Designing Scalable Data Architectures for Enhanced Cross-Domain Analytics: A Framework to Improve Decision-Making Precision and Efficiency in Complex Networks

Abstract

In the era of big data, the capacity to analyze cross-domain data has become increasingly critical for organizations seeking to improve decision-making processes within complex networks. Scalability, in particular, is a pivotal factor in designing data architectures that can effectively manage large volumes of heterogeneous data across multiple domains. This paper presents a framework for designing scalable data architectures optimized for cross-domain analytics, with the goal of enhancing precision and efficiency in decision-making. We examine the foundational principles underlying scalable data architectures, including distributed data storage, parallel processing, and fault tolerance. Additionally, we address the challenges inherent in cross-domain data integration, such as schema heterogeneity, data lineage, and interoperability. Leveraging cloud computing and modern data management strategies, the proposed architecture integrates technologies like distributed data lakes, data warehouses, and event-driven microservices. By employing advanced analytics and machine learning, the framework enables the processing and analysis of real-time data streams from various domains. Through simulation studies, we demonstrate that the proposed architecture achieves improved scalability and accuracy in cross-domain data analysis while maintaining operational efficiency. Ultimately, this framework provides a strategic pathway for organizations seeking to harness complex data flows and deliver actionable insights. The resulting architecture facilitates seamless data interchange across domains, thus supporting a more agile and responsive decision-making environment that aligns with the evolving needs of complex organizational networks.