The Best Way to Make HR Decisions

Finding and keeping employees is a challenge for every small business owner. But there's a way to make it much easier.

Written by Murad Hemmadi

Ian Yates of Fitzii. Photo: Christian Katsarov Luna/CB

Ian Yates likes to ask prospective clients one question: “Have you ever made a bad hire?” More often than not, the answer is an unequivocal yes.

Yates is the managing director of Fitzii, a Toronto-based technology firm that helps companies screen and evaluate job seekers by bringing big data analytics to the HR department. These tools, known as human capital management (HCM) services, promise to make the employment cycle more efficient and cost-effective by doing everything from identifying the right candidates to detecting which employees are most likely to leave the company.

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While large firms are already taking a data-driven approach, small- and medium-size businesses have to overcome their fears of algorithms to reap the benefits. And there are some big ones: HCM tools pay $5.94 for every dollar spent, according to a recent report from Nucleus Research.

Fitzii helps clients by assigning each job applicant a score based on how he or she measures up to the employer’s criteria and to similar positions in the company’s data set. A job seeker applying for a technical IT position would get points for strong server hardware knowledge, for example, and lose them for poor network troubleshooting skills.

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SkinHealth Canada, a small cosmetic medicine firm, hired five of its staff of 10 using the service. Fitzii identified gaps in the new hires’ knowledge and designed a training regimen that favoured their intuitive learning style. “I know it’s working because we’re going to achieve double-digit growth this year,” says David Potter, SkinHealth’s managing director.

Once employees are in place, services like IBM Kenexa Predictive Retention help companies keep them. Kenexa uses a vast database of exit surveys collected from clients to find demographic characteristics associated with those who jump ship, such as education level (overqualified employees are more likely to leave) or length of service in the company. The tool “highlights hot spots of people leaving the company,” says Jonathan Ferrar, a vice-president at IBM. A manager can then intervene to ensure those at-risk employees don’t defect. The latest product from Workday, another player in this space, even suggests career paths that will keep these employees in the organization.

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Not every firm has a big enough workforce to generate meaningful data, though. Digital HCM tools avoid this problem by aggregating data across multiple companies and industries.

But the bigger challenge may be in getting managers to trust the information in the first place. “One of the objections we get is: €˜I’ve been doing this for years. I can look someone in the eye and make a good hire,'” says Yates. What these people miss is that data analytics are meant to inform HR decisions, not replace HR professionals. “You still need a human in that decision process,” says Yates.

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Rebecca Wettemann, vice-president of research at Nucleus, believes HR professionals will have to adapt to these tools. “What we see is the rise of the HR technologist,” she predicts. “This is someone within HR that isn’t part of the IT department but has lived enough time with social networks and has some technical acumen to try out these new tools and pilot new technologies.”

The tech is advancing, too. The next step is analyzing workers’ social media activity to provide a more accurate early-warning system for employee departures and dissatisfaction.

Another effect of data-driven hiring will be increased office diversity, says Yates. “Fitzii was founded out of a desire to help integrate underserved populations into the workforce,” he says. “One of the biggest barriers to getting employed for a recent immigrant is the hiring process and the reliance on the resumé.” For example, a 2011 University of Toronto study showed that applicants with anglophone names were more likely to pass the resumé-screening process, regardless of experience or education. By focusing on abilities and personality traits, Yates suggests, hiring managers will be more likely to evaluate candidates who could otherwise be eliminated.

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But first, companies have to accept big data into their HR departments. “For every company that’s asking about this,” says Ferrar, “there’s nine that aren’t.”

This article is from the January 2015 issue of Canadian Business. Subscribe now!


Would you consider using Big Data analytics in your HR decisions? What questions about your employees would you like to be able to answer? Let us know using the comments section below.

Originally appeared on PROFITguide.com