مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Information Journal Paper

Title

Multi-Criteria Analysis of the Synergy Between Data Governance and Data-Driven Governance: A Comparative Framework for Designing Intelligent Organizations

Pages

  103-141

Abstract

 In the era of digital transformation, organizations aiming for optimal performance, informational agility, and sustainable competitive advantage require integrated approaches to data management—approaches that ensure data quality, security, and compliance, while also enabling evidence-based analytical decision-making. In this context, the two approaches of data governance and data-driven governance emerge as foundational pillars of organizational data management and the backbone of intelligent information architecture. This study conducts a comparative analysis of these two approaches, examining their conceptual, structural, cultural, and strategic dimensions. It seeks to offer a theoretical and practical response to the organizational need for balancing control and value creation from data through an integrative modeling framework. The research follows an analytical-applied methodology using a mixed-method approach. Initially, key comparative criteria were identified through document analysis. Then, a three-dimensional scoring model (conceptual role, dependency intensity, and practical application) was developed. Comparative data were collected and analyzed using a structured questionnaire distributed among 100 academic and industry experts. The findings indicate that the data governance approach performs better in metrics such as structure, quality, security, and data control, whereas the data-driven governance approach excels in areas like decision analytics, data-centric mindset, experimentation culture, and advanced analytical tool adoption. Furthermore, the average overall score of data-driven governance (8.9 out of 10) was higher than that of data governance (8.0 out of 10), and the scoring pattern highlights the complementary nature of the two approaches across different organizational layers. Finally, a four-layer conceptual model—comprising infrastructure, operations, decision-making, and strategy—is proposed. This model integrates both top-down (policy-driven) and bottom-up (gradual transformation) implementation paths, facilitating the design of data-driven and intelligent organizations. The application of this model requires contextual adaptation based on each organization’s cultural and industrial environment. Future studies are recommended to empirically validate the model in real-world organizational settings.

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