Machine Learning Models for Enhancing SAP Business Intelligence in Distributed Cloud Environments
Authors-Arman Petrosyan
Keyword-Machine Learning, SAP Business Intelligence, Distributed Cloud, SAP Datasphere, Predictive Analytics, SAP Analytics Cloud, Federated Data, MLOps.
The paradigm of enterprise analytics is undergoing a fundamental shift from central-ized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Inte-grated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to miti-gate model drift in volatile global markets. The review also addresses critical technical and stra-tegic barriers, including data latency across distributed cloud nodes, the harmonization of struc-tured and unstructured data, and the evolving landscape of global data sovereignty. By synthe-sizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of ma-chine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.
Doi-[http://doi.org/10.5281/zenodo.19470095]