The ball of wax we call HR analytics
ISSN: 1475-4398
Article publication date: 20 December 2018
Issue publication date: 28 January 2019
Abstract
Purpose
The debate surrounding automating analytics processes continues as technology becomes more prominent and advanced in the workplace. Specifically, when it comes to HR analytics, it is important to recognize that human judgment as it is used in recruiting today is flawed. One tool that can provide further analysis and measurement beyond performance indicators and predictors is machine learning. Through automation, HR professionals may someday be able to compare characteristics, apply regression analysis to identify the influence of a characteristic and make adjustments based on new hires, retention and promotion results.
Design/methodology/approach
With more and more companies using artificial intelligence, it is difficult to see how it will revolutionize the HR process. As humans already have biases, will they transfer over to these artificial intelligence machines? Human judgment is already flawed in the recruiting process, so it is crucial to take a look into how it plays a role when AI is becoming built into the process as well.
Findings
Advancements in automation and HR technology are not slowing down anytime soon. As HR departments become increasingly reliant on advanced technologies and the numbers they produce, they also will experience the need for new skillsets required to deploy and use them. The HR process is rapidly changing, and as people, we must adapt now to see how AI is going to affect it. With a growing need for a center of expertise (COE) for HR data and technology, we will need to use this to focus resources on workforce analytics to drive business insights and recommendations.
Originality/value
This paper discusses the importance of understanding the implications of advanced analytics on recruiting and people management.
Keywords
Citation
Fernandez, J. (2019), "The ball of wax we call HR analytics", Strategic HR Review, Vol. 18 No. 1, pp. 21-25. https://doi.org/10.1108/SHR-09-2018-0077
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited