Data literacy - the hidden recruiting superpower

It’s no secret that recruitment has lagged far behind in the collection and use of data. While many recruiters are sitting on vast amounts of candidate data and could access additional data from the company’s HRIS, they rarely do or may not know how to combine it.

They don’t rely on data to screen or score candidates, nor do they objectively define quality of hire. They continue to report activity KPIs rather than value-based KPIs based on data.

Recruiting still too often relies on a variety of assumptions, stereotypes and beliefs. Here’s a small excerpt:

• The university or school a candidate graduated from also determines the quality of the candidate.
• Grade point average is important in assessing the quality of the candidate.
• Work experience is a good indicator of job performance.
• Interviews are as or sometimes more informative than employment tests and assessments.
• Cultural fit (i.e. personality) is very important.
• We post job ads as we have always done.
• Quality of hire is determined by manager satisfaction and average length of employment.
• Older candidates are generally less desirable than younger ones (but we never admit that).

HR departments today naturally want to distance themselves from all of the above, but if we’re honest, many of the above are still commonplace today. Data could help us fully break free from this and force recruiters and hiring managers to focus on specific skills rather than biases, old routines, and gut instincts. Here are a few areas where data and AI are proving useful:

1. Reducing the number of unqualified candidates applying for jobs by better matching applicants to job requirements.
2. Enabling recruiters to present fewer applicants by ranking and evaluating applicants based on specific skills, personality, cultural fit, or qualifications.
3. Eliminate overt and hidden biases and ensure objective selection criteria.
4. Objectively define the quality of a candidate and a new hire.
5. Selecting appropriate media channels for job ad placement based on performance data.

Artificial intelligence could already help. Data scientists are already using Narrow AI (narrow artificial intelligence) to provide calibrated and valid results. IBM and Google use Narrow AI to win at chess and Go and to teach self-driving cars. Narrow AI learns things quickly and can become an expert in a small field. This can also help in recruiting. With assessment and matching tools that use Narrow AI, recruiters can examine employee data and determine employee success characteristics. It can recommend candidates based on skills and qualifications, and eliminate bias and prejudice. Coupled with algorithms, it can provide answers to questions like these:

1. When is a passive candidate most likely to be receptive to a call?
2. Where should we post this job ad for maximum response?
3. Is candidate A better suited for this position than candidate B?
4. Which of these skills are most important for success in this position?
5. Which skills are most commonly used in this role?

This may sound fanciful or overly optimistic, but AI can really answer these questions. Tools like chatbots, online screening and assessment tools, or VONQs Job Marketing are available and working. We can only advise to just give it a try.

If recruitment is to be successful in the company in the future, data must be able to be interpreted and recruiters must learn the basics of data analysis. Communicating in the language of data will make conversations with hiring managers and executives more effective, and there will be less opportunity for dispute or contradiction. 

The future will ask us to marry hard data with business context to define a strategy, track it clearly, and optimize it. Candidates and hiring managers will demand answers and facts, no more opinions or feelings.

If you no longer want to post job ads based on gut feeling, feel free to put our platform through its paces.