Essential Aspects For Managing A Data Governance Projects: A Conceptual Model Proposal

In the digital era, data governance (DG) is essential for ensuring data control, quality, and security, which are critical for organizational decision-making. However, implementing DG effectively requires aligning it with strategic objectives and integrating project management (PM) practices to address unique challenges such as data complexity, regulatory compliance, and interdepartmental coordination. Despite its importance, the literature lacks comprehensive models that integrate DG with PM methodologies, leaving a gap in understanding how PM practices can be adapted for DG initiatives. This study aims to identify and organize the essential aspects for implementing DG programs and evaluate their impact on project constraints, such as scope, time, cost, risks, resources, and quality, under traditional and agile approaches. A scoping review was conducted, following three stages: planning, execution, and reporting. Searches in the Scopus, Web of Science, and El Compendex databases yielded 244 publications. After filtering, 66 articles were analyzed in-depth, leading to the development of a conceptual model. The findings categorize essential DG aspects into Environmental, Programmatic, and Structural components, establishing their relationships and mapping their influence on PM constraints. The proposed model aligns DG with organizational strategies and integrates it with broader data management practices, providing insights for tailoring PM methodologies to DG’s specific needs. This research contributes a practical framework for DG managers and project leaders, offering structured guidance to enhance DG implementation, improve decision-making, and promote sustainable practices that address the complexities of modern data environments.