How Universities Use Analytics for Hiring

by | Nov 28, 2018 | Education

Reading Time: 4 minutes

There’s no perfect solution to hiring. Sometimes someone who looks great on paper is a disappointment in person, and it’s possible the ideal candidate can’t even get his or her foot in the door because of a lackluster resumé.

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In higher education, there are many factors that go into being a great university leader or a great professor. And there are a number of challenges to finding the right candidate in an industry that is highly competitive. Business intelligence can’t solve every problem with the hiring process, but it can help an organization get closer to finding the right person by focusing on analytics it can use.

Improving use of data

Up until recently, data in university hiring has been used mainly to address challenges such as increasing retention or identifying skills gaps. This is partly because those were data points that almost everyone could identify and make sense of. More predictive analytics are now available that can help map candidates’ abilities that would indicate they can perform well in the job for which a university is hiring. The technology can also understand and process the causes of employee turnover so that organizations can identify candidates that might not stay long.

More efficient process

According to LinkedIn’s 2018 Global Recruiting Trends report, artificial intelligence is one of the fastest-growing trends in hiring. 35% of the talent professionals and hiring managers surveyed said that AI is the top trend impacting how they hire.

AI can streamline what can often be a lengthy, expensive (and repetitive) process. Instead of sifting through stacks of resumés and inviting in candidates that miss the mark, AI can analyze the data of applicants. The key skills an organization wants can be highlighted and machine learning can select applicants based on their qualifications compared to the job description.

Governing the data

If the job description is what candidates’ skills are being matched to, that means there needs to be a lot of work that goes into creating that description, or the points of emphasis for the interview process. If the university is looking for certain skills, that needs to be made clear up front.

Some analytics solutions can be used to analyze the skills of current employees who perform at a high level, and those attributes can be used to build the profile of the person the university is looking to hire. This can be especially important in higher education if, for example, a candidate is being considered for a position at a small liberal arts college versus a large public research university.

If the organization is looking for a diverse talent pool, the hiring team needs to make sure they haven’t set up parameters that would pull from a homogeneous group. In order to prevent AI bias, users can set up different algorithms that can classify two groups represented in a set of data – for example, an article on discusses research on gender and racial bias in algorithms. Researchers say an example of how employers can combat this is to evaluate female engineering applicants from the criteria that best predicts a successful female engineer rather than the criteria that determines success for a larger group.

Ideally, an analytics solution brings in the perfect candidate who has the perfect interview and goes on to a long, successful career with the organization. But those instances are few and far between, no matter what approach is taken with the hiring process. One of the most important things to remember in hiring, whether it’s using analytics or not, is that you can always start the process over. And when an organization is using data for hiring, sometimes starting over is as simple as tweaking the data based on the lessons learned the first time through.

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John Sucich
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