Proceedings of the 2018 International Conference on Economics, Business, Management and Corporate Social Responsibility (EBMCSR 2018)

HR Predictive Data Analytics in the Era of Big Data

Authors
Jia Yuan
Corresponding Author
Jia Yuan
Available Online November 2018.
DOI
10.2991/ebmcsr-18.2018.75How to use a DOI?
Keywords
Big data, Predictive analytics, Human resource management.
Abstract

This document explains and demonstrates the application of big data in human resources management. Predictive analytics are a trend in human resources management. In this paper, we explain the concept of HR predictive analytics, and then we introduce some organizations which apply HR predictive analytics to their employees. We also do some predictive analytics research in some companies, which is very useful for human resources management and organization development. Although big data provides new methods for human resource managers, there are still some disadvantages. HR management faces a lot of challenges.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Economics, Business, Management and Corporate Social Responsibility (EBMCSR 2018)
Series
Advances in Economics, Business and Management Research
Publication Date
November 2018
ISBN
978-94-6252-603-7
ISSN
2352-5428
DOI
10.2991/ebmcsr-18.2018.75How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jia Yuan
PY  - 2018/11
DA  - 2018/11
TI  - HR Predictive Data Analytics in the Era of Big Data
BT  - Proceedings of the 2018 International Conference on Economics, Business, Management and Corporate Social Responsibility (EBMCSR 2018)
PB  - Atlantis Press
SP  - 388
EP  - 390
SN  - 2352-5428
UR  - https://doi.org/10.2991/ebmcsr-18.2018.75
DO  - 10.2991/ebmcsr-18.2018.75
ID  - Yuan2018/11
ER  -