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The framework of talent analytics using big data

Arnold Saputra (Bina Nusantara University, Jakarta Barat, Indonesia)
Gunawan Wang (Bina Nusantara University, Jakarta Barat, Indonesia)
Justin Zuopeng Zhang (University of North Florida, Jacksonville, Florida, USA)
Abhishek Behl (OP Jindal Global University, Sonipat, India)

The TQM Journal

ISSN: 1754-2731

Article publication date: 21 September 2021

Issue publication date: 18 January 2022

1645

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Keywords

Citation

Saputra, A., Wang, G., Zhang, J.Z. and Behl, A. (2022), "The framework of talent analytics using big data", The TQM Journal, Vol. 34 No. 1, pp. 178-198. https://doi.org/10.1108/TQM-03-2021-0089

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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