Evaluating Workforce Analytics Effectiveness: An Analytic Hierarchy Process Approach

Abstract

In the evolving landscape of Human Resource (HR) management, workforce analytics has become a pivotal tool for enhancing strategic decision-making and optimizing personnel management. This study utilizes the Analytic Hierarchy Process (AHP) to evaluate the effectiveness of workforce analytics by systematically analyzing and prioritizing key HR criteria and alternatives. The proposed AHP model identifies five critical criteria: Workforce Trend Identification (WTI), Recruitment and Talent Management (RTM), Employee Engagement and Satisfaction (EES), Performance Metrics and Outcomes (PMO), and Data Quality and Integration (DQI). These criteria are assessed against three alternatives: HR Data Collection Systems, Analytics Tools and Technologies, and Data Governance Practices. The analysis reveals that WTI holds the highest significance among the criteria, emphasizing its role in proactive HR management. Recruitment and Talent Management and Employee Engagement and Satisfaction follow, indicating their importance in driving organizational performance. Performance Metrics and Outcomes, along with Data Quality and Integration, are deemed less critical but still relevant. The study's findings suggest that Data Governance Practices emerge as the most effective alternative, followed by HR Data Collection Systems and Analytics Tools and Technologies. The AHP model demonstrates high consistency in prioritizing these elements, providing valuable insights into enhancing workforce analytics practices. This research underscores the need for robust data governance and the strategic application of analytics to improve HR outcomes and organizational performance.