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All Journal Emerging Science Journal
Duarte dos Santos, Vítor M. P.
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Environmental Science

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Data Governance Meets Generative Artificial Intelligence: Towards A Unified Organizational Framework Bernardo, Bruno M. V.; Mamede, Henrique S.; Barroso, João M. P.; Naranjo-Zolotov, Mijail; Duarte dos Santos, Vítor M. P.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-029

Abstract

As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.
Co-Authors Barroso, João M. P. Bernardo, Bruno M. V. Mamede, Henrique S. Naranjo-Zolotov, Mijail