The Training Path of Big Data Talents in the Chinese Government in the field of Emergency Management

DOI

10.2991/icmete-19.2019.8How to use a DOI?

Keywords

Emergency management; Big data; Big data talents; Public crisis events

Abstract

In the field of emergency management, the number of big data talents in the Chinese government is small and the quality is poor. In order to cope with the public crisis events better and meet the actual needs of emergency management, the Chinese government needs to cultivate more excellent big data talents. By applying the documentation method and consulting the relevant research both at home and abroad, it is obtained the latest research trends in the training of big data talents in the field of emergency management. Based on the analysis of detailed data, it is found that the reasons for the shortage of the emergency management talents are due to the weakness of big data awareness of governmental personnel, the lack of specialized laws for the use of big data, and the unreasonable talent training mechanism. The training of big data talents of emergency management in Chinese government should strengthen the staff’s awareness of big data, enact laws to normalize the use of big data, and innovate the training mechanism for big data talents. It will improve the quality of big data talents, increase the number of talents, and provide intellectual support for emergency management.

Copyright

© 2019, 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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TY  - CONF
AU  - Fuyu Li
AU  - Shuyan Wei
AU  - Fucang Li
PY  - 2019/05
DA  - 2019/05
TI  - The Training Path of Big Data Talents in the Chinese Government in the field of Emergency Management
BT  - Proceedings of the 2019 International Conference on Management, Education Technology and Economics (ICMETE 2019)
PB  - Atlantis Press
SP  - 34
EP  - 37
SN  - 2352-5428
UR  - https://doi.org/10.2991/icmete-19.2019.8
DO  - 10.2991/icmete-19.2019.8
ID  - Li2019/05
ER  -