Data-Driven Performance Management using Machine Learning and KPI Analytics

Abstract

This study presents a data-driven performance management framework that integrates machine learning techniques with Key Performance Indicator (KPI) analytics to enhance organizational decision-making and operational efficiency. The proposed approach leverages historical and real-time performance data to identify patterns, predict outcomes, and uncover hidden drivers of employee, process, and organizational performance. Machine learning models such as regression, classification, clustering, and ensemble methods are employed to forecast KPI trends, detect performance anomalies, and support proactive managerial interventions. By aligning predictive insights with strategic objectives, the framework enables continuous monitoring, objective evaluation, and evidence-based performance optimization. The results demonstrate that combining machine learning with KPI analytics improves accuracy in performance assessment, supports timely corrective actions, and fosters a culture of continuous improvement in dynamic business environments.