Meet your new bosses: the super computers
Job interviews may be a thing of the past as firms use 'big data' to predict the best person to employ.
The revelation that Facebook tinkered with the emotions of almost 700,000 users by removing positive or negative posts from their "friends" to study how they would react, shows the growing power that data research wields over our lives.
Computer scientists have been refining and honing the algorithms used to detect patterns in the vast chunks of data we, often unwittingly, share about ourselves online, especially through social media and by browsing the web. When Facebook users first sign up to the social network, they agree to its data-use policy – a 2,200-word agreement that is usually skipped by the average user.
Large companies have typically used the information we provide online – where we live, who we date, which school we went to – to advertise products and services that match our profiles. But some companies are taking "big data" even further, by deciding who to hire and who to promote.
Until recently, one of the most important areas of our lives has remained decidedly traditional. A boss might like our CV because they went to the same secondary school or we might be offered a job because we know how to sell ourselves in an interview. Maybe the interviewer liked our suit, or just had a hunch we would be a good employee. Most of us don't find out why unless we bother to ask.
With the help of data tools, more companies are crunching numbers to judge the value of potential and existing employees in a booming new field dubbed "people analytics".
Large multinationals are investing heavily in data analytics to improve how they hire and manage workers; Google has an entire department dedicated to it.
Valarie Daunt, a director of human capital at Deloitte in Ireland, says 78pc of the 2,500 businesspeople and human resources managers they polled for a recent global study said such analytics were "hugely important" to their organisations.
"The future will be about using analytics to see what type of people applied for that role in the past, and who were successful at that job, and using that as a predictive method for new employees," Daunt says. In Ireland, "we're about five years away from that". Advocates say mining big data will transform workplaces for the better, by matching employees to the right job and making fairer hiring decisions.
The current approach to hiring job applicants is especially flawed, research suggests. Lauren Rivera, a sociologist at Northwestern University in America, spent three years studying the recruitment practices of global investment banks, management consultancies and law firms.
All of these companies were spending millions of dollars on ensuring they had objective methods of securing the best staff. But, when it came down to it, the most common reason that people were hired was the "leisure interests" interviewers shared with job candidates.
Hardly surprising, then, that other research suggests employers would be better off leaving hiring decisions to the machines. When professors of psychology at the University of Minnesota analysed 17 studies evaluating job applicants, they found a simple equation outperformed human decisions by at least 25pc.
Jonathan Campbell, founder and chief executive of Dublin-based Social Talent, agrees with that sentiment. The start-up has trained thousands of recruiters, from companies such as Microsoft and Oracle, on how to use social media to find coveted staff. It has also developed a data tool that aims to track employee behaviour at work, and determine the best performers and the worst.
Social Talent is testing the tool on 2,000 individual recruiters, whose performance in the workplace is traditionally measured by the number of job vacancies they fill and the fee income they drum up. The recruiters have volunteered to have their phone calls, internet browsing and work-related emails monitored, as well as any of the key words they use to search for job candidates.
The resulting trove of information will build a "complete picture of how these individuals do their job", Campbell says. "Then we will ask the [employers] to rank each staff member based on their performance, so we can see what successful people do, minute by minute, compared to people who aren't successful. We've built a whole management system around this so we can adjust their performance by giving people tips and insights on how to do things better as they work.
"We will be motivating individuals for modifying their inputs – just as a mentor would do in real life, by looking over your shoulder and jumping in to offer help.
"There would be pop-ups in the web browser saying: 'You sent four emails today. Do you know that a high performer in this role uses a combination of phone calls and emails? Click here to learn more'."
As Campbell points out, technology has long enabled employers to monitor staff. Swipe cards with time codes show how much time an employee has been out of the office, even if it's just to the bathroom. With GPS trackers, managers of salespeople and truckers can see where vehicles are located or if they have been in one spot for too long. And that trend is becoming more pronounced. Hitachi, the Japanese electronics company, is making a device for employers called the Hitachi Business Microscope. It looks like the ID badges issued by companies to staff and is worn around the neck.
Only its purpose is a little more intrusive: the device is embedded with infrared sensors, a microphone and a wireless communication device to record and transmit to management an employee's every movement.
Hitachi's product tracks everything from how often a worker walks around the office to who they speak to along the way – and for how long. It can even determine whether an employee contributes to meetings, by monitoring if they just sit there silently or display the body language of an animated participant to a meeting, such as by nodding and talking.
Daunt has reservations about how such monitoring would affect employees' productivity, and whether it would be legal to fire someone if the data showed they weren't up to scratch.
"Data has its place, but you have to bear in mind that individuals have personalities and relationships, and I'm not sure data can account for that."
Social media may yet make CVs redundant
Time was, if you wanted to work for a particular company, you waited until it advertised a vacancy, applied for the role by submitting a CV and covering letter, and hoped for the best.
This is unlikely to hold true for much longer.
Smart companies are not sitting around and waiting for the right candidates to apply, especially when they are competing with other employers for valued skills, such as software developers.
Organisations have been using social networking sites for years to research job applicants, but are now sourcing potential candidates directly through these networks.
Consider the job listings placed by employers and recruitment agencies on Twitter using the #jobfairy hashtag.
Daunt says: "There is an increased focus by recruiters to find jobseekers by using the likes of LinkedIn. This puts a great onus on the jobseeker to think about their social media profile and to make sure they are blogging."
Indeed, 44pc of bosses check these social media platforms before recruiting staff, a survey of small and medium-sized enterprises commissioned by Bord Gais showed in May.
And employers continue to check the social media activity of staff long after they have been recruited – more than one-fifth had to fire employees or give them a warning over what they posted on sites such as Facebook and Twitter, the same survey found.
New businesses are cropping up to help employers glean insights about potential recruits from all this information, using tools that combine data from social media sites and other sources, such as internet forums and crowd-sourcing projects, to create profiles of the people these companies may want to hire.
While the focus of such "people analytics" is currently on using big data to find workers in fields where there is a skills' shortage, experts predict that this method will eventually be applied to sourcing potential job candidates in other areas.