Enterprise Scale Predictive Analytics Framework for Data-Driven Decision Support

Abstract

Predictive analytics has become a central component of data-driven decision making across modern enterprises. Organizations increasingly rely on predictive models to anticipate demand, detect risk, optimize operations, and improve customer engagement. Despite significant advances in machine learning algorithms, many enterprise predictive analytics initiatives fail to deliver sustained value at scale. Empirical evidence suggests that these failures are driven less by model accuracy limitations and more by fragmented data architectures, inconsistent feature definitions, and weak governance across the analytics lifecycle [1], [2]. This paper addresses the gap between predictive modeling techniques and enterprise deployment realities. We propose a unified enterprise-scale predictive analytics framework that integrates canonical data modeling, semantic abstraction, governed feature engineering, and end-to-end model lifecycle management. Formal notation is introduced to define predictive analytics pipelines, error propagation, and stability under data perturbations. The framework is evaluated through an enterprise-style case study covering multiple predictive use cases. Results demonstrate improved model stability, faster deployment cycles, and enhanced decision trust compared with silo-based analytics approaches [3], [4]. The study establishes enterprise data architecture as a foundational prerequisite for scalable and reliable predictive analytics.