How Big Data Is Changing Recruitment Forever

by   |   October 25, 2016 5:30 am   |   3 Comments

Bernard Marr

Bernard Marr

In business, people are usually both the most important asset and biggest liability. Talented people are often what makes a business stand out from the competition while an under-motivated or mismanaged workforce will almost certainly lead to poor overall performance.

It is therefore unsurprising that a wide range of applications of big data and analytics technology has emerged, squarely aimed at the field of people management and human resources HR. As with most big data implementations, the key objective is to create a more data-driven and fact-based approach to people management.

I have covered the use of analytics and big data in people management and recruitment extensively, but think it’s worth it to take another fresh look at this fast-emerging field. I recently had a chat with JP Rabbath, chief product officer for Wanted Technologies, which provides a big data-based recruitment analytics service, who told me “If you think about how HR used to deal with recruiting, mostly it was just “post [a job vacancy ad] and pray”.

The Wanted Analytics service offers employers to take a more analytical approach, by scanning thousands of job ads posted online, extracting unstructured data from the job descriptions as well as structured data such as location and salary.

This data is then presented in a web-based dashboard giving insights into recruiting patterns and talent pools around the world. Citibank, one of Wanted’s largest clients (which also include Starbucks, IBM, and Microsoft), used the data to determine that huge efficiencies could be made by relocating operational facilities in several areas of business away from their New York city home turf, to better access local talent.

“On the HR side, there often hasn’t been a ‘fact based’ conversation – so that is what we are trying to help them with,” says Rabbath.

One obstacle to such data-driven approaches is the fact that there are different rules in place around the world regarding information that employers are allowed to collect in relation to job applicants. In some EU states, for example, this is strictly limited to information provided by the applicant or with the applicant’s consent.

Some recently developed analytical recruitment solutions avoid this by relying entirely on data collected from the applicants – such as corporate headhunting specialists Korn Ferry’s KF4D tool. This involves candidates undergoing a 45-minute self-assessment exercise online, data from which is analyzed and applied to assessing their suitability for c-suite positions with Korn Ferry’s clients.

Dana Landis, vice president of global talent assessment and analytics at Korn Ferry, said “When you’re talking about big data you’re talking assessing millions of people all over the world, so you need self-assessment. We’ve designed our tools to take out a lot of the problematic aspects of that – instead of being able to rate yourself high on all the good things and low on all the things that sound bad, you’re forced to make really difficult decisions based on ranking and prioritizing your skills.”

Moving their assessment process to an online, self-assessment model has greatly increased the volume of candidates that Korn Ferry has been able to assess. This further increases the size of the dataset used to measure candidates’ suitability. By comparing their individual profiles against amalgamated profile data from people who have proven themselves successful in similar job roles, a more accurate picture of the skills a person will need to succeed in a particular role emerges.

In addition to skills, the program also assesses individual candidates’ “fit” for the corporate culture of the employer. After all, while incorrectly skilled or unsuitable appointments at junior- or mid-level positions can be problematic, appointing the wrong person in a job title containing the word “chief” will almost invariably be a disastrous move!

A number of solution providers are starting to bring AI and machine learning into the recruitment process. It is not too difficult to imagine that in the future artificial intelligence tools will simply collect all the information they need from all the data that is now available about candidates to create a strong shortlist of candidates. And even those could be interviewed by AI algorithms to determine the perfect match.

I feel that the application of big data in recruitment is one of the most exciting areas of development so stay tuned for more on this topic soon.


Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. In addition, he is a member of the Data Informed Board of Advisers. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher.


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  1. Courtney Benson
    Posted October 27, 2016 at 10:50 pm | Permalink

    We need to be very careful when it comes to having algorithms decide who gets what job. One has to ask what is the definition of success, what data is being used for the neural net to train on? In the U.S. companies can’t discriminate but we have no way of knowing if anyone is auditing these new algorithms for biases. Biases can be built in like race, gender. These types of algorithms might be profiling and a candidate has no way of knowing. Your credit history, social media behavior, background, where you live, motor vehicle records and any testing that an employer requests you do is all gathered and evaluated before you ever get an interview. In addition It’s important to know who owns this data if your not hired and is it being shared with third parties or Is it sold to third parties, data brokers? I think we need to make sure that these systems are fair and objective.

  2. Posted October 31, 2016 at 10:33 am | Permalink

    I love that this conversation is happening.

    The 2 biggest issues I have are:

    1. Vendors that use ONLY big data to somehow tell if a candidate will be a good fit for your company are doing false data science. Each prediction needs to be tied to a performance outcome. Unless folks like Wanted are asking for the actual outcome data from the client (and they typically are not) then this is not predictive.

    Bernard using your sports analytics metaphor it would be like a sports team just using all of the sports data in the world to predict a great pitcher. The team wants a great pitcher for their team – not a great pitcher for a generic team.

    2. Massive bias is taking place today in the hiring process. It scares my how much is taking place and there is no way to make employees accountable. Mystery factors, undocumented decisions. It has to stop.

    Predictive analytics done right in the talent acquisition process is accountable and gets rid of a lot of mystery factors.

  3. Linda
    Posted October 31, 2016 at 10:41 am | Permalink

    Courtney, I think you have a clear handle of what we need. Thanks for sharing.

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