Unified Data Architectures for Decision Support

Modern enterprises increasingly rely on unified data architectures to enable intelligent decision support across domains such as healthcare and retail. Despite differing operational goals, both industries share the challenge of converting fragmented, high-volume data streams into timely, actionable insights. Unified architectures—built on Kafka-based ingestion, Spark-driven computation, and cloud-native warehouses like BigQuery and Snowflake—offer a blueprint for integrating heterogeneous datasets with minimal latency and strong governance.
This paper presents a comparative evaluation of these architectures in healthcare and retail contexts, analyzing dimensions such as scalability, interoperability, privacy, and cost-to-performance balance. In healthcare, unified systems power predictive diagnostics, real-time monitoring, and regulatory compliance; in retail, they drive personalized recommendations, inventory optimization, and adaptive pricing. By mapping parallel use cases, we demonstrate how cross-domain learning can accelerate innovation and reduce redundant effort.
The study proposes an integrated decision-support model that employs modular pipelines, streaming analytics, and AI-driven insights to serve both sectors. Findings highlight that the convergence of data architectures—through shared frameworks for anomaly detection, alerting, and forecasting—can lead to faster, privacy-aware, and more adaptive decision ecosystems across industries.