Improving the Effectiveness of Human Resource Management via Data Mining Tools for Digital Transformation

Abstract

Purpose of the article: The article focuses on the issue of human resources management using automated segmentation based on scoring method. This approach employs expert estimates to create an algorithm for automation of solving these tasks. Subsequently, segmentation is performed to obtain a group of potentially successful candidates. For this, a suitable prediction model is used.

Methodology/methods The above mentioned procedure uses data mining techniques in the area of ​​segmentation and prediction of candidates of the personnel agency to enter into specific positions. Besides the time savings and cost minimization, the efficiency of corporate processes in the processing of human resource demand and the promotion of personal approach to the candidates are also improved.

Scientific aim: The article focuses on the issue of human resources management using automated segmentation based on scoring method. This approach employs expert estimates to create an algorithm for automation of solving these tasks. Subsequently, segmentation is performed to obtain a group of potentially successful candidates. For this, a suitable prediction model is used.

Findings: The result is a model to support decision making and potentially simplify the process of selecting a suitable candidate via data mining methods. This allows the whole process to be divided into phases that leads to the acquisition of a group of potentially successful candidates to fill specific job positions. At the same time, time is saved and the costs associated with selection of suitable candidates are minimized.

Conclusions: The article thus responds to the technological development and collection of large amounts of data in different types of databases and their transformation into relevant information to support decision-making processes in human resource management.