In the era of digital transformation, effective big data governance is considered pivotal for data-driven organizations seeking to leverage their data assets for strategic advantage. In this study, we conducted a comprehensive review that explores the impact, benefits, and pitfalls associated with big data governance programs within data-driven organizations. In order to obtain an effective result of our findings, the focus more on the recent studies published between (2020–2024). Consequently, this study proposes a novel Big Data Governance framework designed to enhance data governance practices across data-driven organizations. The proposed framework comprises of four fundamental components of big data governance (People, Process, Data, and Technology), has further been divided into four subcomponents that are interconnected through an iterative flow. By integrating these components, the proposed framework aims at addressing the existing challenges of data governance practices faced by organizations, such as data literacy, data silos, security threats, and compliance issues. Similarly, this paper contributes to the academic and practical discussions on big data governance practices, by providing a structured approach that data-driven organizations can adapt to enhance their data governance practices that can lead to an improved organizational outcome. However, the practical implementation of the proposed framework remains a limitation of our study, urging avenues for future studies to explore its applicability and impact on data-driven organizations.