As recruiters, we have access to a lot of data. But historically, there has been a lag between gathering that data and actually doing something with it. With a rich set of data, we can improve the recruitment process from the candidate experience to eliminating (or at least reducing) bias.

We can access:

• Job history data: Has this candidate changed jobs often? Are they ready to make a move now? What experience do they have?
• Location data: Will the candidate be willing to relocate, or can they work remotely? Are they in an area where they have access to travel (major airports, etc.) if needed?
• Education data: What education does the candidate have and what skills have that education equipped them with? Is their education up to date, or will they need some retraining or updating of their skills?
• Skill data: This talks to the skills the candidate actually has, whether from job or life experience, education, or some other source. It can help you see if the candidate has the tools they need to do the job or can learn them.

All of this data allows for better, data-driven decisions. But what does that data actually do for you and your company?

Improve Candidate Quality

When you combine all of the data above, you can get a personal evidence record that tells you a lot about your candidate. But how do you know their skills translate? One way is to ensure that you are speaking the same language when it comes to skills.

An advanced solution for this is to use Rich Skills Descriptors when writing job descriptions to ensure that you are targeting the right candidates in the first place. Using the right terminology ensures that the candidate is clear about what the job is and isn’t, and what skills they need to bring to the table. This can eliminate a glut of unqualified applicants who may be confused about what you are offering.

This also helps in your evaluation and interviewing process. If you are clear on the skills needed for a given position, you can quickly filter candidates who don’t have those skills or transferrable ones that could be translated into hard skills.

Clear language, starting with the job description and running through the entire hiring process, keeps your candidate quality high and helps eliminate the dreaded mistake of hiring the wrong person for the job.

Eliminate Bias

Ideally, this data could be analyzed effectively and without bias based on age, gender, sexual orientation, ethnic background, or any other biasing factor. However, a word of caution here. Amazon learned a tough lesson in robotic process automation (RPA) when they had to scrap their hiring algorithm when it actually introduced bias based on the data provided.

It’s important that companies work to make sure data is truly anonymized before relying on such systems, but the right data process can eliminate, or at least reduce bias in the hiring process, and more work is being done in this area all the time.

Forecast Vacancies and Turnover

We know from a variety of surveys, including a huge one by Microsoft last year, that the average employee stays at a job between 3.7 and 4.2 years, and that number is dropping every year.

The days of staying with an employer for your entire career are mostly a thing of the past, and often startups and other emerging companies don’t last, and employees are forced to change jobs. Post pandemic, more people than ever are changing careers and returning to school.

With solid data, you can predict vacancies and turnover, and even take measures to keep your employees engaged and satisfied to reduce those numbers. With turnover being the most expensive HR cost companies face, this can produce huge revenue savings and make your company a more desirable place to work.

Access a Larger Talent Pool

The right data, along with the increase in remote work, can give you access to a larger talent pool. That, and improving the quality of your candidates, can help increase diversity, add new voices to your workforce, and give you more candidates to choose from.

This access is a game-changer for many industries and employees. No longer having to live in a high-rent district in a large city means greater flexibility when accepting a position. The more options you can offer employees, the wider the talent pool you can reach.

Reducing the Time to Fill

All of these work together to reduce the time it takes to fill a vacant or new position, and overall improve the efficiency of your hiring process. Behind attrition, vacant positions come with significant costs and often lead to other, overworked employees which can then add more attrition.

Filling vacant positions quickly also reflects positively and improves your employer brand. It shows employees you are proactive in supporting them by filling empty roles, and it shows other candidates that your company is a desirable place to work.

It’s a win-win for everyone.

Reduce Cost Per Hire

Finally, the right data and using it properly can reduce your overall cost per hire. From job advertising and other recruitment costs to time and money spent on the wrong candidate to the ability to fill positions quickly when needed, making data-driven decisions saves you money.

The amount of data is increasing, and how we analyze it gets better and faster all the time. Leveraging data for recruitment isn’t just a series of trends. It’s the future of HR when it comes to hiring. And those who fail to adopt now may find themselves left behind.

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