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

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/

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-28
DOI
https://doi.org/10.5281/zenodo.15764937
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Zenodo (CERN European Organization for Nuclear Research)
Data Quality and Management
article

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

article en

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/

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 44%
Data Quality and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

DG2M: A Multi-Dimensional Framework for Data Governance Maturity Assessment · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS