The global demand for Data Science Platform Market is presumed to reach the market size of nearly USD 580.76 MN by 2028 from USD 99.3 MN in 2021 with a CAGR of 28.7% under the study period 2022 - 2028.
All Data science and data analysis work is done on a software hub called a data science platform. It includes all the tools required for the data science project, including ideation, installation, discovery, model building, and implementation of the software. To be more precise, data science is an integration of gathering, analysis, and strategy for interpreting results. It enables scientists to brush up on their jobs by allowing them to run, track, repeat, analyze and communicate their findings easily and quickly.
The adoption of AI, dramatic growth of IoT applications, and high demand for machine learning and big data are some of the primary factors fueling the adoption of data science platforms. IT industry trends like big data technology adoption, security and governance, IT centralization, open standards, and libraries have been applied to data science platforms. It offers structured and efficient enterprises in multiple sectors. The upsurge of unified, multidisciplinary cloud-based development platforms helps data scientists to sharpen their abilities and combine ML and analytics into business operations. The techniques like insights, machine learning, predictive analytics, and data mining use these platforms to uncover hidden information from enormous classified and unstructured data. The rise in amounts of structures and unstructured data in end-user industries has fueled big data adoption. The cloud is also catering the market adoption in combination with data science platforms, where users build the could integration platforms. However, costly investment is likely to hamper the market growth, and the development of more data protection issues will slow down the market growth rate. The lack of professional individuals or trained competence can further decrease the growth.
The research report covers Porter’s Five Forces Model, Market Attractiveness Analysis, and Value Chain analysis. These tools help to get a clear picture of the industry’s structure and evaluate the competition attractiveness at a global level. Additionally, these tools also give an inclusive assessment of each segment in the global market of data science platform. The growth and trends of data science platform industry provide a holistic approach to this study.
This section of the data science platform market report provides detailed data on the segments at country and regional level, thereby assisting the strategist in identifying the target demographics for the respective product or services with the upcoming opportunities.
By Deployment Mode
By Organization Size
- Support And Maintenance
- Deployment And Integration
By Business Function
- Small And Medium-Sized Enterprises
- Large Enterprises
- Finance And Accounting
- Customer Support
- Other Business Functions (HR And Operations)
- Retail And E-Commerce
- Telecom And IT
- Media And Entertainment
- Healthcare And Life Sciences
- Government And Defense
- Transportation And Logistics
- Energy And Utilities
- Other Verticals (Travel And Hospitality, And Education And Research)
This section covers the regional outlook, which accentuates current and future demand for the Data Science Platform market across North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa. Further, the report focuses on demand, estimation, and forecast for individual application segments across all the prominent regions.
Global Data Science Platform Market Share by Region (Representative Graph)
The research report also covers the comprehensive profiles of the key players in the market and an in-depth view of the competitive landscape worldwide. The major players in the data science platform market include 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). This section consists of a holistic view of the competitive landscape that includes various strategic developments such as key mergers & acquisitions, future capacities, partnerships, financial overviews, collaborations, new product developments, new product launches, and other developments.
In case you have any custom requirements, do write to us. Our research team can offer a customized report as per your need.