It goes without saying how curious people were about data science a few years ago. The label of being named the Most Wanted Job of the Century has done all wonders. Companies making every effort to unlock transformative growth through artificial intelligence, machine learning, and data analytics have been a boon to those ready to make their mark in data science. A crazy demand for data science as a career around the world was reaching heights. On the principle of supply and demand, the high demand eventually paved the way for the expectation that wages would push through the roof.
If you believe the reports and figures on data science jobs, then the situation in India looked like this: In 2020 the number of data science jobs was expected to be around 1.5 lakh new vacancies. Data science professionals with 3-10 years of experience earned up to 6.5 million salaries. We now need a clear picture here. In line with most technology trends, the hype and reality of what data science can do are mismatched. Let’s find out.
Data science – How is the reality different from the expectations?
The pandemic has had a profound impact on almost everything that can be imagined. The tech world has also changed drastically as a result of the pandemic. Companies had started to invest heavily in advanced analytics and next-generation technologies such as IoT, blockchain and quantum computing in the hope of better results. In fact, not all companies have been able to get the most out of their investments. The reality: For most companies, their investments in artificial intelligence and machine learning have not yielded the promised results. What’s worse, the discrepancy between demand and supply has always been a problem area for data science professionals. Who is to be blamed for this?
Well, the data scientists are not to blame here, they have done their part, they have trained, so what’s the problem? From the talent shortage to the talent gap in data science, AI and machine learning technology, data scientists are trained to use it and the business problems they are trying to solve have all prevented their promised value: the past few years ago, almost every company has joined the AI race, they create their own data, scientific teams. What they overlooked in this process is due diligence before hiring data science professionals or developing the practice in-house. Having a clearly formulated strategy and vision for how their AI investments will play out in the long run is what most organizations have not paid attention to. In the long run, when these organizations realize that great data science teams have no tangible value from data science teams – data science professionals are the first to be fired.
Although trained data science professionals have extensive knowledge of computer science, math, and statistical modeling, they have been found to lack business applications and expertise. It is high time that we acknowledge the reality – overemphasis on technical skills and under-emphasis of commercial and social skills has not only led to a communication gap, but also to a gap in understanding. To be clear: data science will remain and the demand for data science professionals is certain. ‘Perfecting yourself and putting your AI investments in right the place is the need of the hour.
