Data silos remain a key obstacle to business intelligence (BI), constraining holistic analytics and strategic decision-making. Despite advances in cloud computing, artificial intelligence, and data integration techniques, organizations continue to face scattered data environments. In this paper, based on Data Governance Theory, we examine the causes, impacts, and mechanisms of mitigating data silos through an integrative strategic framework. By leveraging centralized data lakes, cloud data warehouse services such as Salesforce Data Cloud, and real-time data virtualization, the proposed model introduces a governance-based decision-making approach. We present comparative case studies in healthcare and retail, demonstrating real-world application and performance estimates. Furthermore, we address concerns related to scalability, implementation challenges, and cost to ensure practical adoption. Original visual models, evaluation matrices, and projections of future trends reinforce this paper's theoretical and practical contributions.