Please use this identifier to cite or link to this item: http://hdl.handle.net/10739/5096
Title: The framework of talent analytics using big data
Authors: Saputra, Arnold
Wang, Gunawan
Zhang, Justin
Behl, Abhishek
Keywords: Big data
Talent analytics
Talent
Human resource
Issue Date: 21-Sep-2021
Publisher: The TQM Journal, Emerald, UK
Citation: Saputra, A., Wang, G., Zhang, J.Z. and Behl, A. (2021). The framework of talent analytics using big data. The TQM Journal, (In Press), DOI: https://doi.org/10.1108/TQM-03-2021-0089
Abstract: The era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework. The methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems. This research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company’s talents that are not yet realized. Big data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management. This research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders’ business challenges.
URI: https://doi.org/10.1108/TQM-03-2021-0089
http://hdl.handle.net/10739/5096
Appears in Collections:JGU Research Publications

Files in This Item:
File Description SizeFormat 
TQM Big data paper.pdfMain Article2.04 MBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.