The Significant Analysis of Data Scientist Evolution in Years Ahead

The digital revolution in technology has changed the whole landscape of the data science industry. The demand for big data, AI and other cutting-edge technologies are surging in the market. With the growth of enormous datasets, not only data science but data scientists are also expected to evolve with time to meet the industry demand.

What Could Be the Possible Evolutions in the Role of Data Scientist?

Owing to the prevailing massive talent gap in data science, data scientists have to try enough to master both technical and business understanding of their projects. Currently, data scientists have found themselves stuck in a situation where they have to manage a broad range of data-specific work due to the project demand which develops a lot of stress in them.

Therefore, companies are trying to include more skillsets in their ecosystem including data science leaders, data translators, and domain-specialist. This simply implies that there will be a demand for data scientists with skill-specific roles.

Previously, the roles of data scientist used to range from data capturing task to data insights. But the scenario now is different and will continue to evolve with time. Today, the roles of these professionals are much diversified than before. Operating and managing big data requires a lot of expertise with different skill sets. As more and more companies are realizing the need for a shift, the demand for different data science skills is expected to soar up over the next decade. And, there will be more specialized work available for data scientists.

The Upsurge in Data-Driven Strategies in Organisations

Today, data influences every aspect of an organization from marketing to customer relations, from identifying threats and weaknesses to form finance strategy. An incapacitated data strategy can nearly kill a business.

Therefore, more and more organizations are beginning to realize the value of data science and are more focused on developing data-driven strategies with high recruitment of data science talents in the years ahead.

Every level of employees should participate in understanding the type of data significant for their respective department which would consequently help in building better strategies.

AI-ML To Wing Up the Ambitions of Data Scientist

As the debate of human workforce vs digital workforce is heated up in tech-industry, professionals are fearing of the consequence of AI/ML adoption in organizations. People are worried that technologies will outmode the work of data scientists in a few years. There are certain companies that believe that automation is way behind matching up the human intuitions. These technologies are more likely to assist data scientist in the data processing.

A huge volume of data is being generated daily and it has been predicted by 2025, about 463 exabytes of data will be generated globally. In reality, it is not data scientist's cup of tea to manage this huge sum of data alone. This is where AI will be of significant help.

However, AI will not threaten their job as a number of organizations believe that AI would eventually increase the headcount in their ecosystem. Rather the technology would tend to eliminate 80 percent of the workload of data scientist occupied with repetitive and mundane tasks. Subsequently, they will get more time to focus on innovation and efficiency.

As the current scenario predicts the data scientist will become a vital investment over the next decade and the variety of jobs lying under this category will become prominent roles in a business environment. Additionally, the prevailing insights suggest that data science provides a highly valuable edge to an organization to thrive in the data-driven market.