Systematic Review of Cross-Platform BI Implementation Using QuickSight, Tableau, and Astrato

As organizations expand their data ecosystems across multiple cloud and on-premises platforms, the need for seamless, scalable business intelligence (BI) solutions has intensified. This systematic review explores cross-platform BI implementation strategies, focusing on Amazon QuickSight, Tableau, and Astrato as leading platforms facilitating multi-environment analytics. Using the PRISMA methodology, we analyzed peer-reviewed studies, technical whitepapers, and case studies published between 2015 and 2024 to identify trends, best practices, challenges, and innovations in cross-platform BI deployment. Our findings reveal that successful cross-platform BI implementations leverage flexible connectivity architectures, modular semantic layers, and real-time data integration capabilities to unify disparate data sources. QuickSight’s serverless, embedded analytics model offers a lightweight approach ideal for cloud-native ecosystems. Tableau provides powerful visual analytics and extensive connectors that facilitate hybrid deployments across cloud and on-premises systems. Astrato, with its live-query cloud-native design, enables direct interaction with modern cloud data warehouses, reducing data movement and ensuring real-time insights. Key techniques identified include federated querying, embedded analytics in operational applications, centralized access controls, and semantic model abstraction to ensure consistency across diverse environments. Despite these advances, challenges persist, notably in maintaining performance at scale, ensuring consistent security governance, and managing integration complexity between heterogeneous platforms. Innovative solutions such as metadata-driven integration layers, cross-platform data observability tools, and low-code development environments are emerging to bridge gaps and accelerate BI deployment. The review highlights that aligning BI strategies with data mesh principles and adopting platform-agnostic governance frameworks can further enhance cross-platform BI success. This paper concludes by proposing future research directions focused on autonomous BI orchestration, AI-assisted cross-platform optimization, and the development of universal semantic modeling standards. As organizations increasingly demand unified, scalable, and agile insights, mastering cross-platform BI implementation will be critical for driving competitive advantage and operational excellence in the evolving data landscape.