Leveraging AI-Driven Analytics: An Integration Framework for SAP DataSphere and SAP Analytics Cloud

Thirumal Raju Pambala

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 222-228

Abstract: This article examines the comprehensive integration framework between SAP Data Sphere and SAP Analytics Cloud (SAC), focusing on how this convergence enables AI-powered analytics capabilities in enterprise environments. The article investigates the technical architecture, implementation methodology, and practical applications across multiple industry sectors, providing detailed insights into data governance, real-time processing, and AI model deployment strategies. Through analysis of industry implementations in retail, financial services, and manufacturing, the article demonstrates how integrated analytics platforms enhance decision-making capabilities and operational efficiency. The article explores critical aspects including system performance metrics, AI model accuracy, and business impact assessment, while addressing implementation challenges and best practices. The article findings reveal significant opportunities for organizations to leverage advanced analytics capabilities through this integration, while highlighting important considerations for security, governance, and user adoption. This article contributes to the evolving field of enterprise analytics by establishing a comprehensive framework for understanding and implementing integrated analytics solutions, while identifying promising directions for future research in areas such as advanced AI applications, automated governance, and privacy-preserving analytics.

Keywords: Enterprise AI Analytics Integration; SAP Data Sphere Architecture; Predictive Analytics Implementation; Cloud-Based Data Governance; Real-Time Analytics Processing (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25111217
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:672

DOI: 10.32628/CSEIT25111217

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