Scalable Cloud Data Architecture for Enhanced Financial Decision-Making: Addressing Legacy Platform Limitations

Abstract

Financial institutions handle massive data volumes daily while navigating strict regulatory demands and competitive pressures for timely decision-making. Legacy data platform architectures frequently display fragmented processing pipelines alongside significant governance deficiencies. Tightly coupled infrastructure configurations reduce organizational agility and limit responsiveness to changing business needs. Poor data quality leads to substantial revenue losses across enterprises operating in financial sectors. Compliance failures result in heavy penalties and extensive remediation costs for affected organizations. The current article proposes the Modular Governance-Integrated Cloud Architecture framework, referred to as MGICA throughout the discussion. MGICA addresses systemic deficiencies through decoupled compute-storage layers combined with domain-driven data organization principles. Clear ownership boundaries emerge from proper domain identification and bounded context establishment. Automated validation mechanisms detect quality issues at ingestion points before downstream propagation occurs. Unified semantic layer implementation ensures consistent metric definitions across all consumption interfaces and reporting tools. Comprehensive observability instrumentation enables proactive platform management rather than reactive troubleshooting patterns. Implementation occurred within a multinational financial institution over an extended deployment period covering multiple phases. Empirical evaluation measured outcomes across operational, governance, and decision-support dimensions systematically. Assessment revealed substantial reduction in infrastructure costs following framework adoption across the enterprise. Data quality incidents decreased significantly after deployment completion and stabilization. Regulatory reporting cycle times improved measurably across compliance functions and audit processes. Metric reconciliation efforts reduced considerably across business units and analytical teams. Statistical validation confirmed significant improvements across all measured dimensions with strong confidence levels. Findings demonstrate measurable operational benefits from principled architectural transformation grounded in modularity concepts. Embedded governance mechanisms establish foundations for sustained platform evolution over extended timeframes. The MGICA framework offers financial technology leaders a validated path for modernizing legacy data infrastructure into strategic enterprise assets.