Big data helps HR get a more complete profile of job candidates.
Human resources departments finally are beginning to apply lessons from “Moneyball,” the 2003 book by Michael Lewis (later a Brad Pitt-starring movie) that chronicled the Oakland A’s use of big data to compete against richer baseball teams.
Convinced that slugging percentages and on-base percentages were more important than batting averages and stolen bases, the Oakland Athletics focused on recruiting under-the-radar college players with good statistics in those under-appreciated areas for its 2002 season.
Joe Brooks, CEO of recruiting analytics firm Zapoint, says many companies lack visibility into the skills of their workforces.
Despite having the third lowest salary budget in major league baseball ($41 million), the A’s went all the way to the playoffs, competing successfully against teams with more than twice its budget. It also produced a 20-game winning streak — the second longest in major league baseball history.
“Recruiting is an inefficient industry,” says Joe Brooks, CEO of the recruiting analytics firm Zapoint. But, unlike baseball, “many companies lack visibility into the skills of their workforces.” Instead, data is spread across multiple silos and pulling it together into a coherent profile is difficult and time-consuming without an overarching analytics application.
Analytics helps HR
The application of big data analytics alleviates the inefficiency, enabling HR to assemble profiles based on resumes, professional profiles, performance reviews, publications, speaking engagements, interests and opinions relevant to the job or industry.
The result is an up-to-date, more comprehensive portrait of a candidate that includes skills and capabilities that may not appear on resumes. With this, HR can broaden its focus — like the 2002 Oakland A’s — and build a more effective team.
High-tech recruiter Entelo, for example, combs the Internet regularly to identify updates to personal websites and professional profiles like those on LinkedIn or more specialized sites (like Inbound.org for marketers) for the 23 million people in its database.
“Individuals’ skills are evolving, and people apply for several different types of jobs. Often they have different resumes for each type of job. Finding the relevance is what’s important,” Brooks says.
Data analytics helps put individuals’ capabilities in the proper context. For example, a resume may show a candidate has six years’ sales experience, but if it was 10 years ago, it may not be relevant to his or her current skills and aspirations. Big data analytics also help provide checks and balances by showing how skills have been applied and finding recommendations that may not otherwise be obvious.
‘Personalized approach’
“Recruiting used to be based on gut feeling,” acknowledges Kyle Paice, senior director of marketing for Entelo. “Today, if we’re searching for a senior IT developer, we can do a Google search and see what candidates have written, the type of code they develop, and opinions and indicators of personal interest.” This helps recruiters craft more personalized approaches for candidates.
Entelo also tracks organizational changes, like acquisitions or downsizing notices. Combining this information moves recruiting beyond headhunters and resumes to help hiring companies target job candidates who are ready to move. “If you’re flagged by Entelo, you’re 30 percent more likely to change jobs in the next 90 days,” Paice says.
“You can look at individuals, but in reality it’s a team you’re trying to establish,” Zapoint’s Brooks emphasizes. “First understand what you have.”
He recommends identifying key performers and comparing them to the competencies of others in that role. Perhaps others need the same skills, but it’s also possible the team needs to be augmented with complementary capabilities to increase effectiveness and efficiency.
Challenging assumptions
For the Oakland A’s, this meant challenging the assumption that recruits with the most home runs should be on the team. Instead, it analyzed what made a successful team and recruited accordingly.
“Organizations list the talent shortage as a top challenge. But, those using data to inform their recruiting efforts say they experience fewer challenges,” notes Elissa Tucker, human capital management research program manager for business benchmarking advocate APQC. Therefore, “there’s a lot of experimentation in big data analytics for hiring among large companies.”
For example, APQC conducted a study for a leading cereal company several years ago to correlate employees’ performance and attrition rates to their universities. Based on the results, it now it targets its recruiting to certain schools.
The point of analytics is to help organizations make more objective, evidence-based hiring decisions that increase effectiveness and efficiency. But, as Brooks points out, “Analytics don’t replace the human component. Instead, it makes a pile of resumes easier to wade through.”
via Companies use ‘Moneyball’ approach in hiring.
Companies use ‘Moneyball’ approach in hiring
