The ball of wax we call HR analytics
| Pages | 21-25 |
| DOI | https://doi.org/10.1108/SHR-09-2018-0077 |
| Published date | 11 February 2019 |
| Date | 11 February 2019 |
| Author | Julie Fernandez |
| Subject Matter | HR & organizational behaviour,Employee behaviour |
On another note
The ball of wax we call HR analytics
Julie Fernandez
Abstract
Purpose –The debate surroundingautomating analytics processes continues as technologybecomes
more prominent and advanced in the workplace. Specifically, when it comes to HR analytics, it is
important to recognizethat human judgment as it is used in recruiting today is flawed. One toolthat can
provide further analysis and measurement beyond performance indicators and predictors is machine
learning.Through automation, HR professionals maysomeday be able to compare characteristics,apply
regression analysis to identify the influence of a characteristic and make adjustments based on new
hires, retentionand promotion results.
Design/methodology/approach –With more and more companies using artificial intelligence, it is
difficult to see how it will revolutionizethe 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, soit is crucial to take a look into how it plays a role when AIis 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 discussesthe importance of understanding the implicationsof advanced
analyticson recruiting and people management.
Keywords Human resource management, Technology, Recruitment, Talent
Paper type Viewpoint
It is not far-fetched to imagine a company using recruiting technology to help it find
potential candidates for specific jobs. And it is not science fiction to imagine that the
technology is based on machine learning, which involves identifying and repeating
certain patterns and applying algorithms. Does this mean the software could, therefore,
“learn” that most candidates hired come from a specific school or town? Does this
encourage bias –or even profiling?
The potential for bias is certainly there, but does it have greater influence than the biases
inherent in people-driven decisions?
First, it is important to recognize that human judgment as it is used today in recruiting is
flawed. Even the most careful recruiter is drawn to certain nameless qualities. And, even if
you ignore personal experience and bias in human recruiters, the battery of tests and
selection methods available to HR teams today cannot be considered “fact-based”
indicators of performance. Because so much of what enterprises mean when they talk
about performance is unexplainable, they use two terms to measure how someone might
contribute to the workplace:
Julie Fernandez is Partner
at Information Services
Group (ISG), Stamford,
Connecticut, USA.
DOI 10.1108/SHR-09-2018-0077 VOL. 18 NO. 1 2019, pp. 21-25, ©Emerald Publishing Limited, ISSN 1475-4398 jSTRATEGIC HR REVIEW jPAGE 21
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