The success of any organization is directly dependent on the quality of the hire and the association it forges with the employees. After all, employees are the biggest investment for any company and a bad hiring decision can lead to a snowball effect, detrimental to the overall employee return on investment. With so much at stake, talent acquisition becomes a key task in the HR process, with its capacity to tap and evaluate the skills and experience of active and passive candidates, driving the recruitment process.
The hiring decision is based on a very heavy data-driven process, which entails rummaging through millions of profiles, engaging with the shortlisted candidates for an initial round of telephonic interview,s and ascertaining the best fit candidate for the role. The issue with these tasks is that they follow the same pattern of hiring making them more compliance-oriented rather than result-oriented. This is where the capabilities of Machine Learning really shine as it provides ‘perspective’ to the data. It runs through the statistical data and powered by inbuilt algorithms it learns patterns and hones its analytical and cognitive capabilities. Algorithms are insatiable when it comes to data, therefore the more measurable variables you provide the better it will become in making predictions. The prediction of an important performance metric by an algorithm is based on human intuition to decide which characteristics to base the computation on. The following are a few of the many ways machine learning is changing the standard recruitment landscape:
The machine learning capabilities depend upon the amount of data fed into it to decipher regular patterns: more the data, more patterns it will come up with for advanced analysis. Although the advancement in analysis and pattern recognition is unparalleled, the technology is still in its nascent phase, making human involvement still quite necessary.