DG2M: A Multi-Dimensional Framework for Data Governance Maturity Assessment
Year: 2025
Type: supplementary-materials
Abstract: Context: In response to the exponential growth of data and increasingly stringent 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 asset for organizations. These regulations underscore the importance of structured strategies to ensure compliance, data quality, and security. Objective: This study aims to propose and validate a conceptual framework to support the evaluation and advancement of DG maturity, grounded in current scientific and practical evidence. Method: We conducted a literature review to identify DG maturity models, tools, and recurring practices adopted in both academia and industry. Insights from this review guided the development of a novel six-dimensional framework—Data Governance Maturity Model (DG2M). To refine and validate the model, we surveyed 46 data specialists from diverse professional and organizational backgrounds. Their feedback was used to iteratively improve the framework. Results: The literature review revealed commonly used maturity models and highlighted key practices such as policy formalization, staff training, and iterative quality assessment—critical for addressing challenges related to data integration and strategic alignment. Building on this synthesis and expert validation, the refined DG2M framework offers a practical and comprehensive tool for assessing DG maturity and guiding improvement efforts. Conclusions: This work reinforces the relevance of DG maturity models and recurring practices in guiding governance initiatives. The proposed and validated DG2M framework reflects both theoretical insights and practical needs, supporting organizations in identifying gaps, aligning with regulatory requirements, and enhancing their data governance capabilities. Keywords: Data Governance, Data Maturity, Survey, Data Governance Framework, Data Quality, Systematic Literature Review Website for the framework featuring an interactive view: https://datagovernancematurity.github.io/framework/
Author: Anonymous, Anonymous
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893297486
Cites:
Cited by:
Related to: