Building a Data Lake on GCP: From Data Migration to AI-Driven Insights

Large healthcare supply chain organizations generate enormous volumes of structured and semi-structured data from enterprise resource planning systems, inventory management platforms, warehouse management systems, transportation networks, procurement systems, pharmacy distribution platforms, and customer order processing applications. Legacy on-premise data warehouses often struggle to manage these rapidly growing datasets due to scalability limitations, fragmented architectures, delayed reporting cycles, and high infrastructure maintenance costs. These limitations restrict organizations from leveraging real-time analytics and artificial intelligence for operational decision-making. This paper presents a scalable cloud-native framework for building a modern data lake on Google Cloud Platform (GCP) that enables seamless migration from legacy systems while creating a foundation for AI-driven operational insights. The proposed architecture utilizes Google Cloud Storage (GCS) for raw data ingestion, Dataflow for batch and streaming data pipelines, Dataproc for large-scale distributed transformations, BigQuery for enterprise analytics, and Cloud Composer for workflow orchestration. A metadata-driven governance framework ensures data quality, lineage tracking, role-based access control, and regulatory compliance for sensitive operational datasets. The migration framework outlines strategies for legacy system extraction, schema transformation, automated pipeline deployment, validation controls, and storage optimization. After migration, the platform enables advanced use cases such as demand forecasting, inventory optimization, transportation analytics, procurement efficiency, and AI-driven supply chain decision support.