Chicago, Aug. 17, 2022 (GLOBE NEWSWIRE) -- Data Science Platform market size is projected to grow from USD 95.3 billion in 2021 to 322.9 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 27.7% during the forecast period, according to a new report by MarketsandMarkets™. The Data Science Platform industry is driven by Astonishing growth of big data, however, rising in adoption of cloud-based solutions, rising application of the data science platform in various industries and Growing need to extract in-depth insights from voluminous data to gain competitive advantage.
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310 - Tables
57 - Figures
349 - Pages
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According to Oracle, data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract value from data. Data scientists combine a range of skills, including statistics, computer science, and business knowledge, to analyze data collected from the web, smartphones, customers, sensors, and other sources.
Data science platform is the combination of statistical, mathematical, and programming techniques that use Machin Learning (ML) algorithms and advanced analytics to uncover insights from data and automate data processing activities, such as data discovery, data cleansing, data preparation, data analysis, and data visualization. It helps organizations in making data-driven decisions to enhance operational efficiency and improve customer experience.
Scope of the Report
| Report Metrics | Details |
| Market size available for years | 2016–2026 |
| Base year considered | 2021 |
| Forecast period | 2021–2026 |
| Forecast units | Value (USD Million) |
| Data Science Platform Market Size in 2020 | USD 4.89 billion |
| Revenue forecast in 2027 | USD 25.94 billion |
| Growth Rate | CAGR of 26.9% from 2020 to 2027 |
| Segments covered | Data Science Platform Market by Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical, and Region - Global Forecast to 2026. |
| Regions covered | North America, Europe, APAC, MEA, and Latin America |
| Companies covered | IBM(US), Google(US), Microsoft(US), SAS(US), AWS(US), MathWorks (US), Cloudera (US), Teradata (US), TIBCO (US), Alteryx (US), RapidMiner (US), Databricks (US), Snowflake (US), H2O.ai (US), Altair (US), Anaconda (US), SAP (US), Domino Data Lab (US), Dataiku (US), DataRobot (US), Apheris (Germany), Comet (US), Databand (US), dotData (US), Explorium (US), Noogata (US), Tecton (US), Spell (US), Arrikto (US), and Iterative (US). |
The Data Science Platform market has been segmented by component into platform and services. The services segment comprises professional services and managed services. The business function includes Marketing, Sales, Logistics, Finance and Accounting, Customer Support, Other Business Functions (operations and HR). The deployment type is segmented into cloud and on-premises. Organization size is segmented into SMEs and large enterprises. Verticals in the report include BFSI, Retail and eCommerce, Telecom and IT, Media and Entertainment, Healthcare and Life Sciences, Government and Defense, Manufacturing, Transportation and Logistics, Energy and Utilities, Other Verticals (travel and hospitality, and education and research). The regional analysis of the Data Science Platform market covers North America, Europe, APAC, MEA and Latin America.
Among the component segment, the services segment in the Data Science Platform market is expected to witness the highest CAGR during the forecast period. The demand for professional services is seen to drive the market for Data Science Platform.
Among deployment type, the cloud Data Science Platform segment is estimated to grow with the highest CAGR during the forecast period. The increasing generation of data leads to various challenges for several organizations. These challenges include storage, privacy, and affordability.
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Among the organization size, the large enterprises are projected to dominate the market, while the SMEs segment is projected to record a higher growth rate during the forecast period. The adoption of Data Science Platform and services among large enterprises is high due to huge chunks of data that can be attributed to the widespread client base. In large enterprises, data plays a major role in evaluating the overall performance of organizations. Large enterprises are leveraging the data science platform coming from various sources, for instance, social media feeds or sensors and cameras, each record needs to be processed in a way that preserves its relation to other data and sequence in time. Log files, eCommerce purchases, weather events, utility service usage, geo-location of people and things, server activity, and more which allow them to take business decisions pro-actively as well as provide customers with better and more personalized services based on real-time data science platform insights.
Based on vertical, the healthcare and life science segment is expected to grow at a higher CAGR during the forecast period, Data Science Platform are gaining acceptance among all verticals to improve profitability and reduce overall costs. The major verticals adopting Data Science Platform are BFSI, Retail and eCommerce, Telecom and IT, Media and Entertainment, Healthcare and Life Sciences, Government and Defense, Manufacturing, Transportation and Logistics, Energy and Utilities, Other Verticals (travel and hospitality, and education and research). Healthcare and life science, by vertical segment, is expected to grow at a higher CAGR during the forecast period.
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The Data Science Platform market is studied across five major regions: North America, Europe, MEA, APAC, and Latin America. North America is estimated to account for the largest market share during the forecast period.
Cloud computing refers to the storage, management, and processing of data via networks of remote servers, which are typically accessed via the Internet. Enterprises mostly in heavily regulated industry verticals, such as BFSI, healthcare and life sciences, and manufacturing, opt for the on-premises deployment model of Data Science Platform. Furthermore, large enterprises with sufficient IT resources are expected to opt for the on-premises deployment model. On-premises is the most reliable deployment mode, which an enterprise can rely on for a high level of control and security. Enterprises need to purchase a license or a copy to deploy cloud-based solutions.
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