches often struggle to deliver consistent, scalable, and trustworthy insights across complex organizations. Power BI has emerged as a leading business intelligence platform, but unlocking its full enterprise potential requires careful architectural design of semantic models that can serve diverse stakeholders while maintaining governance and performance. This paper explores the design of multi-layered semantic models in Power BI as a strategic approach for enterprise-scale analytics. By structuring data models into layered abstractions ranging from foundational data models to curated business models and specialized analytical layers organizations can achieve both flexibility and standardization. Key architectural principles, including modularity, reusability, role-based security, and centralized governance, are examined to ensure scalability and consistency across reporting ecosystems. Furthermore, the study highlights how multi-layered semantic models improve collaboration between technical teams and business users, reduce redundancy, and enable advanced self-service analytics without sacrificing compliance or data integrity. The proposed framework contributes a practical blueprint for enterprises seeking to transform fragmented reporting environments into cohesive, governed, and insight-driven ecosystems powered by Power BI.