Key talent metrics to segregate good talent from average

In an ideal scenario, every employer wants to hire an ‘A’ candidate. With success in today’s job market hinged on having the right talent on board, organizations cannot afford to hire average or low performers. Concerted efforts from the Government, industry and academia alike are underway in building a quality and skilled talent pool within the country. However, without effective benchmarks or parameters to assess the millions of students entering the workforce every year, identifying the right talent can never be easy.

 

With the nature of jobs changing dramatically over the years, success is driven by but no longer restricted to a candidate’s proficiency in his/her domain knowledge. The on-job performance of a candidate is also closely related to key skills like language skills, cognitive and aptitude skills as well as functional skills. However, organizations continue to rely on traditional assessment methods, which cannot effectively measure these skills, and segregating good talent or high performers from the average becomes a challenge. This situation affects job seekers as well as recruiters adversely by being a misfit for both.

Aspiring Minds’ National Employability Report which studies the employability of engineers and graduates in the country shows that a majority of students lack basic language skills. Surprisingly, more than 50% of the engineers are rejected for jobs because they are not soft skill trainable in a short period of time. With good communication as one of the key skills that recruiters look for, irrespective of the role, an effective assessment mechanism to test these skills is essential in filtering the wrong set of candidates.

Another major hurdle which most organizations face today is finding good tech talent. The lack of objective signals/credentials to identify good talent can have a significant impact on the organization’s success. With the start-up boom and majority of businesses going online, demand for tech talent has also seen a sudden surge. In this scenario, for jobs like data scientists and software engineers which require a diverse skill set, organisations cannot afford a bad hire. With the progress in data science algorithms and machine learning, automated scientific assessments like AUTOMATA today can effectively identify high performers. The tool allows for a scientific measurement of skills in an efficient, scalable and cost effective manner. In a recent case, a group of 90,000 programmers took this automated programming assessment. The tool helped identify 20% candidates who could write a maintainable and efficient code which would have otherwise been missed through traditional test cases.

The need for identifying good talent is critical for professionals in customer facing roles like sales, operations, hospitality etc. which requires an effective assessment of soft skills, personality and situational handling skills. In this scenario, simulation based Situational Judgement tests can effectively assess traits which are essential on the job. The assessment simulates job scenarios which can measure the candidate’s correct behaviour and response in each situation thereby assessing traits which current tests are unable to do.

Both job seekers and recruiters can benefit from a job market wherein biases like college degrees, GPAs, tier of city etc. do not serve as indicators for potential. The National Employability Report over the years has shown that candidates from Tier 2 and 3 cities are equally employable but end up not being a part of the employable pool due to the biases that exist in the recruitment/shortlisting process. In such a scenario, assessments that can objectively prove the skills/capabilities of candidates can be invaluable to the job market.