Conceptual Model of Data Governance in Data-Driven Organizations Utilizing Artificial Intelligence

This study designs and evaluates an innovative conceptual model for data governance in data-driven organizations by integrating international standards with artificial intelligence (AI) technologies. It aims to address the complex challenges organizations face in managing large-scale, dispersed datasets while simultaneously enhancing the quality, security, and efficiency of data-driven decision-making processes. The proposed advanced conceptual model combines three key international standards with AI technologies:ISO/IEC 38505 – Data Governance at the Board Level; COBIT 2019 – Framework for IT Control Objectives and ISO/IEC 42001 – Management Systems for Artificial Intelligence.
The research follows a mixed-methods approach (qualitative–quantitative) conducted in three main stages. In the qualitative phase, a systematic content analysis was performed on 40 high-quality scientific articles published between 2010 and 2024. In the second phase, the initial conceptual model was developed and validated through feedback from 15 experts in data governance and AI. Finally, in the quantitative phase, the finalized model was tested using structured questionnaires and a case study in five large Iranian organizations. Data collection tools included 5-point Likert scale questionnaires (Cronbach’s alpha = 0.89) and semi-structured interviews. Data analysis employed advanced statistical techniques, including Structural Equation Modeling (SEM) using SmartPLS and machine learning analyses in Python.Key findings indicate that integrating AI into data governance can lead to substantial improvements in critical indicators. These include a 47% improvement in the Data Quality Index (DQI), a 50% reduction in time needed for regulatory compliance (e.g., GDPR), and a 38% increase in data security levels. Statistical analyses confirmed a significant relationship (p < 0.01) between AI adoption and improved data governance. In SEM, the path coefficients for AI’s impact were 0.68 on data quality, 0.72 on data security, and 0.59 on data transparency—all significant at the 99% confidence level.A key innovation of this research is the introduction of the DQAI (AI-Based Data Quality Index), enabling more precise assessment of improvements derived from intelligent technologies. The proposed model is tailored to the Iranian organizational context, addressing its specific challenges. Case study results revealed that implementing the model could reduce operational costs by up to 37.5% and lower human error rates by as much as 65%.Despite its contributions, the study has certain limitations, including the relatively high initial investment required for AI infrastructure and cultural resistance to changing traditional processes. Furthermore, the absence of long-term historical data in some Iranian organizations limited longitudinal trend analysis.The practical implications for managers are significant. Organizations are advised to adopt the model gradually, starting with low-risk modules such as metadata management, while investing in workforce training and fostering a data-driven culture across all organizational levels. From a policy-making perspective, the findings could inform the development of a national strategic roadmap for intelligent data governance.Compared with previous studies, this research is noteworthy for three reasons: (1) it systematically integrates AI capabilities into traditional data governance standards; (2) it provides robust empirical evidence from Iranian organizations, supporting further localization of data governance concepts; and (3) it employs advanced quantitative methods to test the model rather than relying solely on descriptive approaches.Future research could expand this work by (1) designing AI algorithms tailored to specific industries such as banking and healthcare; (2) conducting more precise cost–benefit analyses to calculate long-term return on investment (ROI) over 3–5 years; and (3) addressing potential algorithmic biases with ethics-driven solutions.In conclusion, the proposed model offers a practical and effective framework for organizations seeking to leverage their data as a sustainable competitive advantage. The findings clearly demonstrate that intelligently combining international standards with advanced AI technologies can revolutionize data governance and prepare organizations for the challenges of the digital era. This study represents an important step toward localizing data governance knowledge and developing intelligent solutions for Iranian organizations.