Data integration architecture outlines the processes within a data pipeline and how they relate to each other, defining how data flows from source systems, where it gets stored, and how it's transformed into usable metrics and analytics. It helps businesses establish development standards, set overall architectural patterns, provide normalization, improve simplicity, and more. Factors affecting the structure of data integration architecture include storage, cloud-based solutions, ETL vs ELT frameworks, real-time data integration, and AI-powered systems. To create an effective data integration architecture, businesses should cater to business objectives, promote easier collaboration, capitalize on automation, ensure flexibility, and prioritize security.