An Intelligent Machine Learning Framework for Enterprise Data Management in SAP Leonardo

Abstract

Enterprise organizations generate enormous volumes of structured and unstructured data through Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), Human Resource Management (HRM), and financial systems. Managing such data efficiently while extracting actionable insights remains a significant challenge. Traditional enterprise data management approaches rely on static business rules and manual analysis, which often fail to support real-time decision-making, predictive analytics, and anomaly detection. Machine Learning (ML) provides intelligent techniques capable of automating enterprise data processing and improving business intelligence.
This paper proposes an Intelligent Machine Learning Framework for Enterprise Data Management using SAP Leonardo. The framework integrates enterprise data acquisition, preprocessing, feature engineering, machine learning algorithms, predictive analytics, and business intelligence into a unified architecture. Supervised learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression, are combined with unsupervised learning algorithms such as K-Means Clustering and Isolation Forest to perform classification, prediction, customer segmentation, and anomaly detection. The proposed framework is implemented using Python and integrated conceptually with SAP Leonardo services to support intelligent enterprise applications.
The proposed architecture improves data quality, increases prediction accuracy, supports proactive business decision-making, and reduces manual intervention. Performance evaluation demonstrates that machine learning techniques significantly outperform conventional rule-based enterprise analytics. The proposed framework can be adapted for manufacturing, healthcare, banking, retail, logistics, and government organizations seeking intelligent enterprise solutions.

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