Developing Effective Big Data Strategies and Governance Frameworks: Principles, Tools, Challenges and Best Practices

Abstract

Big data has become an increasingly critical asset for organizations across industries. However, developing effective big data strategies and governance remains a significant challenge. This research article provides a comprehensive overview of principles, tools, challenges, and best practices for developing big data strategies and governance frameworks. A systematic literature review methodology was employed to synthesize insights from over 30 scholarly articles published in the last five years. Key findings indicate that organizations need robust governance frameworks centered around availability, usability, consistency, and security of data assets. Critical success factors include executive sponsorship, cross-functional collaboration, flexible and adaptive policies, focus on data quality and master data management, as well as continuous monitoring. Popular frameworks include DAMA-DMBOK and MIT CISR’s DGIF model. Main challenges revolve around organizational, technological, and analytical issues. Best practices highlight the need for governance to be embedded across data lifecycle, not treated as an afterthought. Strategic alignment, change management and developing data-driven culture also emerge as vital enablers. The insights from this study provide organizations with guiding principles and pragmatic recommendations for developing governance-centric big data strategies.