DECENTRALIZED DATA ECOSYSTEMS: A COMPREHENSIVE ANALYSIS OF STRATEGIES FOR ENHANCED RESILIENCE AND COMPLIANCE

Abstract

Data decentralization has emerged as a critical paradigm in modern data management, offering enhanced scalability, fault tolerance, and compliance with localized regulations. However, implementing effective decentralization strategies presents significant challenges in maintaining data accessibility, consistency, and security across distributed systems. This article proposes a comprehensive framework for achieving robust data decentralization, synthesizing best practices and leveraging cutting-edge technologies. Through a systematic review of existing literature and analysis of industry case studies, we identify key components of successful decentralization strategies, including clear objective definition, robust governance frameworks, advanced data integration tools, and AI-driven management systems. Our findings reveal that organizations implementing this holistic approach demonstrate improved data quality, enhanced operational efficiency, and adaptability to changing business requirements. The proposed framework provides a structured methodology for enterprises to navigate the complexities of data decentralization, offering insights into overcoming common pitfalls and optimizing performance in distributed data environments. This article contributes to the growing knowledge on data management strategies and offers practical guidance for organizations embarking on data decentralization initiatives.