Architectural Shifts in Modern Data Ecosystems: Evaluating the Symbiosis of Cloud Computing, Agile Data Modeling, and Business Intelligence for Competitive Advantage

• Dr. Elena R. Vancroft

  Department of Information Systems, Institute of Advanced Technology Research

  Author

• Dr. Marcus A. Thorne

  School of Business Analytics, Metropolitan University of Science

  Author

Purpose: As data volumes expand exponentially, traditional data warehousing methodologies often struggle to meet the agility and scalability demands of modern enterprises. This study investigates the intersection of Cloud Computing, advanced data modeling techniques (specifically Data Vault), and Business Intelligence (BI) to understand how organizations can secure a sustainable competitive advantage.

Design/methodology/approach: The research employs a comprehensive architectural analysis and literature synthesis, examining the transition from legacy on-premise systems to cloud-native ecosystems. It evaluates the efficacy of Inmon, Kimball, and Data Vault methodologies when applied within modern Cloud ETL frameworks.

Findings: The analysis reveals that while traditional dimensional modeling remains relevant for the presentation layer, the Data Vault methodology offers superior adaptability for cloud-based data warehouses due to its decoupling of business keys and relationships. Furthermore, the adoption of cloud services is not merely an IT upgrade but a critical innovation driver that democratizes access to high-end BI and AI capabilities for SMEs.

Originality/value: This paper bridges the gap between technical data engineering concepts—such as hash-based integration and ELT pipelines—and strategic business outcomes, providing a roadmap for organizations seeking to leverage Big Data for innovation and market agility.

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