Comparative study of database normalization techniques for optimizing financial data storage

Posted: Apr 27, 2020

• Michael Johnson
• Michael Ramirez
• Noah Johnson

Abstract

The optimization of financial data storage represents a critical challenge in modern database management systems, particularly given the exponential growth of financial transactions and the increasing complexity of regulatory requirements. Traditional database normalization techniques, while theoretically sound, often fail to address the unique characteristics of financial data, including temporal dependencies, complex hierarchical relationships, and stringent compliance mandates. This research addresses this gap by conducting a comprehensive comparative analysis of normalization techniques specifically adapted for financial data environments. Financial institutions face unprecedented challenges in data management, with transaction volumes growing at approximately 40%. Our investigation reveals that conventional normalization forms (1NF through 5NF) require significant adaptation when applied to financial data structures. Financial transactions exhibit unique properties including temporal sequencing, complex derivation relationships, and multi-dimensional categorization that challenge traditional normalization assumptions. This research introduces a novel hybrid normalization framework that integrates domain-specific optimizations with established normalization principles.