This introductory chapter establishes the conceptual foundation for treating data as infrastructure in the AI era. It argues that the reliability and sustainability of intelligent systems depend not only on algorithmic performance but on the stability, observability, and governance of the underlying data environment. Drawing from established infrastructure disciplines, the chapter introduces the concept of the Data Grid as a software-defined infrastructure designed to support continuous data generation, validation, transformation, and consumption. Four engineering principles—structural determinism, lifecycle discipline, system observability, and explicit AI boundary design—are presented as foundational properties. The chapter outlines the methodological structure of the book and frames data infrastructure as a cross-disciplinary engineering domain rather than a collection of tools or architectural patterns.