Optimizing Organizational Productivity: Leveraging Business Analytics and Machine Learning for Predictive Employee Performance Management

EasyChair Preprint no. 12696

Abstract

In the contemporary landscape of organizational management, maximizing productivity is imperative for sustainable growth and competitive advantage. This paper proposes an integrated approach leveraging business analytics and machine learning techniques to enhance predictive employee performance management, thereby optimizing organizational efficiency. Traditional methods of performance assessment often rely on retrospective analysis, lacking the ability to anticipate future outcomes. By contrast, the fusion of business analytics and machine learning offers a proactive solution, enabling organizations to forecast employee performance based on historical data patterns and contextual factors. The integration of business analytics facilitates the extraction of actionable insights from vast datasets, enabling organizations to identify underlying trends and correlations related to employee performance. Machine learning algorithms further enhance this capability by analyzing complex data sets and generating predictive models to forecast future performance outcomes. Through this combined approach, organizations can gain a comprehensive understanding of the factors influencing employee productivity, including individual characteristics, job roles, organizational dynamics, and external variables.

Keyphrases: Business Analytics, decision making, Employee Performance Prediction, machine learning, Organizational Efficiency, resource allocation

@Booklet{EasyChair:12696,
  author = {Deep Himmatbhai Ajabani},
  title = {Optimizing Organizational Productivity: Leveraging Business Analytics and Machine Learning for Predictive Employee Performance Management},
  howpublished = {EasyChair Preprint no. 12696},

  year = {EasyChair, 2024}}