Today, more and more organizations are leveraging big data and its potential as they are generating a voluminous amount of data every day. However, this is increasing the value of data science and the demand of data scientists that use scientific methods, processes, algorithms and systems to mine insights from both structured and unstructured data. There is no doubt data is an essential asset for every business, enabling executives and leaders to make effective decisions based on facts to bolster their productivity and profitability.
Considering a market report, the global market of big data is predicted to grow at a CAGR of 0.6 percent from US$138.9 billion in 2020 to US$229.4 billion by 2025. It has also been projected that the data science platform market size will grow at a CAGR of around 30 percent from US$37.9 billion in 2019 to US$140.9 billion by 2024.
Data Science plays a vital role in any business, supporting businesses to deliver germane products, assisting in minimizing peril and fraud in business, and helping business leaders to understand and manage their data effectively.
Already, most companies that use big data and analytics technologies into their business processes are benefiting from the growing democratization of data. With a large set of data that have the potential to drive business transformation, organizations, public and private, are now turning to data science knowledge and programming experts like Python developers. Because data scientists are able to make sense out of the data companies are gleaning, using specialized platforms in data management and analysis.
Heightened Demand of Data Science
According to IBM predictions, the demand of data scientists will surge by 28 percent in 2020, which will give the spike to get the best talent. The huge demand for data science skills will also cause a disruption in the workforce that will have to be addressed by the education sector.
The increasing adoption of robotic process automation (RPA) is further giving rise to the demand of data science professionals. Since a wide range of industries are leveraging this technology, infusing intelligence in automation using data science and analytics will bring the era of hyper-automation that seems to be expanding its territory than ever before. Thus, this integration can assist businesses in analyzing risks and controlling mechanisms associated with hyper-automation.
Many data scientists have advanced and training in statistics, math, and computer science. Their experience is a vast horizon extending to data visualization, data extraction, and information management. While the complete data science process is driven by data that cannot be considered anonymous at all, the cybersecurity industry is also investing heavily in data science techniques to ward off attacks.
By embracing the application of data analytics and machine learning tools, an organization can conduct a thorough analysis of the collection of information, and professionals can evaluate data closely to unlock trends, patterns and actionable intelligence. Also, as a data breach can harm extremely valuable data and information that can be really damaging to an organization, involving data science here can be effective in assessing the history of cyberattacks and creating algorithms to spot the most frequently targeted chunks of data. With the rising capabilities of data science, it is clear that the demand of data scientists will become a vital job in years to come. And integrating them into businesses will drive innovation at large.