In the recent years, Data Science has created a hyperbole around itself while the jobs related to it have become equally pompous. In 2012, Harvard Business Review has named Data Scientist as the “sexiest job title of the 21st century” and since then the aura has not faded. Data is driving all industries today; from the government to corporations to healthcare. While data has emerged as a valued commodity for businesses, it has also become a mammoth task to manage and analyze those. In order to fulfill this task, the industry has created the role of “data scientist,” a person who uses his abstract thinking and technical skills to look at data in novel and meaningful ways. Data Scientists seem to appear like a new species of techie who are in high demand, not only in the Silicon Valley but in companies across the globe.
Why is Data Science in Demand
Data science is neither a regular IT job nor a niche domain for a statistician. Rather, it is a multidisciplinary blend of technology, data inference and algorithm development in order to solve analytically complex problems. Data science is all about uncovering information from data. Digging in at a granular level to mine and understand trends, complex behaviors and inferences. The field is about surfacing hidden insight that can help businesses make impactful decisions. Considering the fact that there has been a massive explosion in both data generation and retention by companies, as well as by individuals like you and me in the last decade, data scientists are ought to be the superheroes in the tech world today.
The Skills Gap
The gap between data science supply and demand is substantial. In fact, the gap is expected to get bigger in the coming years. In 2015, a study by McKinsey has predicted that by 2018, there will be 50 to 60 percent gap between supply and demand of data scientists in the U.S. Meanwhile, a Gartner study, that surveyed nearly 1000 IT professionals, has revealed that 59% of the professionals believed that their IT organizations were not ready for digital business in the next two years citing lack of talent as the one of the main obstacles to achieving digital transformation.
How to Bridge the Gap
Given the vast stores of data accumulated by the businesses, the demand for data scientists is not going to decrease in the near future. There could possibly be a few steps that can help to fill the vacancies behind the hottest job title of the century. Let’s take a look at them:
- Upskilling existing talent- One potential answer to this skill shortage would be to develop talent in-house, with proper training and right supervision.
- Team building- Team-building is an important tactic in tackling the skills gap. Instead of looking for a single all-rounder who can do everything, it is much feasible to build a team of people with required skill-set.
- Educating– Considering the bigger picture, it is important for the universities and other higher education institutes to produce more data science-focused graduates and post-graduates.
EMC and Pivotal have taken a serious step ahead to bridge the skills gap in data sciences and have come up with certified courses to train experienced professionals working in IT or Analytics.
SpringPeople, being the leader in LVC and a master partner of both EMC and Pivotal, is the best marketplace for data science related training courses in the country.