Analytics and gig economy: Improving access and performance
Recruiters, traditionally have had a tough time finding the right talent to support organizational demands. With technology becoming an important part of how companies operate today, their role, in turn, has become even more difficult. Core technical skills often are fast replaced with newer ones. Studies also project that in many sectors like IT, Banking and other tech facing sectors, the proficiency of technical skills is no longer the only consideration when it comes to hiring, with soft skills such as creativity and adaptability becoming more important.
Talent today has become a core concern of companies. With ‘business agility’ becoming an important consideration among many, it comes as a little surprise that companies are actively looking for ways to make their workforce more responsive to external changes. This makes the job of recruiters and HR professionals even more important. And difficult. Finding people with the right skill sets and mentality to contribute productively while be prepared to face future uncertainty becomes a daunting task.
It is in such circumstances that gig work becomes highly relevant for companies. Companies, both old traditional business houses to startups, all have begun experimenting with the phenomena of gig-based working—encapsulated aptly as the gig economy—and often with varying degrees.
And as seen, companies can tap into the gig economy in multiple ways today. But hiring qualified individuals remains a major concern in front of companies. Although labor costs attached to hiring gig workers on a contractual basis come down significantly, recruiters remain wary of depending on the option heavily to address their talent needs due to many inefficiencies of traditional hiring mechanisms. In such cases, Big data and predictive analytics become an important ally of HR professionals.
Big data analytics helps the function make more data-driven decisions when it comes to hiring gig workers. Recruiters can generate deeper insights on the suitability of gig workers and freelancers for a specific project given their past performance records. Although there has been growth in the demand for gig workers in India, there remain challenges in finding the right talent. And driving productivity with a similar level of accountability. But that hasn’t deterred companies from increasing the use of gig workers over the last five years according to an EY report. But, as the online gig space becomes more crowded, both employers and gig workers confide that finding a good 'match' can take a lot of time and effort.
Big data and predictive analytics can greatly improve the company’s ability to find the people within the gig economy. Analytics can assist with the talent search by gauging the company’s demands (including the unvoiced ones) and when paired with AI and ML algorithms can suggest them with the optimal match for the job opening. Professional data of gig workers like information from social media profiles, educational credentials, a complete resume, etc can be analyzed to assist such systems with the selection process.
While companies can depend on the gig economy to address their internal skills gap, by utilizing big data analytics they can greatly increase the accuracy of their hires. But such systems are still at a very nascent stage if implementations. A large number of gig workers today find employment through digital spaces, many don’t come necessarily come with the required assessments. The gig economy too is in its infancy in a country like India, with employee data mostly decentralized or missing. But as the ecosystem matures, the scope of big data application is only going to rise in case of hiring the gig worker. The use of big data analytics has to further fine-tuned to address the nature of work that companies aim to hire gig workers for and cater to a varied business need.
While some companies tap into the gig economy solely on a project to project basis, while startups like Uber, Amazon, etc have large portions of their workforce stemming from the gig economy. In cases of companies like Uber, their ‘employability’ of gig or contractual workers becomes even more blurry. Such ride-hailing services like Uber and Ola serves more as a platform for gig workers to directly reach out to the buyers of their expertise—in this case, their ability and availability to carry individuals from one point to the other—thus have major portion of people working for them without necessarily ‘employing’ them in the technical sense.
Such forms of employment are slowly becoming a major part of labour markets across the globe. Estimates show that one-third of the working population falls under gig capacity in America. Currently, the average organization in the US is made up of 18 percent of the contract workforce. Reports project this number increase not only for the US but also for countries like India as the digital ecosystem matures and more skilled professionals willingly shift to a more flexible work structure.