Examining the size, trends, and potential of the data science platform market in 2024

Data Science is experiencing a notable upsurge in interest, as evidenced by the growing Data Science market size. The demand for Data Science platforms, which help companies glean insightful information from massive volumes of data, is fueling the growth. These platforms are developing quickly, and new Data Science trends, such as AI-driven analytics and automated machine learning, are appearing regularly. The Data Science Market Forecast anticipates a sustained upward trend going forward, driven by the increasing demand across a variety of industries for data-driven decision-making in business. There is a notable upward trend in the data science platform market. With a projected Compound Annual Growth Rate (CAGR) of 30.0%, the market, which was valued at USD 37.9 billion in 2019, is anticipated to reach USD 140.9 billion by 2024.

Market Drivers

One of the main factors propelling the market for data science platforms is the astounding rise of big data. Because of the growth of social media, the Internet of Things, and multimedia, which have generated an overwhelming flow of data in both organized and unstructured formats, enterprises are collecting an ever-increasing amount of data. For example, in just the last two years alone, about 90% of the world's data has been created.

Massive volumes of structured and unstructured data are also being produced as a result of the quick advancement of technology, the falling average selling price of smart gadgets, and the growing use of cloud-based infrastructure. A typical relational database does not include more than 80% of the data that organizations collect. Rather, it is imprisoned in unstructured papers, pictures, machine logs, social media posts, and other sources.

Market Opportunities

Organizations can obtain new insights thanks to the enormous growth of data, which has raised the need for new approaches and procedures. Consequently, this is a major factor propelling the market for data science platforms.

The phases of the data science production process that are especially covered by data science platforms are data preparation, model creation, DevOps, and business delivery. These platforms rely on tools that evaluate and improve their effects on business, including operationalized model management, transparent data access, uniform metadata, robust enterprise governance, automated machine learning, and model building and maintenance.

Market Challenges

Though the market is growing, there are still certain obstacles to overcome. The ambiguity of business challenges is one of the main obstacles. Businesses should research the business issues they plan to use the data science platform to solve. It is less successful to choose the mechanical method of finding datasets and analyzing data before having a good understanding of the business problem that needs to be solved.

In the upcoming years, the data science platform market is expected to increase significantly. This expansion is being driven by several factors, including the emergence of new technologies, the volume of data that is being collected, and the growing need for deep insights from large amounts of data to obtain a competitive advantage. To fully realize the potential of data science platforms, however, businesses must confront the difficulties involved in their implementation.