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Abstract :

This research is applied in terms of purpose and descriptive-analytical in nature with a mixed approach (quantitative and qualitative) and is designed based on a five-stage cycle including data collection, analysis, decision-making, implementation and monitoring, and feedback and continuous improvement. Quantitative and qualitative data were collected from human resource development systems, performance evaluation, job satisfaction questionnaires, and educational data. Data analysis was performed using SPSS and R software at descriptive, predictive, qualitative, and comparative levels. The results of the analyses were used as the basis for decision-making at strategic, operational, and corrective levels and were implemented and monitored through management dashboards and intelligent systems. Overall, the results showed that the establishment of data-driven human resource development management in the form of a coherent cycle including multi-source data collection, descriptive and predictive analytics, multi-level decision-making, indicator-driven implementation, and continuous feedback can lead to improved efficiency, agility, and organizational learning. The results indicate that alignment between data quality, depth of analysis, and coordination of strategic, operational, and corrective levels is the main condition for the success of this approach. Overall, the research confirms that data-driven human resource management, if challenges such as algorithmic bias and execution gap are addressed, can provide organizations with a sustainable competitive advantage and more scientific decision-making.

References:

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