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Tech Disruption: How Data Driven Hiring is Changing Recruiting

Two truths have collided: Every business is only as strong as its people, and technology has disrupted everything. The result: Technology is having a revolutionary impact on what used to be a people-driven, instinctual process.

What is Data-Driven Recruiting?

Using data to drive marketing, sales, or other business outcomes is a standard issue for most companies. But recruiting and HR have always been people-centric, more gut-feel than data-driven.

Data-driven recruiting relies on the facts that are collected, collated, and analyzed by software. This data can help organizations create hiring plans and select candidates. These tools can help your recruiting team grow more efficient by pinpointing repeatable tasks that lead to success.

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Interestingly, just last year Google secured a U.S. patent on “Systems and methods for data-driven identification of talent.” — “The present disclosure describes talent-identification systems and methods that can be used by companies to assist in the recruitment process for new employees. Additionally, the systems and methods can be used by job seekers to determine ideal career fields and industries. The systems and methods may employ an array of neuroscience-based tests to assess a user’s career propensities, after which the systems and methods can provide career recommendations to the user or report on employment suitability of the user to a company.”

The full patent can be seen here.

Why Should Business Leadership Care About Data-Driven Recruiting?

Even in the the hard-to-identify leadership level, data-driven recruiting can help identify leadership skills in candidates. It can also:

  • Help better allocate budget by targeting the recruiting channels that yield the best candidates more quickly.
  • Increase efficiency and productivity by tracking routing activity. This data can help teams normalize time to hire trends.
  • Shorten the “sales cycle” by tweaking the application and interviewing process.
  • Discover and correct hidden biases in hiring by reviewing demographic applicant data.
  • Forecast hiring curve with data that shows how much activity is required to find a particular type of technologist or other specialists.
  • Reach hiring decisions that are more predictable and lessen hiring risks by making data-driven, not emotional, hiring decisions.
  • Use the data to show the effectiveness of the hiring team and justify additional purchases of software tools to help the team become even more efficient.

Data metrics must be actionable for recruiting teams to improve.

A paper by multiple Cornell scholars published in 2019 in the Journal of Sociological Methods & Research shows how data can be used to reduce bias. The paper is titled “Model-Based and Design-Based Inference: Reducing Bias Due to Differential Recruitment in Respondent-Driven Sampling,” and states:

“Respondent-driven sampling (RDS), a link-tracing sampling and inference method for studying hard-to-reach populations, has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. However, some of the assumptions are prohibitively difficult to reach in the field, and the violation of a crucial assumption can produce biased estimates. We compare two different inference approaches: design-based inference, which relies on the known probability of selection in sampling, and model-based inference, which is based on models of human recruitment behavior and the social context within which sampling is conducted. The advantage of the latter approach is that when the violation of an assumption has been shown to produce biased population estimates, the model can be adjusted to more accurately reflect actual recruitment behavior, and thereby control for the source of bias. To illustrate this process, we focus on three sources of bias, differential effectiveness of recruitment, a form of nonresponse bias, and bias resulting from status differentials that produce asymmetries in recruitment behavior. We first present diagnostics for identifying types of bias and then present new forms of a model-based RDS estimator that controls for each type of bias. In this way, we show the unique advantages of a model-based estimator.”

Big data can do all of these things, but collecting data is only part of the success story. The data must be extrapolated and analyzed in ways that are meaningful to the organization on the whole. Fast-growing companies increasingly rely on big data tools to find developers, engineers, and executives. It’s not just data mining; business leaders also must also have the right people in the right seats to make the information actionable to the entire recruiting team.

(Note: For more, LinkedIn published a useful eBook titled “Data Driven Recruiting: Find quality hires faster with talent pool analytics.”)

Using Data to Drive Recruitment Process Improvement

Like any business process, hiring needs to be reviewed retroactively to improve future performance. Data can play an important role here.

Google Staffing Manager Jeff Moore notes: “Looking backwards shows you trends within the funnel, and can help you learn what a good or bad candidate looks like based on historical hiring. Not only does this approach let you source more effectively and target people you’ll be more likely to hire, but it also allows you to take a deeper look at the talent you’ve added to the team and ask if that’s the right talent moving forward.”

“For example, say you look back at your hiring data and find that a high percentage of your hires that performed best over the past year, lacked a specific technical skill when you hired them or didn’t have a specific degree. Yet, because the team thought these criteria were important to do the job well, your recruiters often rejected applicants because they didn’t have that degree or skill. Once you make the connection in the data that your resume filtering criteria is disqualifying a lot of promising candidates, you could change screening criteria to help bring more viable candidates into your pipeline.”

Moore continues: “Analyzing your hiring data also allows you to myth-bust stories about the hiring process heard around the organization. For example, a hiring manager may say, “We only hire people from the best computer science programs.” By analyzing your hiring data, you’ll be able to see if this is true, or not.”

Can Big Data Improve IT Recruiting?

While software can’t completely replace the human element in hiring, the data can improve the efficiency of these teams. Business leadership is increasingly tasked with doing more with fewer resources, so a data-driven recruiting process add value here.

But it’s not just the recruiting teams’ actions that can be improved by analyzing data. It can also help you create a candidate persona that accurately predicts success in a given role. While a resume and prior work experience in a vacuum can accurately predict leadership skills, big data can help organizations roadmap the kinds of candidates that succeed in the company and in the role they’re hiring for.

Developing a data-driven recruiting strategy requires a constant process of testing, measuring, and continuously improving your recruiting efforts. Data-driven recruiting can help organizations meet and exceed their IT recruiting goals.