A Framework for Data Mining Approach Applications in Human Resource Management

Efficient and effective decision making in human resource management have considerable competitive advantage outcomes in organizations. Although there are a lot of data and information in organizations in human resource filed, unfortunately this data is not analyzed and used in an effective manner. Data mining techniques can be used as an effective approach to analyze human resources data, so that by relying on it different business decisions can be made. The purpose of this study is to analytically review and investigate studies which used data mining techniques to analyze different HRM related issues and thus to propose a strategic framework for applying data mining methods on human resource management areas. For this purpose, 89 valuable and independent studies extracted from internal and external resources and reviewed. So, at the first, different areas of HRM like recruitment and selection, training and development, employee turnover and withdrawal behaviors, and performance management which were focused and paid attention by data miners are identified. Then, with regard to the CRISP-MD methodology different stages of decision making in HRM using data mining are explained and finally, a proper framework for studies in this field is obtained which is a macro guide for HR managers to make smarter and more aligned decisions with corporate objectives using organization internal information resources. For academics also, the mentioned framework represents a coherent image of previous studies which can be verified and used in further researches.