This chapter redefines data modeling as an infrastructure discipline grounded in lifecycle responsibility rather than reporting convenience. It proposes a layered modeling framework in which foundational layers maintain structural consistency and semantic integrity, while upper layers accommodate business variability under controlled boundaries. The chapter outlines architectural mandates for immutability, canonical representation, normalization, and volatility isolation. Through case studies involving metadata-based transformation representation and graph-driven entity resolution, it demonstrates how disciplined modeling supports reliability, governance, and controlled AI integration. The chapter positions modeling as the semantic backbone of the infrastructure.