DATA SCIENCE PLATFORM MARKET ANALYSIS

Data Science Platform Market Size and Trends

Global data science platform market is estimated to be valued at US$ 11.03 Bn in 2024 and is expected to reach US$ 45.86 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 22.6% from 2024 to 2031.

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Global data science platform market growth is driven by widespread adoption of data-driven decision making across various industries such as BFSI (Banking, Financial Services, and Insurance), Healthcare, Retail, Telecommunications, and Others. Increasing focus on generating insights from structured and unstructured data to gain competitive advantage can boost demand for data science platforms. Proliferation of cloud-based platforms and availability of open-source tools can boost adoption of these platforms by small and medium enterprises. Integration of advanced technologies including AI, machine learning and deep learning can boost the attractiveness of data science platforms. However, shortage of data science professionals may hamper the market growth.

Increasing Demand for Data-Driven Decision-Making across Industries

As businesses across industries are moving towards digital transformation, data has become important assets for any organization. With data now being recognized as the new oil that fuels the digital economy, companies are investing heavily in developing data-driven cultures and processes. Advanced analytics and machine learning techniques are enabling executives to gain deeper insights from data, which helps them make more informed decisions. Data science platforms provide businesses with the tools and infrastructure to collect, store, process and analyze vast amounts of data from various sources within the organization as well as external sources. This aids in identifying patterns, trends and correlations that would otherwise be impossible to detect.

Across all these sectors, the need to derive actionable insights from data on a real-time basis can boost demand for advanced analytics tools, data scientists and integrated data science platforms. Businesses want platforms that can automate various stages of data analytics like data preparation, modeling, deployment as well as support collaborative work between data scientists, business analysts and other teams. This makes data science platforms a crucial investment area as businesses realize the true transformational potential of data-driven decision making.

For instance, in November 2023, IBM, a global leader in hybrid cloud and AI solutions, collaborated with Amazon Web Services (AWS), a premier cloud services provider, to announce the general availability of Amazon Relational Database Service (Amazon RDS) for Db2. This fully managed cloud offering is designed to simplify data management for artificial intelligence (AI) workloads across hybrid cloud environments. Users will benefit from a comprehensive suite of integrated data and AI capabilities provided by IBM on AWS, enabling them to efficiently manage data and scale their AI workloads effectively.

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Growing Volume of Data from Various Sources

With proliferation of smartphones, sensors, IoT devices and digital services, huge amount of data is now being generated on an unprecedented scale from a wide variety of sources. Business operations are becoming highly digital as organizations adopt technologies like cloud, mobile apps and digital processes. This continuous digitization of human activity and enterprise functions leads to growth in enterprise data over the past decade across structured, unstructured and multi-structured formats.

Rise of new data types like graphs, geospatial, genomic and other specialized forms of data that are being collected. Problems around data storage, processing and extracting useful insights are further compounded by the different velocities at which data streams in - from real-time streams to batch processing of archived data. The varied and massive volumes of data pose tremendous challenges for tech infrastructure and human capabilities when it comes to data management, analytics and science. Traditional tools are unable to handle these sizes, varieties and velocities of modern data ecosystems.

This has accelerated the need amongst organizations to invest in unified, scalable and collaborative data science platforms. Platforms that can ingest and process high volumes of data from multiple internal and external sources, and also support both streaming and historical analysis use cases. These provide the ability to glean knowledge from massive and diverse datasets much more effectively than isolated point tools. This boosts adoption of data science platforms as the primary infrastructure for data management as well as advanced, enterprise-grade data analytics at scale.