Social data as a tool for an organization's human resources management specialist

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Volumes, variability, speed of generation, variety of social data require new technological ways of their reception, accumulation and processing from analysts and administrative structure of the organization. For these purposes the People Data technology is used, allowing to process the data on the human resources received from external and internal environment of the organization. The analysis of People Data directions used today in HR management has shown the narrowness of technical tools and technological solutions in human resources management. To solve this problem, it is proposed to use software products with Big Data technology based on artificial intelligence. The aim of the research was to study the application of digital technologies by HR specialists in receiving and analyzing social data. On the basis of the conducted research the architecture of the software product was developed, which allows with the help of technologies of personnel profiling by electronic traces in social networks to estimate the degree of readiness of the applicant to fulfill the range of professional duties of the position which they apply for. “HR Analytics” software product on the basis of artificial intelligence is offered for collection and analysis of social data during personnel selection. The advantages of this program are the following: speed, transparency of the process of analysis of the collected information, obtaining an extended list of business qualities and personal characteristics of the applicant, the ease of perception of the final report on the candidate for the position, the ability to export the report in Excel format for further work with the data. The practical applicability of the proposed program in human resources management is considered.

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Технология people data, people data technology, social data, human resources management, personnel selection, artificial intelligence

Короткий адрес: https://sciup.org/142224633

IDR: 142224633   |   DOI: 10.24411/2079-7958-2020-13818

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