Bridging the Data Science Talent Gap Across Businesses

by May 20, 2020

Data Science

In today’s digital world, data is the new currency for every single business across diverse industries. It is expected that the volume of data is going to reach 44 trillion gigabytes by the end of 2020, and the number will increase to 463 exabytes each day globally by 2025. However, processing this voluminous amount of data requires expertise. That is why most companies turn to data science that leverages scientific methods, processes, algorithms, and systems to pull out information from data and take major decisions for an organization.

As businesses cannot derive full value from data without data centric technologies, there is a deficiency of data scientists around the globe. Every company these days is actively looking for data science talent, but the supply of people with relevant skills is lesser than demand. Based on our analysis, the global skills gap of big data is forecast to reach 58% in 2020. This skills gap, as well as longer hiring times, could cause project delays and higher costs, impeding organizations’ data analytics efforts.

Thus, investing in data-experts and technology can add value to businesses, alleviating the talent bottleneck and enabling companies to drive efficiency. Data scientists explore historicals, make comparisons to competition, assess the market, and eventually make recommendations of when and where a company’s product or services will sell best. This can assist business leaders in understanding how their product helps others and, as needed, question existing business processes.


Democratizing Data Science

Acquiring data is one of the most critical jobs for modern businesses, and that data will be useless unless organizations leverage it to accomplish actionable insights. Most organizations overlook data science knowledge to a small number of employees that can make them jeopardize. Thus, to conquer the challenge and stimulate enterprise data analytics efforts, companies must democratize data science that would empower citizen data scientists in order to solve complex data science problems.

While enterprises seek to build data science capabilities, they need to consider prolonged approaches. In this way, the democratization of data science would be feasible. But before doing so, businesses need to address certain challenges as a lot of technological advancements in this space have happened recently and continue to occur. They should explore hybrid-staffing models for their data science projects, with bringing together a set of talents such as data engineers, statisticians, and business analysts with germane data science automation tools, instead of relying on and overburdening the data scientists with all the analytics work.

Moreover, embarking on data science requires a clear understanding of how the initiative will be introduced, managed, and scaled in terms of team structures. Sometimes, acquiring data scientists is not only an option, rather businesses must leverage in-house talents. With the right staff, keeping everyone informed and up-to-date can also be effective to perform data problems. Therefore, a specialized team can help address complex data science tasks, driving valuable decision making.