Traditional hiring practices have long relied on intuition and subjective judgment to identify the right candidates. However, forward-thinking organizations are discovering that applying rigorous data-driven methods to talent acquisition can improve both the quality and predictability of hiring decisions.

Start with a clear competency framework

“Thin-slice judgments,” according to Deloitte researchers—like those based on a handshake and brief intro—have historically influenced final interview evaluations. In most cases, the rest of the interview simply confirmed that initial judgment. The problem? Those first impressions do a poor job of predicting actual job performance.

Mark Linnville, head of talent at Garner Health—a platform to help employees find the doctors they need—uses a proactive and systematic method for measuring and refining hiring practices. “There is standard performance data that you are able to track through review cycles, but that is largely too lagging to make a difference quickly enough,” Linnville explains.

Instead, his team focuses on early indicators that provide faster feedback loops. At Garner, they track metrics tied to onboarding processes, particularly “time to productivity,” which allows them to generate quality assessments much sooner and make adjustments to their hiring process in real time.This data-driven approach starts by building a clear “competency structure” that acts as a shared language throughout the organization. “Generally, our process is to first establish what the philosophy of performance is, then focus almost maniacally on Garner’s competency structure,” Linnville notes.

By standardizing this foundation, the team can collect consistent data and conduct meaningful analysis. With that in place, they’re able to work backward to identify the patterns that separate successful hires from those who don’t perform as well.

Use early indicators to improve in real time

This “reverse engineering” process, as Linnville describes it, involves examining multiple data points from successful employees to identify predictive indicators. His team’s analysis extends beyond resume characteristics to include how candidates deliver responses to interview questions and which specific assessment tools prove most predictive of long-term success.

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