An Overview of Data Governance Maturity Models and Practices with a Framework Proposal

Context: In the digital age, Data Governance (DG) has emerged as a critical priority for organizations, driven by the exponential growth of data and the increasing complexity of regulatory frameworks, such as the General Data Protection Regulation (GDPR) and Brazil's Personal Data Protection Law (LGPD). These regulations underscore the necessity of robust data governance strategies to ensure compliance, security, and quality in data management. Furthermore, aligning data initiatives with organizational objectives and treating data as a strategic asset is essential to leverage its potential fully.

Objective: This study provides an overview of DG maturity models and practical approaches to assess and enhance organizational capabilities. Additionally, based on the evidence gathered, it proposes a conceptual framework composed of six key dimensions to support the evaluation and advancement of data governance maturity in organizations.

Method: A Systematic Literature Review (SLR) was conducted, focusing on DG maturity assessments, tools, and best practices across academia and industry. After applying inclusion, exclusion, and quality assessment criteria, 22 primary studies were identified. These studies were then systematically analyzed and synthesized to provide a comprehensive understanding of the maturity models, tools, and practices under investigation.

Results: The findings highlight prominent maturity models that serve as frameworks for evaluating DG. Additionally, various tools and practices—ranging from policy formalization and staff training to advanced analytics and iterative quality assessment—reveal how organizations can address challenges in security, data integration, and alignment with corporate goals. Building on these results, this work proposes a six-dimensional framework.

Conclusions: This work underscores the importance of structured maturity models and emerging best practices in guiding DG implementation. The outcomes establish a foundation for practical initiatives, enabling organizations to strengthen their DG maturity and improve decision-making through reliable, high-quality data. The proposed framework serves as a guide to help organizations assess their current capabilities, identify gaps, and implement targeted improvements in data governance.

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.