Data Governance Maturity Models and Practices: A Systematic Literature Review

Context: The exponential growth of data, coupled with regulatory requirements coming from the General Data Protection Regulation (GDPR) and Brazil's General Data Protection Law (LGPD), has elevated Data Governance (DG) to a strategic imperative for organizations. These regulations emphasize the critical need for structured approaches to ensure data compliance, quality, and security.
Objective: This study aims to provide a comprehensive overview of DG maturity models and practical methodologies for assessing and enhancing organizational DG capabilities. 
Method: A Systematic Literature Review (SLR) was conducted, specifically focusing on DG maturity assessments, tools, and best practices from both academic and industrial perspectives. Following the SLR, 22 primary studies were selected, analyzed, and synthesized. This synthesis highlights the main features of existing models, tools, and practices employed in organizational DG.
Results: The findings illuminate DG maturity models and a range of recurring practices. These practices, which include policy formalization, staff training, and iterative quality assessment, demonstrably support organizations in addressing challenges related to data integration, security, and strategic alignment. 
Conclusions: This work underscores the significance of DG maturity models and emerging recurring practices in structuring governance initiatives. The information gathered provides a comprehensive overview of this critical domain.

Keywords: Data Governance, Data Maturity, Systematic Literature Review, Data Governance Framework, Data Quality.