The Role of Data Governance, Integration Architecture, and AI-Enabled Analytics in Enhancing Decision-Making Effectiveness: Evidence from the Telecom Sector in Saudi Arabia

Abstract

Purpose—This study investigated the influence of data governance and quality management, integration architecture design, and AI-enabled analytics on decision-making effectiveness in the Saudi Arabian telecom sector.

Study Design/methodology/approach—Primary data were collected from a sample of 370 data architects, engineers, and analytics professionals employed at Saudi Telecom Company (STC) and its partner organizations. A structured questionnaire was used to capture perceptions of governance practices, data integration capabilities, and AI-driven analytics. The data were analyzed using Structural Equation Modeling (SEM) to validate the proposed relationships, with constructs informed by literature on data governance, enterprise architecture, and analytics adoption.

Findings—The results demonstrated that data governance and quality management significantly improved decision-making effectiveness by ensuring data reliability, regulatory compliance, and trust in business intelligence systems. Integration architecture design was also found to have a positive effect, enabling organizations to unify structured, semi-structured, and unstructured data into coherent platforms that support real-time insights. In addition, AI-enabled analytics played a critical role in driving decision-making effectiveness by providing predictive intelligence, uncovering hidden patterns, and enhancing the speed and accuracy of strategic choices. Collectively, the three independent variables explained a substantial proportion of variance in decision-making effectiveness, confirming the importance of governance, architecture, and advanced analytics in the telecom industry.

Research Practical Implications— This study provides empirical evidence from Saudi Arabia, demonstrating that telecom organizations can strengthen their decision-making capabilities by integrating robust governance frameworks, scalable data architectures, and AI-enabled analytics.

Originality/value—The findings contribute to both academic research and industry practice, showing how enterprise data management and modern analytics platforms create a foundation for more effective and agile decisions.