A Proposal for a Data Governance Maturity Assessment Framework

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 LGPD. These regulations underscore the necessity of robust data governance strategies to ensure compliance, security, and quality in data management. To address these challenges, established frameworks provide foundational principles for effective data governance. Maturity models, such as the Stanford Data Governance Maturity Model, have gained widespread adoption in academia and industry, serving as essential tools for evaluating organizational practices and fostering continuous improvement in data governance initiatives. Objective: This study aims to propose a comprehensive framework for assessing data governance maturity. The proposed framework aims to support organizations in refining their data governance strategies and enhancing operational efficiency across diverse sectors, including public administration and private enterprises. Method: This research is based on a Systematic Literature Review (SLR) conducted to identify key practices, existing frameworks, and data maturity models. Results: The study identifies key frameworks, including the Stanford Data Governance Maturity Model and the Master Data Management Maturity Model, as foundational references. These frameworks provided material and structure that supported the development of the proposed data maturity framework. Conclusions: This research contributes to the field of data governance by presenting a practical and adaptable framework for assessing data governance maturity. The proposed model aims to establish data governance as an organisation's strategic enabler, enhancing decision-making through reliable and high-quality data aligned with organizational objectives. 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.