Supplementary Material for DG2M: A Multi-Dimensional Framework for Data Governance Maturity Assessment

Details Supplementary material for the paper entitled DG2M: A Multi-Dimensional Framework for Data Governance Maturity Assessment. This repository contains the following folders: systematic_literature_review_data: Documents and spreadsheets related to a systematic literature review on data governance maturity models and recurring practices. survey_validation: Files related to the survey conducted for DG2M validation. DG2M_development: Files related to the development of the Data Governance Maturity Model (DG2M). Website for the framework featuring an interactive view DG2M before validation: https://datagovernancematurity.github.io/dg2m-v1/ Validated DG2M: https://datagovernancematurity.github.io/dg2m/ Abstract Context: The exponential growth of data and stringent regulations like Europe's General Data Protection Regulation (GDPR) and Brazil's General Personal Data Protection Law (LGPD) underscore Data Governance (DG) as a strategic organizational asset. Effective DG ensures compliance, data quality, and security, yet implementing it remains challenging due to complexity and limited engagement. While various DG maturity models (MMs) exist, many lack conceptual clarity, adaptability, or practical applicability, hindering effective adoption. Objective: This study proposes and validates a novel conceptual framework, the Data Governance Maturity Model (DG2M), to assess and advance organizational DG maturity, grounded in current scientific and usefulness evidence. Method: We conducted a systematic literature review (SLR) to identify existing DG maturity models and recurring practices across academia and industry. Insights from this review guided the development of the DG2M, a six-dimensional framework. To empirically refine and validate the model, we surveyed 46 data professionals from diverse backgrounds, incorporating their feedback for iterative improvements. Results: The SLR highlighted common MMs and key practices like policy formalization, staff training, and iterative quality assessment, crucial for addressing data integration and strategic alignment challenges. Expert validation confirmed the DG2M's relevance and coherence. The refined DG2M framework provides a comprehensive tool for assessing DG maturity and guiding improvement initiatives. Conclusions: This work reinforces the critical role of DG maturity models and recurring practices in guiding governance efforts. The proposed and validated DG2M framework synthesizes theoretical insights with practical needs, empowering organizations to identify gaps, align with regulatory requirements, and enhance their data governance capabilities for sustained data value. Keywords Data Governance, Data Maturity, Survey, Data Governance Framework, Data Quality, Systematic Literature Review.