Comparative Analysis of Machine Learning and AI-Powered Data Warehousing for Employee Attrition and Performance Optimization

In the age of digital revolution, organizations increasingly use artificial intelligence (AI) and machine learning (ML) to improve their data-driven decision-making, especially in human resource management. This paper makes a comparative evaluation of AI-driven data warehousing systems and ML methods for forecasting employee turnover and maximizing employee performance. The study compares top data warehousing platforms like Redshift, BigQuery, Snowflake, and Databricks and their coupling with ML models with regard to prominent workforce features.

Qualitative findings from HR managers were also examined, in order to evaluate the real-world effect of these technologies on the productivity of the workforce and employment strategies. Research shows that AI-based data warehousing integrated with competent machine learning models drastically enhances attrition prediction accuracy, performance tracking, and strategic workforce planning.

This research identifies the strategic advantages of combining AI-driven data warehousing with HR analytics, offering organizations actionable findings to choose the best AI-enabled solutions. The findings contribute to extending knowledge on efficient data strategies in lessening attrition as well as improving employee performance, aiding organizations in their pursuit of strategic human capital objectives.

Data Processing, Comparative analysis, Data Warehousing, Performance Optimization, Prediction and Attrition, Machine Learning

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