Enterprise Data Management Using Predictive Analytics and Cloud Pipelines: A Forecast-Reliability Architecture

Abstract

Enterprises increasingly route operational decisions through predictive analytics built on cloud data pipelines, forecasting demand, risk, and resource needs from data these pipelines collect, clean, and serve at scale. Three specific, well-documented problems threaten the reliability of predictions produced this way. First, the data quality literature establishes that poor input data quality degrades the output of any downstream process built on it, often silently, long before the degradation is noticed. Second, the machine-learning systems literature documents that predictive pipelines accumulate hidden technical debt entangled components, correction cascades, and undocumented “pipeline jungles” at a rate ordinary software engineering practice does not anticipate, making a pipeline progressively harder to change safely even as it keeps producing output. Third, the concept-drift literature establishes that a predictive model’s accuracy can decay over time as the statistical relationship between its inputs and the outcome it predicts shifts, without any visible failure signaling that decay to the people relying on its output. This paper proposes PRISM (Predictive Reliability for Integrated Scalable data Management), an architecture in which a data quality and governance layer, a predictive modeling layer, and a cloud pipeline orchestration layer are coordinated by a forecast reliability controller that monitors prediction quality against both data-quality degradation and concept drift, triggering governed retraining before silent decay reaches decision-makers. Following a design-science research approach, PRISM is illustrated, not empirically validated, through a comparative capability table, an illustrative case scenario, and two illustrative figures, with every quantitative value explicitly labeled as an illustrative estimate rather than measured production data. The paper situates PRISM against the literature on data quality, hidden technical debt in machine learning systems, predictive modeling techniques, concept drift, distributed data processing, and cloud computing infrastructure, and discusses the architecture’s practical implications, assumptions, and limitations.