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Business by numbers: How big-data analytics represents the new face of HR


Here’s how big data can help you build a sustainable business:

  • Analytics offer insight free from initial human bias
  • Technology smarts can eliminate unsuitable applicants in an instant
  • Zoom in on talent by screening for temperament
  • Committed HR software use benefits your bottom line

Gone are the days of resumes and interviews. Now data mining can help you discover top talent in an instant – with lasting results.

Leaders today should be wary of placing much faith in resumes because each is just an applicant advertisement or worse, warns Expr3ss! co-founder and chairman, Dr Glyn Brokensha.

“We know that 74 per cent of resumes are misleading and 40 per cent of them frankly contain lies,” says Brokensha, whose background in medicine and psychotherapy influenced his algorithm-based recruitment software, which is designed to help employers hire the right candidate for the job.

Business by numbers: How big-data analytics represents the new face of HR

Brokensha is also sceptical about interviews, because some candidates get stage fright. References are not bad, he says, but he anticipates them to be stymied by potential litigation, which may force referees to give reports based only on formal HR records.

In contrast, cognitive computing can help organisations nix unconscious preferences and biases, which emerge even when HR has the best intentions, Brokensha adds, casting big data analytics as part of a long-term trend.

A growing trend

It’s no secret that over the past decade, big data has transformed the way companies do business, with chief marketing officers tracking shopping patterns and preferences to predict and inform consumer behaviour. Likewise, chief financial officers use real-time, forward-looking, integrated analytics to improve their grasp of various business lines.

The HR department is next, a report published in McKinsey Quarterly says. “Human resources chiefs are starting to deploy predictive talent models that can more effectively – and more rapidly – identify, recruit, develop, and retain the right people,” the report says. “Mapping HR data helps organisations identify current pain points and prioritise future analytics investments.”

Christine Khor, managing director of recruitment company Chorus Executive and author of the HR guide Hire Love, emphasises that dispassionate scrutiny is vital. Back in the day, employers relied only on face-to-face meetings, peer reviews and gut instinct to determine if a candidate had what it took to be part of the team, Khor says.

“But when you gather the right data, beyond credentials and experience, you can get a clearer picture of who fits where and why,” she says. Gathering the right data and shaping your assessments around it yields clear cost-and-productivity benefits, she adds.

Objective data tools – predictive technology, psychometric testing and behavioural testing – in conjuction with face to face interviews and human insight are the best way to find the right person, according to Khor.

“Predictive analytics takes subjectivity and human bias out of human resources, saves time, reduces stress, and makes it more likely that the organisation will hire people who will be the best contributors and stay with the organisation for a long time.”

– Vadim Bichutskiy, data scientist

More flow, less churn

Vadim Bichutskiy, founder and chief executive officer of data science and analytics company Angel Technology Group, describes predictive analytics as “a game-changer for talent recruiting and retention”.

Hiring people is notoriously difficult, stressful and time-consuming because the hiring manager must worry about the candidate’s qualifications and how the person fits within both team and company culture.

“Predictive analytics use data and sophisticated statistical algorithms to predict which candidate is the best fit for the job, who should be retained, and who’s likely to churn. It takes subjectivity and human bias out of human resources, saves time, reduces stress, and makes it more likely that the organisation will hire people who will be the best contributors and stay with the organisation for a long time,” says Bichutskiy, who is also a data scientist with experience building individual-level HR predictive models.

Computers can easily handle the volume of applicants, adds Brokensha, stressing the ease with which the right software can help you build the perfect team. Start by addressing the negative – let your predictive hiring app weed out candidates lacking key credentials, be it a driver’s licence, a mining and minerals certificate or the requisite experience, he says.

The next step, he adds, from perhaps an original 100 applicants is to gauge each remaining applicant’s temperament. Through an checklist or survey, assess their values. Then you can narrow your remaining applicants down from 30 to about five elite candidates.

Finally comes the interview, if at all, Brokensha says. “The interview should really only occur when we understand the person as a result of having some kind of characterological assessment,” he says.

“We need to understand where they fit already, how we can engage with them intelligently and talk to them about the different sorts of scenarios they might encounter in your workplace. That way the interview does become appreciably more predictive of success in any given role.”

Retaining high-calibre hires and creating flexible work environments that boost productivity are high priorities for the professional services industry.

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