Human capital
The danger of a bad hire In a 2021 survey conducted by global human capital services provider CareerBuilder, nearly two thirds (74%) of US companies said they have hired the wrong candidate, costing them almost $15,000 per failed recruitment. Nor do the implications of hiring the wrong person just stop at the process itself. Lost productivity, missed business opportunities and the effects ill-hired candidates have on work colleagues cannot be underestimated – though they are more difficult to quantify. Such difficulties make it more imperative than ever for businesses to ensure they are sourcing the right people, for the right roles, at the right time – and that they manage that talent once they’ve secured it.
It should come as no surprise, in other words, that data science is playing an ever-more important role in recruitment. But what exactly are big data and data science – and how do the two combine in the realm of data-based recruitment? In a nutshell, explains Professor Peter Cappelli, director of Wharton’s Centre for Human Resources, big data comprises the ‘three Vs’: hugely diverse data (variety), coming in increasing volume and with growing velocity. “Big data typically refers to alternative sources of data that we haven’t collected before,” continues Cappelli, an industry veteran with more than three decades of experience in human resources, employment policy and talent management.
Data science, for its part, combines a mix of specialities including artificial intelligence (AI), data analysis, statistics and scientific methods, to obtain value from all that information. Otherwise data would be just that – data with no meaningful purpose. Data science-based recruitment, or recruitment analytics, helps recruiters evaluate their strategies and adjust them in line with their objectives, as well as identify challenges in their talent management pipeline and determine which recruitment channels are having the greatest impact. Practically speaking, it can help explain where candidates come from, provide insight about the calibre of candidate being drawn to the specific roles, and help address issues within the
Professor Peter Cappelli (pictured) says that companies don’t yet have the tools or big data to benefit from new recruitment technology.
recruitment process. If, for instance, candidates drop out, what part of the process led to it? At the same time, this data-based approach can provide insight after the candidate takes up a new position. As an example, if a new employee decides a role isn’t right for them after starting, analytics can be used to determine the catalyst for their decision, perhaps highlighting discrepancies between the advertised role and what it involves in practice. According to the CareerBuilder survey, after all, two thirds of candidates took up roles only to find the position was not right for them, leading half to leave within six months.
Predicting the frontline
In short, having access to data and knowing how to read it is becoming a critical component for businesses everywhere. That’s similarly true in helping to smooth the process of integrating candidates into a company and its culture. So-called predictive hiring is a way of determining how appropriate a candidate is before they’ve even stepped through the door – potentially saving time, money and even preventing ill-feeling.
98%
Almost all Fortune 500 companies use an applicant tracking system (ATS) to monitor recruitment.
Jobscan
$43.4 bn
The size of the online global recruitment market by 2027, up from $28.8bn in 2019. The growing popularity of social media as an avenue for recruitment is driving the market’s growth.
Fortune Business Insights Finance Director Europe /
www.ns-businesshub.com 69
Studio Romantic/
Shutterstock.com; Amanda Stevenson
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