Data Governance Maturity Models: A Review and Framework Proposal

Context: In response to the exponential growth of data and increasingly regulatory requirements, such as the General Data Protection Regulation (GDPR) and Brazil’s General Data Protection Law (LGPD), Data Governance (DG) has become a strategic priority for organizations. These regulations underscore the importance of structured strategies to ensure compliance, data quality, and security.

Objective: This study presents an overview of DG maturity models and practical approaches to assess and enhance organizational capabilities. Based on the evidence gathered, it proposes a conceptual framework composed of six dimensions to support the evaluation and advancement of DG maturity.

Method: We carried out a Systematic Literature Review (SLR), focusing on DG maturity assessments, tools, and best practices from academy and industry. After the conduction of the SLR, 22 primary studies were selected. These studies were analyzed and synthesized to highlight main features of existing models, tools, and practices applied in organizational data governance.

Results: The findings highlight widely used maturity models and a range of practices—such as policy formalization, staff training, and iterative quality assessment—that support organizations in addressing challenges related to data integration, security, and strategic alignment. Building on this synthesis, the study introduces a six-dimensional framework to guide organizations in assessing current capabilities and implementing targeted improvements.

Conclusions: This work reinforces the significance of DG maturity models and emerging best practices in structuring governance initiatives. The proposed framework serves as a practical tool to support organizations in identifying gaps and enhancing their data governance capabilities in alignment with regulatory and strategic demands.

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

Available Resources

The following files are available for download and reference:

• Overview of the SLR Process: A detailed description of the methodology, including research questions, inclusion/exclusion criteria, and quality assessment protocols.
• Collected Articles and Selection Criteria: A spreadsheet documenting the collected studies and their evaluation against inclusion and exclusion criteria.
• Quality Assessment Results: A spreadsheet with the quality assessment of the selected studies, based on predefined questions to ensure methodological rigor.
• Data Extraction Results: A detailed spreadsheet capturing information extracted from the selected studies, including objectives, methods, and outcomes.
• Proposed Framework Construction: a spreadsheet that supported the development of this framework.