Last week, Google played havoc with the lives of many businesses by changing its algorithm to favour mobile-friendly sites, in an event mildly referred to as "mobilegeddon."
Considering that a top ranking on a Google search page can command 20 to 30 per cent of a site's click-through rates, these rankings play an important role for many businesses. And considering that over 40 per cent of Fortune 500 companies' websites are not currently mobile friendly, their rankings may have dropped in one quick swoop.
The potential impact to branding and revenue aside, companies whose websites aren't optimized for mobile devices may also be losing out on opportunities to recruit talent, as that's increasingly the manner job seekers are choosing to search for new roles.
Forty-seven per cent of millennials are using mobile devices to search for new roles, according to recruiting platform Jobvite's 2015 Job Seeker Nation report, up from 43 per cent last year.
Not only are they using mobile devices to look for work, they are searching more frequently, with 45 per cent of job seekers saying they will search for new opportunities even if they are happy in their current role. So a slight change in an algorithm can have big consequences.
But it's not only job seekers who are relying on algorithms to find roles; recruiters and companies are increasingly turning to machines to determine whom they hire.
Two employees at software company SAP – Andrea Woolley, a project director and Matthew Jeffery, vice-president of global sourcing and employment branding – recently argued that the time for machine selection has arrived.
They explain that the traditional method of recruiting – building relationships with top universities in order to entice the best talent – is now an old-school approach. So when SAP started looking for candidates to join their sales academy, they decided to spread a wider net and rely on technology to filter the applicants.
Candidates needed to first take a corporate culture test to determine whether they would be a good fit. If they passed, they needed to complete a "situational judgment" assessment, a test that typically measures candidates' ability to handle situations they could be faced with at work. Candidates who passed both could expect a call from a human recruiter to arrange a day-long assessment.
All candidates found out quickly, if not instantly, whether they got to keep going. The process was likely a better experience for many job seekers, who often wait for what can seem like an eternity for a recruiter or hiring manager to call back.
According to SAP, this screening method allowed them to process 50,000 applicants globally, leading to 500 new hires, with a projected cost saving of £250,000 ($462,000).
It remains to be seen how well those 500 fare in the company over the long-term. Still, data analytics is playing an increasing role in expediting and, in some cases, choosing who gets a job. But deciding the level of human-versus-computer input remains up for debate.
According to the Harvard Business Review, humans are often fallible when it comes to evaluating who is a good candidate for a job. The article argues that mathematical equations trump humans 25 per cent of the time, since we can be distracted by facts that may only be "marginally relevant."
That's not only the case when hiring for entry-level roles; Korn Ferry introduced technology that allows them to better determine who will succeed in C-Suite roles.
This evolution of machine-led hiring is opening up the doors for some very interesting partnerships, including one between job search site Simply Hired and dating site eHarmony. The new service will match job seekers to opportunities based on compatibility, values, culture and competence. The service launches in a test phase in June.
Could this be a golden age of hiring, where computers handle the job more efficiently, effectively and fairly? Chelsea Barabas, a research assistant at the Massachusetts Institute of Technology's Center for Civic Media, is not convinced. As she wrote on an MIT blog post, algorithms rely on a specific set of rules that may unknowingly prejudice the results.
Gerry Purcell, founder of the Toronto hub of Internal Consulting Group, also warns companies to tread carefully when relying on big data for hiring decisions.
"While the engines are more powerful than they ever have been, and the buy-in is higher as the numbers can be crunched faster and easier, the underlying math is the same and, still susceptible to the same biases," Mr. Purcell said.
He notes that the challenge with analytics is that the users who rely on it may lose sight of the assumptions that built the underlying data set. Since there is never "perfect data," some human intervention is necessary. At least, for now.
Leah Eichler is founder and CEO of r/ally, a machine-learning, human capital search engine for enterprises. Twitter: @LeahEichler