Data Science Platform Market to Reach $322.9 Billion by 2026 –

CHICAGO, August 17, 2022 (GLOBE NEWSWIRE) — A new report from MarketsandMarkets™ predicts that the market size for data science platforms will grow from US$95.3 billion in 2021 to US$322.9 billion in 2026, at a compound annual growth rate (CAGR) of 27.7% during the forecast period It has been. The data science platform industry is driven by the tremendous growth of big data, increasing adoption of cloud-based solutions, increasing application of data science platforms in various industries, and huge demand to gain a competitive edge. Advantages in the growing need to extract detailed insights from complex data.

View detailed TOC atData science platform market
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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 variety of skills, including statistics, computer science, and business knowledge, to analyze data collected from the web, smartphones, customers, sensors, and other sources.

Data science platforms use machine 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, statistical, mathematical , and programming techniques. and data visualization. It helps organizations make data-driven decisions, increase operational efficiency, and improve customer experience.

Reporting scope

Reporting metrics detail
Market size available for several years 2016–2026
Base year considered 2021
Forecast period 2021–2026
prediction unit Value (USD Million)
Data Science Platform Market Size in 2020 US$4.89 billion
Revenue forecast for 2027 US$25.94 billion
growth rate 26.9% CAGR from 2020 to 2027
Target segment Data Science Platforms Market by Component (Platforms and Services), Business Functions (Marketing, Sales, Logistics, and Customer Support), Deployment Modes, Organization Size, Industry Verticals, and Regions – Global Forecast to 2026.
Target area North America, Europe, APAC, MEA, Latin America
Target company 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), (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 Iteration (US).

The data science platform market is segmented into platforms and services by component. The Services segment consists of Professional Services and Managed Services. Business functions include marketing, sales, logistics, finance and accounting, customer support, and other business functions (operations and human resources). Deployment types are split between cloud and on-premises. The size of the organization is categorized as small business and large enterprise. Industries covered in the report include BFSI, Retail & E-Commerce, Telecom & IT, Media & Entertainment, Healthcare & Life Sciences, Government & Defense, Manufacturing, Transportation & Logistics, Energy & Utilities, Other Industries (Travel & Hospitality, education and research). A regional analysis of the data science platform market covers North America, Europe, APAC, MEA, and Latin America.

Among the components segment, the services segment of the data science platform market is expected to witness the highest CAGR during the forecast period. Demand for professional services is expected to drive the market for data science platforms.

Among the deployment types, the cloud data science platform segment is estimated to grow at the highest CAGR during the forecast period. The growing amount of data being generated poses a variety of challenges for some organizations. These challenges include storage, privacy, and affordability.

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Among organization sizes, large companies are projected to dominate the market, while the SME segment is projected to register a higher growth rate during the forecast period. Adoption of data science platforms and services in large enterprises is high due to huge chunks of data that can result from a broad customer base. In large enterprises, data plays an important role in evaluating the overall performance of the organization. Large enterprises leverage data science platforms from a variety of sources, including social media feeds, sensors, and cameras. Each record should be processed in a way that preserves its relationship and chronological order with other data. Log files, e-commerce purchases, weather events, utility service usage, geolocation of people and objects, server activity, etc., can be used to proactively make business decisions as well as based on real data. provide better, more personalized service to your customers. – Insights for Temporal Data Science Platform.

Based on vertical, the healthcare and life sciences segment is expected to grow at a higher CAGR during the forecast period. Data science platforms are gaining acceptance across all verticals to improve profitability and reduce overall costs. Key verticals adopting data science platforms are BFSI, Retail & E-Commerce, Telecom & IT, Media & Entertainment, Healthcare & Life Sciences, Government & Defense, Manufacturing, Transportation & Logistics, Energy & Utilities, Others industries (Travel & Hospitality, Education). and research). Healthcare and life sciences 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 hold the largest market share during the forecast period.

Cloud computing refers to storing, managing, and processing data over a network of remote servers, usually accessed over the Internet. Companies in mostly highly regulated verticals, such as BFSI, healthcare and life sciences, and manufacturing, choose the on-premises deployment model for their data science platform. Additionally, large enterprises with sufficient IT resources are expected to choose the on-premises deployment model. On-premises is the most reliable mode of deployment that businesses can rely on for the highest level of control and security. Companies must purchase licenses or copies to deploy cloud-based solutions.

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