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Data science to transform an HR vertical from a loss to a profit centre

Data science powered with Machine learning and Artificial Intelligence is changing the HR landscape phenomenally

Having spoken to several Chief Human Resources Officers (CHROs) and Talent Acquisition Heads over last year or so, I learnt that at times, it is difficult for them to decipher what will a pack of algorithms be able to do for the recruitment industry. According to them, it’s doubtful that a function which is largely managed on the strength of humane capabilities for centuries will have strong reasons to churn numbers. But things are changing. Software as we know is made up of binary language and every spoken word or a visual data point can be understood, encoded & decoded using machine understandable language with simple zeros and ones.

Further, human mind has a very little attention span, it’s a serious problem and there are two ways to deal with it. One is simply the brains first reaction which comes from past experiences. But this can go wrong where hundreds of factors come into play. This is the very reason why election results go awry even when best of political experts with expert opinions go wrong. This is because it’s not possible for a human mind to fathom unseen influencers. Validating this, renowned & Nobel Prize author Daniel Kanhaan says in his book Thinking Fast & Slow, that algorithms are much better placed to recommend a decision than experts in most cases where past data can be studied.

The Human Resource function steps in right on the day a new company or department is established. Although HR may sound a bit inconsequential when it comes to drawing insights for the management, its sheer existence is laden with data. HR heads see data in the form of resume, interviews, performance review, email content, attendance sheet, client reviews, peer reviews, Initiatives, team play, attitude, etc. It is important for the HR head to evaluate these parameters to take informed decisions upon hiring employees as any wrong hire or incorrect placement, performance, skill assessment of the talent can lead to dire consequences.

Having set the context right, we can pick a debate if HR should be viewed as a cost center. And if it still is perceived that way, will data science come to its rescue?

People are hired into the company as HR has a mandate to fulfill a business requirement and mostly the business managers are chasing them to fulfil positions. HR resorts to all sourcing methods to find the right fit. It could be a referral, however it is the HR which performs the initial set of candidate filters. If the candidate manages to qualify through the funnel, then it is the business manager’s turn to evaluate them further. However, since businesses are usually short of numbers and only new hiring could come to add up to new numbers, HR or business people tend to overlook the finer aspects of selecting the right candidates’ and try to force-fit randomly selected candidates who match some criteria. This kills the very purpose of using multiple filters and the performance goes down tremendously. Now if the organisation does not derive value from the new hire, it is because of the wrong hire and the opportunity cost involved is huge. We can put some fortunate numbers to this like – a cost of bad hire is almost 4 times his CTC, and this could range between a few lacs to several crores. The very reason why HR is actually a profit center.

Now the big picture – Its all about data. At every level, as an employee spends time in the company, he leaves lots of data footprint. If you have systems in place to trace that data, well and good, otherwise it’s a missed opportunity. The very next point would be – how to harness the potential for this burgeoning data. Data science powered with Machine learning and Artificial Intelligence can read into data points, create and predict patterns to recommend a candidate for an interview, or a performance review or simply predict a possible attrition.

Let us be a little more specific here. For instance, you have over the course of doing business, data from many employees who have performed extremely well. You have their profile and several other metrics associated – like demographics, age group, gender, education, background & aspirations and much more. Using data science, one can create a unique persona of a person like this and that persona can then be matched with the incoming candidate profiles. This will result in significantly improving the quality and specifics of the hire. One can then be certain that the new hire is bound to perform in the given role. This ensures that all the parameters required in the profile are met and the performance is also almost certain as it is highly correlated and will help the organisation in achieving its business goals.

In today’s scenario, organizations use multiple social networks to source interested candidates. According to research done at CareerleticsTM, a recruitment intelligence platform, an average of 250 resumes get pulled for a single JD. Now that’s a huge volume for any Talent Acquirer (TA) to go through in a short time and select the best candidates. It is also possible that TAs will develop a blind spot while reading through resumes thus important missing points. Can this be solved using data science? The answer is yes. Various analytical tools can help shorten the time to hire by over 90%.

Data science is enroute to transform HR in the coming years. We have experienced this phenomena across industries and we clearly foresee how analytics can help HR to source the right candidate, hiring the best matched talent and managing them righteously. The time is ripe that organizations become aware of the potential of data analytics and plan their journey in a way that they make best use of this technology.  

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I am the Founder & CEO of iPredictt Data Labs, an advanced data analytics company that provides predictive modelling solutions using big data tools. Most recently, I headed Telecom Commerce Mobile Advertising business globally at Vserv. I have over 18 years of work experience in technology consumer marketing and a thorough understanding of consumer behavior and consumption patterns on mobile, mobile commerce, and mobile advertising. I am a Marketing-Technologist with expertise in Media advertising, Telecom Products, Mobile Content, Mobile Advertising and Data Science that includes big data, artificial intelligence, machine learning, unstructured text mining, image and sensory data. I am a Business Manager with keen interest in applied technology.