Data Science Platform Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023 - 2028)

Data Science Platform Market Analysis

The global data science platform market (hereafter, referred to as the market studied) was valued at USD 31.05 billion in 2020, and it is expected to reach USD 230.80 billion by 2026, registering a CAGR of 39.7 % during the forecast period, 2021-2026. The data science platform comprises the software hub around which all the types of data science work takes place, including integrating and exploring data from various sources, coding, and building models. It also leverages the data, deploys models into production, and serves up results through model-powered applications or reports. It allows data scientists within a single environment to discover actionable insights from data, plan a strategy, and communicate the collected ideas throughout an enterprise.

• IT managers who support a large team of data scientists in an enterprise setting are tasked with data governance and providing the infrastructure and tools that data scientists need. The proliferation of data science tools and applications available provides opportunities along with challenges.
• Data science encompasses many job titles across different industries and organizations, starting from analytics officer, to actuary, to research scientist. But regardless of all title posts, all the roles are united in unlocking strategic insights from data, for which business demand is stronger than ever before. IBM estimated that the need for data scientists would soar 28% by 2020. The increased usage of large amounts of structured and unstructured data in the various end-user industries is boosting big data adoption. For instance, according to Seagate Technology PLC, the global volume of data is expected to increase to 47 zettabytes and 163 zettabytes in 2020 and 2025, respectively, from 12 zettabytes in 2015.
• Specifically, a data science platform supports all four stages of the data science production line: data preparation, model development, DevOps, and business delivery. It relies on transparent data access, consistent metadata, strong enterprise governance, automated machine learning and model building, operationalized model management, and tools that measure and improve its impact on business.
• Moreover, the active use of data science and machine learning is boosting the telecommunication industry. The telecom companies operate with full data flow as they majorly function with vast communication networks and infrastructures. Analyzing and processing this data with the help of data science platforms is one of the most practical solutions.
• Further, the cloud is catering to market adoption with the data science platform integration, where players are significantly developing the cloud integration platform. Cloud computing offers the ability to access limitless processing power. Cloud vendors, such as Amazon Web Services, offer their servers up to 96 virtual CPU cores and around 768 GB of RAM. These servers can be set up in the autoscaling group, where more than hundreds of them can be launched or stopped without much delay. Beyond just compute, cloud computing companies are offering full-fledged platforms for Data Analytics. For instance, Google Cloud offers a platform called BigQuery, a serverless and scalable data warehouse giving Data Scientists the ability to store and analyze petabytes of data, all in a single platform.
• Furthermore, the COVID-19 crisis continues to rise with the coming period. During this phase, cloud computing has emerged as an efficient model that helps facilitate some of the most significant transformations businesses are undergoing. Globally, as much as 50 % of organizations (both SMBs and enterprises combined) plan to increase their cloud usage in the light of the coronavirus crisis, according to a cloud survey conducted by Flexera. The numbers in India are much more promising, with key sectors like education, healthcare, entertainment, and gaming consistently moving to the cloud to ensure business continuity and resilience. This caters to the high usage of the data science platform.

Data Science Platform Industry Segments

The data science platform puts the entire data modeling process in the hands of data science teams so they can focus on deriving insights from data and communicating them to key stakeholders in the business. The market studied comprises of applications, such as marketing, sales. And others, which are mostly deployed on-premises and cloud-based with the platform.

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Data Science Platform Market Trends

This section covers the major market trends shaping the Data Science Platform Market according to our research experts:

Healthcare Sector to Dominate the Market over the Forecast Period

• Health care is a segment where individual pieces of data provide life-or-death importance, and many organizations fail to aggregate data adequately to gain insights into broader care processes. Drawing conclusions and making decisions based on data and efficiently use medical knowledge to improve safety and quality.
• The data science platform provides various medical research communities that can broadly share, integrate, and analyze historical, patient-level data from academic and industry phase III clinical trials. Such a rich data set is a part of data science and undoubtedly will help the pharmaceutical research and development segment.
• Further, players are offering new platforms that are cloud-agnostic and can be deployed as a single-tenant Platform on AWS, GCP, Azure, or Private Cloud. In June 2020, Aigenpulse introduced a new data intelligence platform designed to expedite drug discovery and development. Aigenpulse platform harnesses the latest artificial intelligence (AI) and machine learning tools to deliver advanced analytics to underpin scientific decision-making.
• Also, scientists can process hundreds of datasets simultaneously and at scale, making them free for higher-value tasks. The platform easily integrates with ELNs and LIMSs, in-house data lakes for sample/experiment meta-data, and public data sources, such as TRON, TCGA, and GTeX.
• Further, despite vast amounts of health data at hand, diagnostic failure rates are still relatively high. Employing this health data to the data science platform will increase the accuracy and efficiency of diagnostics. These advantages are achieved by using powerful machine learning algorithms to extract and analyze the biological samples from over 1,000 patients.

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