How Algorithms Are Changing The Face of HR

Data-driven HR is here to stay

Thanks to technology, the business world continues to move faster and faster. In this world of constant innovation, the most successful organizations are those that can keep up with or even get ahead of the technological trends. Just think of Google or Apple.

Of course, creating an innovative company is impossible without the right workforce. Hiring the right employees for the right roles at the right time is critical, and it requires good resource management to do it. That’s why many corporations are leaning more and more on HR data and algorithms to help with their decision making. In this article, I want to lift the lid on how some of those algorithms are changing the face of HR, and the kind of systems you can expect to see in your workplace within the next decade.

Driven by organizations like Google, algorithms are now being utilized across marketing and customer service in the form of conversation AI, and in many HR departments. You can compare it to how an organization uses Google Analytics to improve website performance, except that HR data focuses on employees and candidates.

One of the beauties of data-driven HR, or people analytics, is its flexibility. Algorithms can be adapted to measure specific elements which can help organizations address their most pressing HR concerns. These are the three areas of HR in which algorithms are having the biggest impact.

Any HR professional will tell you that their field isn’t only about hiring and firing. The fact remains, however, that overseeing hiring and onboarding is still key to HR. It’s also an area in which algorithms have a considerable influence.

The impact of hiring algorithms on HR has been widely tested and analyzed. A variety of different studies, including one by the National Bureau of Economic Research, have come to the conclusion that recruitment driven by data and algorithms leads to higher quality hires for companies.

Algorithms can collect and process a huge volume of data. They can analyze information from CVs, publicly available information, and responses to assessments, which allows them to build a comprehensive picture of any and every candidate.

These data allow for the identification of qualities that make for a successful employee. Hiring teams can then look for those qualities within the skills and personality of candidates and choose people who are best suited for the job. Not only does this make for a better hire, but it also allows hiring teams to make decisions much faster than before.

The fairness of data-driven decisions also prevents human error from getting in the way of hiring the right candidates. Proponents of hiring algorithms argue that the algorithms can remove human biases—biases that color decision-making—from the equation. As explained by Nathan Kuncel, professor of psychology at the University of Minnesota:

‘We haven’t concluded that human judgments have no value. It’s just that these judgments come with a package that includes bias. People can get hung up on one piece of information and make too much of it.’

At best, biases can mean making the wrong hiring decision, and at worst, they can derail the hiring process completely. In theory, algorithms would remove that subjectivity.

With that said, the average person on the street would not like to have their CV screened by an algorithm. A study conducted by the Pew Research Center found that 76 percent of US workers would not want to apply for a job where their CV was screened by an algorithm, and most people think algorithms would do a worse job than a human.

There’s one final way in which algorithms have impacted hiring. That’s by analyzing and reassessing the hiring process itself. Part of Google’s early research into data-driven HR focused on the optimal length of the hiring process.

The results of the research led to Google’s so-called “rule of four” for interviews. They found that four interviews were optimal for hiring. Further assessment of candidates gave little additional value. Thus, Google recommended the shortening of what were often far longer hiring processes, a decision that saved both time and money.

Algorithms also shape HR when it comes to predictive modeling and analytics. Predictive modeling software uses algorithms to find patterns in large volumes of data, allowing people to more accurately predict future trends.

In the field of HR specifically, the data concerned would be information about an organization’s workforce. HR professionals and executives can use algorithms to identify factors that make for successful employees, that influence employee retention, and more.

Discovering what characteristics make successful employees can help your recruiting team find and attract better candidates for your organization. Meanwhile, identifying reasons for turnover and attrition is vital to workforce planning. Predictive modeling in this area can help firms answer the following questions:
 

 
Answers to these questions help HR departments get ahead of any problems and take pre-emptive action to retain the staff they might otherwise lose. Such modeling can also help identify skills shortages and leadership needs, aiding firms in mitigating the risk of a resources gap. Firms can identify potential issues or other patterns within their workforce, and they can do so before problems come to fruition. That allows them more time to find solutions.

Predictive modeling and analytics are particularly important to firms experiencing rapid growth. These are the businesses at most risk of workforce problems. When a company grows at pace, it’s more difficult to ensure that the workforce keeps up. Algorithms help these firms stay ahead of the curve.

Employee satisfaction and retention are key to business success. Its how firms retain their edge over competitors. Its also another of the major areas where algorithms are changing the face of HR. The way this is done is by combining data-driven HR and behavioral psychology. Data for the algorithms is drawn from a company’s work environment and internal surveys. Then are processed to identify critical behavioral changes that will have the biggest positive impact on the happiness of the workforce at large.

Data-driven HR helps to remove human bias from recruitment. However, poorly implemented algorithms could actually reinforce discrimination if they are designed to focus on the wrong character traits or qualities in applicants. In extreme circumstances, this can lead to certain groups being overlooked completely and thus an unfair hiring process.

The concerns surrounding data-driven HR shouldn’t be minimized. However, that doesn’t mean algorithms in HR aren’t useful and valuable when used properly.

The success of Google, as one of the first to use data-driven HR, is compelling by itself. Add to that the studies which have provided evidence of its efficacy, and it seems data-driven HR is here to stay. By considering how HR algorithms could fit within your organization, you can propel your organization into the future of HR and use the best tools and strategies to help your people at the same time.

This article was submitted to www.BambooHR.com/blogs by a Guest Blogger.  Click HERE to read the original blog.