Supplementary Material for DG2M: A Governance-Oriented Maturity Framework for Data Management in Public and Private Organizations

Supplementary material for the paper entitled DG2M: A Governance-Oriented Maturity Framework for Data Management in Public and Private Organizations

Website for the framework featuring an interactive view

• DG2M before validation: https://datagovernancematurity.github.io/dg2m-v1/
• Validated DG2M: https://datagovernancematurity.github.io/dg2m/

Paper Abstract 

Context: The increasing reliance on data-driven decision-making in the public sector, combined with stringent data protection and governance regulations such as the General Data Protection Regulation (GDPR) and Brazil’s General Data Protection Law (LGPD), has intensified the need for robust Data Governance (DG) practices. In government contexts, DG plays a central role in shaping information flows, ensuring accountability, enabling transparency, and supporting effective public policy implementation. However, public organizations often lack practical and empirically grounded instruments to assess and systematically improve their DG capabilities.

Objective: This study proposes and empirically validates DG2M, a Data Governance Maturity Model designed to support public and private organizations in diagnosing their current DG maturity and guiding strategic improvement initiatives, with particular relevance for government and regulatory environments.

Method: The study follows a multi-stage empirical approach. First, a Systematic Literature Review (SLR) was conducted to identify recurrent DG maturity models, dimensions, and practices across academic and practitioner literature. Based on this synthesis, the DG2M framework was developed and subsequently validated through a survey with 46 data professionals from public-sector institutions, private organizations, and academia.

Results: DG2M is structured into six maturity levels (from Level~0 to Level~5) and five core evaluation dimensions: Data Strategy and Governance, Culture, People, and Organizational Structure, Formalization and Data Management Processes, Data Quality, and Data Infrastructure. In addition, DG2M includes an optional Open Data dimension to support organizations, particularly in the public sector that aim to advance transparency, reuse, and public value creation through open data practices. Survey results confirm the relevance, clarity, and applicability of the maturity levels and dimensions, highlighting the model's usefulness as a diagnostic and planning tool for improving DG practices in organizational and governmental settings.

Conclusions: By providing an empirically validated maturity model, this study contributes to research and practice at the intersection of information policy, information technology, and government. DG2M supports public-sector organizations in strengthening data governance as a strategic capability, enhancing compliance, transparency, and data-informed decision-making, while also offering a flexible framework applicable to diverse institutional contexts.