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How AI makes data-driven decisions possible in recruiting


Data is a guiding light. Staring directly into it can blind us, but with it, we can see everything else.
The facts and figures present in any given dataset tell stories, identify trends, and chart courses of action. But none of these come to fruition by the mere presence of information. Empirical evidence usually has this intended effect when those in possession of it commit to data analytics — a critical foundational need in every company that wants to analyze data and use it to make decisions.

In the past, data has dictated business practices to a certain extent. But the reliance some organizations have on data has kicked into high gear in the past few years. According to research by the Corporate Research Forum, 69% of companies that employ 10,000 or more people use dedicated data analytics teams. These teams support any number of departments, including talent acquisition.

The sheer amount of information HR departments collect during one recruitment period makes data a useful and invaluable asset. Large datasets that contain general and niche candidate information allow HR professionals to target and tailor their approaches, engagement, and outreach efforts while improving their internal decision-making.

The larger and more comprehensive a dataset is, the more likely it is to provide a full spectrum of the options and approaches at a department’s disposal. With the litany of important choices that HR departments must make, a commitment to data positions these teams to make decisions that are in the best interest of the organization as a whole. The more diverse the dataset you have, the more diverse your decision-making will be.

Data is the foundation of recruiting

Data analytics can factor heavily into how your organization identifies recruiting software that best fits its needs. Look at reviews, statistics, and results on prospective solutions. Exasery CEO Wim de Smet forecasts that technology will “analyze work production instead of work time,” leading to “more result-driven performance analysis” in HR as well as more important results.

Here are a few examples of the role data analytics can play in realizing an recruiting department’s long-term goals:

Making the right hire from the start. 

CareerBuilder estimates that a bad hire can cost companies $7,000 to $10,000 for an entry-level or mid-level role and closer to $40,000 for a manager. With as tricky (and expensive) as hiring is, data analytics can be a useful resource to help mitigate any inherent risk.

Using data in recruiting allows organizations to evaluate everything a candidate brings to the table. Examples of qualitative data include determining how well someone might fit into a given role and how long he or she is likely to stay with the company. Recruiters can use these data points to ensure they bring on someone who will excel in the role and contribute to the company’s long-term growth.

Making more efficient decisions. 

HR folks spend a lot of time recruiting and interviewing the most suitable candidates, which is costly. When equipped with the right information, however, companies can more effectively sift through lists of applicants and only interview top candidates that are right for the role.

With proper data analytics, recruiters can eliminate guesswork and land the right hires quicker and with more certainty. They can then devote more time to candidate engagement.

Making successful hiring patterns a habit. 

Data’s real value comes when it helps decision makers settle on better choices. When it becomes routine to make these “right” decisions, companies will bring on more useful people with long-term potential and derive greater value from their data.

But data alone can’t make those processes repeatable. While data analytics steers companies toward those decisions, artificial intelligence and machine learning log the intricacies of those picks and save the nuances for later use. Wells Fargo, for example, practices data-driven recruitment by applying predictive methods to verifiable data such as areas of expertise, job history, and tenure.

The application of big data in HR functions is rife with possibilities for optimizing hiring, onboarding, training, promotions, and retention.

Data analytics in a nutshell

With all the talk about data analytics, it’s crucial to understand what it means. In short, it involves taking a slew of raw data points and drawing conclusions from them. For example, data analytics in HR helps recruiters with sourcing and mining information on potential candidates.

The information itself can be granular, but data can be analyzed for different functions: Do you want to learn more about a past initiative? Do you want to predict how a future initiative will go? Both are possible. Data analytics typically falls into four categories, all of which can be used across various business functions:


This analysis makes sense of large amounts of data, and it’s the most frequently used function. It helps humans identify and understand patterns. Using descriptive data, departments break down large datasets, develop key performance indicators, and summarize their findings for stakeholders. This form of analytics necessitates collecting relevant data, processing that data, and then analyzing and illustrating the information. HR might use descriptive analytics to determine turnover rates, for instance.


This analysis helps humans answer questions about why things happen. The performance indicators are researched in greater depth before a conclusion is reached. That research process includes identifying anomalies and attempting to find trends and relationships within those anomalies. For example, HR might use diagnostic analysis to find out why people are choosing to leave the company or why there is low engagement on job postings.


This method attempts to forecast what might or will happen in the future based on past occurrences. For example, healthcare companies might use predictive analytics to predict someone’s future health based on genetic and lifestyle factors. Predictive analysis leverages machine learning, decision trees, neural networks, regression analysis, and multiple other approaches. Recruiters might use predictive analysis to guess how long candidates might stay in a given position.


Here, data crunching helps us determine what should happen. Machines will sort through inputted data to find the easiest, cheapest, or otherwise most valuable options. Using predictive points, analysts can give advice on which decisions serve an organization’s best interests. If HR uses predictive analysis to determine whether someone is at risk for leaving the company, for example, prescriptive analysis could suggest which training regimen might engage that individual further.

When considering what data driven recruiting means for HR, these four pillars can make a significant difference. Each category plays a role in the use of recruiting analytics — especially when it comes to assessing and projecting the trajectory and qualifications of candidates.

The types of data that support HR functions

At this point, we know there are several methods of using data analysis to solve business and HR problems. Just as there are multiple ways to use data analytics, there are numerous types of data that support HR practices. Suggested resource on HR practices: How to build HR flexibility and resilience post-COVID. Here are three data types used in the HR industry:

  • HR analytics is a blanket term that covers such areas as payroll, salary history, resume information, and job growth. The information itself is vital to HR departments that have to track and monitor employee information for compliance and bookkeeping purposes. The data helps indicate what resources — if any — a department needs to be effective.
  • People analytics, also known as talent analytics, is the sweet spot for recruiters. This information includes a candidate’s resume, time spent with other companies, development in previous roles, education, online presence, and any personal branding specifics. In short, people analytics is the type of HR data that puts candidates on recruiters’ radars and indicates how well they’ll fit within a company.
  • Workforce analytics is the type of HR data collected to determine how effective an employee or group has been since being hired. Employee experience, for example, is a vital workplace analytic metric that can shed light on retention and an employer’s ability to foster a productive and supportive work environment. This type of HR data also helps assess how people thrive in certain positions or within an organization, offering insights on how to hire in the future, what kind of tools are needed, and how best to support employees.

In its own way, each metric addresses one or many of the HR challenges that companies face. Each piece of information can pinpoint gaps in hiring strategies and catalyze more efficient and logical ways of operating.

How data supports decision-making in recruiting

Data-driven decisions require a data analytics strategy that illustrates what information is needed and how success or failure will be measured. To apply that approach to a recruiting team’s decision-making process, an organization might need to craft the profile of an ideal hire. That profile would include specifics about the initial outreach, conversations, successful interview techniques, on-the-job successes, and relevant sources to find similar candidates.

All of these are easy-to-verify pieces of information that are essential to hiring top talent every time. Here are a few ways recruiting leaders can improve their data-driven decision-making as it relates to recruiting and hiring:

Research promising candidates

If someone seems like an attractive candidate on paper, look for particulars that confirm (or deny) that hunch. This could be time spent with a company, LinkedIn reviews, or other resources.

JetBlue Airways previously listed “niceness” as the most crucial trait its flight attendants should possess. After partnering with the University of Pennsylvania’s Wharton School of Business on a consumer data survey, JetBlue found that customers valued helpfulness above other attributes. To its credit, the company took that information and incorporated it into its next round of hiring. HR analytics can narrow searches and fuel data-driven decision-making.

Look into hiring trends

Look at how similar candidates or similar job searches have fared and then apply those findings to your current crop of candidates. Big data decision-making is built on spotting successful patterns to replicate and unsuccessful ones to avoid. Most AI tools will do this for you.

Workforce analytics can help companies improve productivity by pinpointing habits of effective employees and applying those tendencies to team members or areas of the company that need bolstering. Take insightful information into your data-based decision-making to pick up trends and solidify your strategy.

Suggested resource on candidate pipeline strategy: Recruiting Strategies to attract a more diverse candidate pipeline

Show recruiters successful techniques

Pull data regarding successful hires and use them as examples for recruiters to follow. Do a deep dive into numbers to determine which candidates look for which jobs, encouraging recruiters to look for that information when making data-informed decisions.

Using data to drive organizational decisions can be a massive undertaking without a solid foundation. Once the hiring strategy is locked in, find a way to make the transition seamless so that data-backed decision-making operates as intended.

Implementing data into recruiting practices

HR professionals must build effective HR analytics strategies that make the most of their datasets. Start by writing down what your business needs, what it does well, and every facet of what your recruiting team is currently doing. Examine parts of the process that data has the most insights on, and apply your findings there.

Don’t get too complicated too fast, though. Trust us when we say data will be the most helpful when it fits naturally into your everyday processes. It should meet you where you are and work for you instead of the other way around. Here are four critical steps to implementing a data-driven HR strategy:

  1. Focus on goals rather than technology. Following through and staying committed to your recruiting data strategy should hinge on your vision — not a technological solution. Polish and perfect your vision before data, technology, or anything else is brought into the fold to ensure you have a solid foundation.
  2. Decide what to track, monitor, and measure. There’s a difference between metrics and analytics. Metrics are measures of operational success and efficiency — analytics are used to hone in on decisions. Using data in HR requires differentiating between the two, picking a direction, and basing your data collection and decision-making on that choice.
  3. Pick the right data for your team. Do you need recruiting analytics? People analytics? Workplace analytics? All of them? Find out before putting anything into motion. Maybe your needs focus on employee fit, benefits, or professional development. Data has every possibility covered and can better inform your vision and decision-making.
  4. Understand your strengths and weaknesses. Build an HR data strategy to get a sense of both aspects. Thankfully, you can use HR data to learn more about the pluses and minuses of your recruiting strategy. Most recruiting is wide-ranging and crosses multiple channels. If your data says that approach works for you, great. If it appears something else is needed, use data to chart your next move.

Data alone isn’t a fix — and getting your people on board with using it can be a challenge. At the end of the day, you should base your data implementation on your overall vision and values while finding natural ways to plug in data-driven strategies.

Common challenges recruiting faces when using data

As you implement data into your HR processes, you will likely face some common sticking points. Data can indeed help solve HR challenges and solutions, but it can be difficult for human beings to change mindsets, adapt to new technology, and embrace new systems. There are a few challenges you can expect to overcome as you incorporate more data into your processes:

1. Lack of innovation

Not much has changed in the past 20 years for HR departments. The goal of building and fostering a great team and environment remains the same, but so have ways HR professionals have gone about achieving those goals. New approaches can be challenging to come up with and even harder to gain buy-in for, so it’s no surprise HR departments stick to what has worked.

Even if the technology is integrated into an established process, that won’t make a difference if the core steps are still in place. Workforce analytics is a type of HR data that can speak to this gap.

2. Lack of urgency

Most organizations only use data that aligns with their processes. In the case of HR, a department might rely on the same three vetting prerequisites while ignoring five others that could be useful.

This indicates a lack of urgency and an absence of ambition to find the best candidates. HR analytics exposes recruiters to volumes of information that can lead to an improved vetting process. Workforce analytics and people analytics, for example, can each unearth new and unique data related to effectiveness and employee satisfaction — both of which are essential for effective HR work.

3. Lack of strategy

For some people, it’s more appealing to think about shiny objects than to understand what they actually do. In the case of using data in HR, that means people might not know what types of data support HR practices or how to effectively leverage them.

Workplace, people, and HR analytics all shine a light on strategy and force HR departments to take a long look at what data they have and how it can be used to make recruiting more effective. Strategy will help you know what you can achieve in the present to make the most of the future.

4. Lack of tracking

If successes and failures aren’t properly tracked, routines can’t be optimized. Tracking takes time to review and is tedious by nature, but a failure to do so puts any initiative behind schedule.

HR analytics represents logistical and compliance-focused figures that tell recruiters whether their approaches are in line with established oversight and industry competitors. Incorporating them into tracking is an excellent way to ensure operations are going as they should.

The future of data in talent acquisition

Data will only grow more accessible and essential to the future of talent acquisition management. As these datasets grow and become more common, AI and other resources will be trusted to analyze and turn these findings into useful HR data strategies.

HR and data are just scratching the surface of their partnership. As recruiting teams look to sharpen their approaches, here are three ways they can use data in their recruiting capabilities of the future:

1. Track your success

Future recruiting practices should come from successes (or lack thereof). Look at your recruiting data strategy and determine what works, what doesn’t, and what’s contributing to your bigger goals. Stay on top of new findings, compare them to old patterns, and see how each contributed to the current state of your recruiting strategy.

2. Keep an ear to the ground

Data is only helpful when it’s integrated into real-life scenarios. Gather that data by surveying candidates and asking employees what they need. You can then take those findings and apply them to your recruiting strategy.

Look at candidate feedback in comparison to your hiring metrics to find correlations between the two. HR big data use is most effective when you’ve established those patterns and applied them to future HR practices.

3. Stop at the water cooler

A recruiting data strategy is helpful in acquiring talent, but you can also leverage it to learn how your company can be of better service to employees. Look for the data and analytics that will give a glimpse of what they need to be more valuable to the company.

For instance, IBM analyzed sentiments gathered from its internal social network to build a new review system. The responses varied, though most team members expressed dissatisfaction with being graded on a curve. The company heard the feedback and responded accordingly. This information can give you a glimpse into the present while building toward a future in which data can help enact change.

Suggested reading on talent fostering: Unlocking the Hidden Talent Pool

Data tells stories, kick-starts initiatives, and brings about change. Incorporate it into your strategy to light the way for the future of recruiting.


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