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.

Equally important, Linnville notes, is analyzing “mis-hires” to understand what creates false positives in the interview process, helping eliminate factors that may appear promising but don’t actually predict success.

“Overall, I think this can be challenging, and you have to use a process that is fit for the company you are in, as I don’t think that there is a one-size-fits-all approach here,” says the talent pro.

Reverse engineer what success looks like

According to a report from Josh Bersin Company, leading organizations—which the researchers call pacesetters—don’t “chase headcount.” They use data to predict future needs and invest in the skills needed for a future-ready workforce.

An ongoing analysis helps organizations understand not only what has happened, but also whether their current strategies are truly improving on past efforts. “Historical hiring data is the backbone in getting a baseline understanding of what has been done and gives you a jumping off point to measure whether or not your new recruiting strategies are working,” Linnville explains. He stresses the importance of regularly revisiting this data and examining it from multiple perspectives to build a comprehensive picture of the talent team’s performance.

This is an important factor in matching talent acquisition to business objectives. TA teams often track metrics like time to hire, cost per hire and quality of hire. But a report from Mercer questions whether these always align with the organization’s broader people goals. Metrics like skill alignment, time to productivity, retention and employee wellbeing may offer a more meaningful view of success, say Mercer researchers.

Read more: Advanced global talent acquisition tips every CHRO needs

Design talent acquisition moves that reflect the role

A key part of this analytical approach, Linnville says, is making sure data-driven hiring remains inclusive and free of bias. He’s “constantly staring at what is happening throughout [the] funnel” to make sure no demographic is being consistently disadvantaged. The goal, he emphasizes, is to use data not just to improve hiring quality, but to ensure fair and equitable outcomes.

Analysts agree that subjectivity and guesswork are not the best ways to fill roles. According to the Josh Bersin Company, in a fast-changing economy with scarce talent, leading organizations prioritize real skills over resumes. They focus on the work itself to identify key capabilities for future growth and hire strategically—seeking adaptable, cross-functional experts in emerging fields.

The design of assessments themselves has also benefited from an analytical approach. Rather than relying on candidates to recount past experiences, Linnville advocates for assessments that place candidates in situations they might actually face on the job. He suggests putting them “into the shoes of their role” by engaging them in solving a real, challenging problem with the hiring team.

Linnville believes Garner Health’s evidence-based approach provides a much cleaner read on long-term success because it reveals how a candidate would approach a specific situation “they may not have faced before.” If HR teams can understand how job seekers handle unfamiliar situations, he argues, they can scope out a more accurate prediction of performance. This is especially relevant in high-growth environments where adaptability is crucial.

One of the more nuanced challenges in this approach is finding the right balance between structured data and human judgment. While quantitative assessments offer valuable insights, hiring decisions still depend on people. “This is the most fun part of recruiting, because no matter how much we want and use data, at the end of the day it is still a human on the other side,” Linnville notes.

Balance human judgement with evidence in talent acquisition

The key is to ground those human insights in evidence, not gut instinct. Linnville says the “sweet spot” combines codified assessment scores paired with a rubric of subjective insights that “allow everyone to be playing off the same sheet of music” to determine the quality of the interview feedback and insight.

Organizations following Linnville’s methodology can develop deeper insights into what drives success within their specific context and culture. He says data can identify which interviewer techniques are most predictive, which assessment methods provide the clearest signal and which candidate characteristics correlate most strongly with high performance. Linnville notes that you can even determine patterns around “who interviewed them and what types of interviews proved more predictive in success.”

Linnville’s approach acknowledges that while technology and analytics provide powerful tools for improving hiring practices, the human element remains central to the process. As he puts it, the trick is “to use subjectivity but base it on elements that are evidence-based and factual.”