Unraveling the Divide: How Data Governance and Data Management Shape Enterprise Success

Abstract

Data governance has become a pivotal element in assessing the efficacy and enduring stability of IT systems in the contemporary data-centric landscape. It includes frameworks that assist organizations in overseeing data collection, storage, processing, utilization, and sharing, while ensuring alignment with business objectives, compliance with regulations, and adherence to ethical standards. The intricacies of data governance, especially regarding emerging technological frameworks, present considerable challenges. This systematic review seeks to examine the function of data governance in improving data quality, system stability, and regulatory compliance. It aims to identify prevalent challenges, successful methodologies, and strategic consequences for the implementation of data governance frameworks across various industries, with particular emphasis on small and medium enterprises (SMEs). We performed a systematic review of 68 qualifying studies published from 2014 to 2024, employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The studies included were evaluated for bias risk utilizing the Cochrane Risk of Bias Assessment Tool. The review encompassed various study types, comprising qualitative (60%), quantitative (19%), and mixed-methods (21%) research, to furnish a thorough understanding of the influence of data governance on IT performance. The results indicate that data governance profoundly affects system success by enhancing data quality, operational efficiency, and regulatory compliance. While qualitative studies primarily focused on in-depth analysis, there was a moderate presence of empirical validation via quantitative studies. Identified challenges encompass data migration difficulties, resistance to change, and budget limitations, especially during system upgrades. Effective practices entail aligning data governance with business strategies, utilizing hybrid models, and consistently revising governance policies to accommodate technological advancements such as artificial intelligence and big data. This review offers pragmatic suggestions for IT managers and policymakers to improve data governance frameworks. These encompass incremental implementation strategies, the formation of data governance committees, and the application of performance metrics to evaluate data quality and system robustness. Despite considerable advancements, additional research is necessary to tackle deficiencies concerning emerging technologies and the distinct challenges encountered by SMEs in developing regions.