Revisiting big data governance: Insights from contemporary frameworks and emerging challenges

Abstract

The rapid expansion of big data ecosystems has intensified demand for robust data governance frameworks that ensure data quality, security, privacy, and the creation of strategic value. Although numerous big data governance frameworks have been proposed over the past decade, they differ substantially in scope, maturity, and applicability, leaving critical gaps in addressing emerging technological, organizational, and ethical challenges. This study revisits the landscape of big data governance frameworks published between 2018 and 2025 through a comprehensive review and thematic synthesis of 13 peer-reviewed studies from high-impact journals and leading conferences. Unlike previous review studies that primarily collect governance dimensions or conceptual components, this study adopts a pattern-oriented analytical perspective to synthesize contemporary frameworks and identify recurring governance across contexts. The analysis identifies four major governance patterns, including fragmented governance approaches across sectors, context-specific frameworks without generalizable foundations, the growing intersection of AI and governance, and the imperative for adaptive and dynamic governance mechanisms. These patterns extend existing knowledge by explaining not only which governance elements are present in current frameworks but also how and why governance practices evolve in response to complex data ecosystems. The findings highlight the necessity of an integrated, adaptive, and context-sensitive big data governance framework that can respond to technological evolution and the complexity of the modern data environment. In addition, this study provides a structured roadmap for future research and offers actionable insight for organizations aiming to strengthen their data governance capabilities in increasingly data-driven environments.