Recruiters today have access to more information about candidates than ever before.

No longer limited to what appears on a resume, recruiters can scour social media, scrutinize references, and make use of predictive analytics to inform their hiring practices. But with so much data available at their fingertips, companies need to know how to weed out the distractions and focus on the information useful to finding the candidates they really want.

So how do you know what data is useful? After all, about 80% of big data is unstructured words. Consider that resumes are just a collection of carefully considered terms and phrases a candidate believes captures their skills and experience. And you don’t have any way to verify they have the expertise they claim.

So how do you break through the noise and gather data that's actually predictive of performance? Consider these four data collection strategies for making smarter, data-based hiring decisions.

1: Relate it to the role
Before you can collect data relevant to your hiring procedure, you first must know what you’re hiring for. How do you establish job-relevant hiring criteria? You perform a thorough job analysis, formally examining the knowledge, skills, attributes, and other characteristics required for job performance. A multimethod job analysis usually features direct observation of incumbents in the day-to-day performance of their jobs.

Other strategies include conducting focus groups with subject matter experts, managers, and current employees to describe, discuss, and document what contributes to superior job performance. Determining job relevance also requires a thorough review of descriptive information about the job, training materials used to support performance development and job proficiency, and structured job analysis questionnaires to capture specifics about the performance environment and operating context. Outcomes from the job analysis provide the framework for establishing what evaluation criteria and methodologies to use to evaluate candidate job fit.

2: Collect it with intention
Effective data capture should help streamline what you want to know about candidates, but you’ll inevitably capture unnecessary information along the way. A resume is an example of passively collected data—it’s based on what the candidate decides is valuable for you to know, rather than what you decide you need to know. What can you do to increase the effectiveness of using data to identify best-fit candidates? Switch from passively accepting random data to actively collecting relevant and structured data from candidates.

Intentionally collecting data begins with experiment design. You determine what you want to know about a candidate’s work history, work style, specific skills, and knowledge as it relates to success in the role. Effective intentional data collection requires an understanding of the baseline skills required for the job based on direct—not proxy—measurement.

For example, a candidate’s earning of a college degree is a proxy measure. It shows they have certain skills, but not if those skills are job relevant. How can you measure skills for job relevance? Create an evaluation exercise that solicits specific demonstration of knowledge and abilities—instead of just hoping the skills you find listed on a resume or social media profile will produce the job performance you’re looking for.

3: Cast a wide net
Capture a wide range of candidate information by going beyond descriptive words about their experiences. Make sure your data collection efforts capture a diversity of performance potential, from physical, sensory, and cognitive abilities to reasoning, communication, and interpersonal skills. Collecting information from various and diverse assessment exercises will provide you with a more comprehensive and robust view of your candidates. Over 100 years of published research on selection science shows that multimethod evaluation is far superior to a single-method process.

4: Find empirical value
Too often, bright people in successful companies sit around a room and say what they think they should be screening candidates for. But they rarely discuss how to weight each of those attributes when comparing candidates. Data validation analysts can provide evidence of which candidate variables are most important and how much to weight any given trait. For example, you may give favorable weight to a candidate who uses the word coaching on their resume. A better strategy is to ask the candidate what percentage of their time was spent coaching and relate the extent of this experience to on-the-job performance.

Similarly, you may think you need someone with previous P&L responsibility, but what you really need is evidence that someone can use a P&L statement to identify trends, areas of concern, and opportunities for improvement. A candidate may report customer service experience on their resume, but you can’t know what customer service looks like to the candidate until you see them in action. Determining how proficient a candidate is in any of the various skills listed on their resume is impossible until you can observe how they perform on-the-job tasks.

Better data = better choices
Finding the right-fit talent the first time is key to creating a high performing workforce. But a seemingly limitless supply of candidate data makes identifying the skills and competencies that can predict success feel overwhelming. Building a data collection methodology that includes the above four strategies will help you collect the information you need that reflects the complexity of the job and evaluates your candidates accordingly.

Identifying the relevant values, weights, and meanings of skills across the candidate pool will enable you to make the most of the most meaningful data—and make better informed hiring decisions.

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