Resume Analysis and Big Data

The following is a portion of an article that originally appeared on TechTarget in December 2017, and includes comments from OperationsInc CEO David Lewis. To view the complete article, please click here.


by Patrick Thibodeau

A new generation of recruiting management systems promises to reduce human bias. These systems use machine learning and big data to discover what they claim are the best candidates. They use data, not intuition or first impressions, to recommend candidates for interviews.

This takes applicant-tracking systems well beyond resume keyword scanning. To make their assessments, these systems use historical data, including past hiring decisions, as well as data on the candidates, to determine what constitutes a successful employee. The goal is to find patterns in this data.

… David Lewis, CEO of OperationsInc, an HR consulting firm, sees potential in machine learning-enabled recruiting management systems and believes it is “theoretically correct” that these machine learning systems can eliminate bias. But he also has some concerns.

A note of caution

“The technology is there to eliminate you — not to try to figure out how you can fit,” Lewis said. “And that’s where I think this potentially goes off the rails.”

Lewis’ point is that, if recruiting management systems aren’t properly adjusted for market conditions, it might eliminate people at a time when hiring is difficult. He pointed to the difference in demand during the start of the recession in 2009 and today when unemployment is low.

Lewis also said that many hiring managers don’t have the training they need and act on intuition. That problem extends to technology use.

“Companies also don’t understand how to either select the right technology or use the available tools that the technology offers to get the technology to do what they want them to do,” Lewis said.

The vendors say these systems have to be routinely adjusted to account for the data issues and changing conditions. For instance, if a firm’s employees are mostly male, that could impact the outcomes, said Xavier Parkhouse-Parker, co-founder and director of recruiting technology provider Plato Intelligence, a London-based firm. The way around it is to carefully select data that includes all socioeconomic backgrounds and ages, he said.

These systems don’t eliminate bias in the hiring process, and a manager can reject a candidate for any reason. But with the data, “you get something that is really hard to argue against,” Parkhouse-Parker said.