“Big data” and complex algorithms are increasingly taking decisions out of the hands of individual interviewers a trend that is seen to affect job seekers and recruiters alike.

A number of businesses are now using data sets of past behaviours to predict everything from salesmanship to loyalty of clients, employees and company growth levels.

Recruiters are now looking at the time period job seekers spend at former jobs and what time frame they have planned for the new job.

Data in regard to social behave, networks and connection has also been under a microscope for purposes of in depth understanding of a person’s lifestyle.

This kind data collection helps open doors for people who would have never gotten to interview based on a CV.

Organisations have long held large amounts of data. From financial accounts to staff time sheets, the movement from paper to computer made it easier to understand and analyse. As computing power increased exponentially, so did data storage. The floppy disk of the 1990s could store barely more than one megabyte of data; today a 16 gigabyte USB flash drive are cost effective. It is simple, then, to say that recruiters are to arrive at a point where crunching data could replace the human touch of job interviews.

Research by NewVantage Partners, the technology consultants, found that 85 per cent of Fortune 1000 executives in 2013 had a big data initiative planned or in progress, with almost half using big data operationally.

“From an HR and recruitment perspective, big data enables you to analyse volumes of data that in the past were hard to access and understand,” explains David Woodward, chief product and innovation officer at Ceridian UK.

This includes “applying the data you hold about your employees and how they’ve performed, to see the causal links between the characteristics of the hire that you took in versus those that stayed with you and became successful employees. Drawing those links can better inform your decisions in the hiring process.”

Data sets need not rely on internal data, however. “Social media data now gives us the ability to ‘listen’ to the business,” says Zahir Ladhani, vice-president at IBM Smarter Workforce. “You can look at what customers are saying about your business, what employees are saying, and what you yourself are saying – cull all that data together and you can understand the impact.

“Most recruitment organisations now use social media and job-site data,” says Mr Ladhani. “We looked at an organisation which had very specialised, very hard to find skill sets. When we analysed the data of the top performers in that job family, we found out that they all hung out at a very unique, niche social media site. Once we tapped into that database, boom!”

As more companies start to analyse their employee data to make hiring decisions, could recruitment finally become more of a science than an art?

“The potential is clearly much greater now than ever before to crunch very large volumes of data and draw conclusions from that which can make better decisions,” says Mr Woodward. “The methods and computing power being used in weather forecasting 10 years ago are now available to us all . . . who knows where this may go.”

Patricia Kahill

is a Social Media, Content Creator and Marketer at Kahill Insights.
A Development Practitioner who has no self talent but is driven by curiosity and passion; in a nutshell she is a Multipotentialite. She believes in God the Father, the Son and the Holy Spirit which makes her a Christian.

2 thoughts on “Forget CVs, Big Data Is Taking Over Recruiting Decisions”

  1. this is very true indeed, with the big volumes of data, diff organizations have, data scientist is the one of the big next career opportunity to take on. Not even the diff schools that are trying to teach data analyst really tackle this new challenge of big data. am an example of school of statistics.
    Exploring on your own isn’t easy too and teaching yourself the new trending data analysis tools like R and python is another mountain to climb, but we are getting there.
    even schools of data scientists, mathematicians and statisticians should start looking into this direction .
    thanks

Leave a Reply

Your email address will not be published. Required fields are marked *