Consider this: a dedicated recruiter can spend in excess of two months of their time reviewing initial candidate applications. No matter how many recruiters a company has, months can be spent navigating just the initial review activity of the talent acquisition process.
According to a recent report by Aptitude Research (sponsored by Oleeo, the innovation leader in recruiting enablement technology), a growing number of HR and Talent Acquisition leaders are looking to apply data-driven recruiting enablement technology. The goal is to meet the needs of the business and the changing expectations of candidates, recruiters, and hiring managers.
Based on research conducted in February and March earlier this year, the report finds that companies that use data to automate decision making are twice as likely to improve quality of hire. However, talent acquisition has a long way to go: while 72% of enterprise companies believe that using data to automate recruiting decisions is a priority in 2020, only 32% of senior leaders are confident in the data that they have available to make decisions.
Decision intelligence can radicalize this process. It offers recruiters the opportunity to take evidence-based decision making to an entirely new level by factoring in an unprecedented amount of data from a wide array of sources, some of which might never have been considered previously.
If utilized well, practitioners can test theories, proactively solve problems and conduct more complex predictive analytics related to sourcing and hiring strategies. What’s more, this isn’t necessarily something that’s in the future; it’s already here. You’re already using it via your smartphones. You’re already approaching a scenario whereby this will factor into most things that you do.
And the blurring of those lines between employee and social points of view is becoming one of the major features with regard to how AI can help. However, safeguards around its use are essential.
Knowing what worked well in the past can help to fine-tune the types of candidates that carry high favor within a firm. The benefits to recruitment include:
- Saving recruiters’ time
- Getting to the top candidates first
- Finding a needle in a hay stack
- Reducing bias and increasing diversity
When biases are recognized, it’s possible to adjust for them. But this can be tricky when it comes to informing an acquisition strategy through the use of data. As a result, it’s important to clarify this with providers. How is bias going to be identified? How is it going to be eradicated?
So, why is this important? In the eyes of some, the recruiting process is imperfect, elitist and obviously, exclusionary.
With decision intelligence, you can get to the candidates that no one else knows about. Currently, you might receive over 150,000 applications a year from a mixture of core and non-core schools – ‘big data’ can ease this pressure. If used well, it will notify you of candidates that have all the key indicators of success you’re looking for, but that didn’t attend a ‘target school’ – i.e. schools that are not on anyone’s core schools lists but do produce exceptional talent.
If you think about your own recruitment, you’re probably getting 10, 20, 50 or 100 applications for every hire – that’s a lot of information to process and understand, and so employers often resort to limiting the number of applications they receive. This is perfectly understandable because everyone wants to make their jobs more manageable, but this can come with some hidden consequences.
At the same time, you also perhaps limit the number of sources you recruit from. Consequently, we invest more into those sources and we end up hiring even more people from them, unconsciously ignoring others.
Harnessing the potential of decision intelligence means you’re not just dismissing elitism theories, but you’re also identifying and quantifying any historic bias, and therefore reducing it in future decision making. The algorithms eliminate the chance of preferential treatment by not accepting protected category data, and thus reduces the influence of bias.
This means you can mitigate the influence of disparate impact and focus on simply obtaining great hires. Reporting-wise, it helps to ensure that you are providing stronger evidence to support hiring decisions and can accept more applications with lower resource implications. Clever algorithms replicate your collective decision making, reducing the influence of bias by individuals or process.
However, recruiters shouldn’t perceive this as a big obstacle to their careers. Remember, humans still have to understand how it’s being used. Data in itself will not get you a decision. Humans should never be taken out of the equation; the technology is just an enabler.