Discovering Actionable Knowledge through Advanced Data Analytics and Intelligent Inference

Abstract

In the era of data abundance, organizations across healthcare, retail, finance, and governance increasingly seek not just descriptive insights but actionable knowledge that can guide timely and effective decisions. While conventional analytics models provide predictions, they often fall short of contextualizing these outputs into meaningful interventions. This research proposes a unified multi-phase pipeline that integrates advanced data analytics with structured inference mechanisms to bridge the gap between prediction and action. The framework encompasses data preprocessing, model development, rule-based inference, and domain-specific action mapping. Empirical evaluation is conducted using real-world datasets spanning 2015 to 2023, demonstrating the practical utility of the system across multiple domains. Experimental results show improved accuracy in classification tasks (up to 84%) and reduced forecasting error in financial predictions. Additionally, statistical tests confirm the significance of inference-based decisions in enhancing outcomes. The proposed approach is interpretable, generalizable across sectors, and responsive to real-world constraints, making it a robust contribution to the field of actionable intelligence and decision science.