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Data Science Platform Market | Contrive Datum Insights

Data Science Platform Market Size, Share & Trends Estimation Report By Component (Platform, Services {Professional Services, Managed Services}, Support and maintenance, Consulting, Deployment and Integration), By Type (Cloud, On-premise, By Application:, Marketing, Sales, Logistics, Finance & Accounting, Customer Support, Others), By Industry Vertical (BFSI, Retail & eCommerce, Telecom & IT, Media & Entertainment, Healthcare & Life Sciences, Government & Defense, Manufacturing, Transportation & Logistics), By Region, And Segment Forecasts, 2023 - 2030

Published : Dec 2022

Report ID: CDI3323

Pages : 250

Format : Data Science Platform Market Size, Share & Trends Estimation Report By Component (Platform, Services {Professional Services, Managed Services}, Support and maintenance, Consulting, Deployment and Integration), By Type (Cloud, On-premise, By Application:, Marketing, Sales, Logistics, Finance & Accounting, Customer Support, Others), By Industry Vertical (BFSI, Retail & eCommerce, Telecom & IT, Media & Entertainment, Healthcare & Life Sciences, Government & Defense, Manufacturing, Transportation & Logistics), By Region, And Segment Forecasts, 2023 - 2030

Summary Table of Content Customization Download Sample Infographics

In 2021, the data science platforms market size was estimated at USD 96.3 billion and it is expected to hit around USD 378.7 billion by 2030, poised to grow at a compound annual growth rate (CAGR) of 16.43% over the forecast period 2022 to 2030.

Data Science Platform Market Overview

The global data science platform market size was $96.3 billion in 2021 and is expected to expand at a CAGR of 16.43% from 2022 to 2030. Technology advances are advancing rapidly as investments in research and development increase. As businesses grow, demand for technology that can increase productivity and efficiency increases. Advances such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are driving the adoption of software and platforms. As data grows daily, advanced data processing tools and platforms are contributing significantly to business growth.
Data science platforms are one of the most popular software technologies in the industry today. This software consists of a variety of techniques for advanced analytics and machine learning. It enables data scientists to design skills, reveal insights from information, and pass on those experiences through ventures within solitary contexts. Projects undertaken in data science consist of different tools designed for each step of the data modeling process.
The adoption of data science platforms today is growing rapidly. This software provides open-source tools with high flexibility and scalability of computer resources. It can also easily align with various data architectures. In addition to this, the platform enables version control that allows data science groups to team up on ventures without losing work just completed. These benefits are contributing significantly to market growth.

Contrive Datum Insights has recently published the Data Science Platform Market to its huge database which helps to shape the future of businesses by making well-informed business decisions. It offers a comprehensive analysis of various business aspects such as global market trends, recent technological advancements, market size, shares, and new innovations. Furthermore, this analytical data has been compiled through data exploratory techniques such as primary and secondary research. Moreover, an expert team of researchers throws light on various static and dynamic aspects of the global Data Science Platform market.

Some of the key players profiled in the study are:

Different leading key players have been profiled to get better insights into the businesses. It offers detailed elaboration on different top-level industries which are functioning in global regions. It includes informative data such as company overview, contact information, and some significant strategies followed by key players: Microsoft, IBM, Google, Wolfram, Datarobot, Cloudera, Rapidminer, Domino Data Lab, Dataiku, Alteryx, Continuum Analytics, Bridgei2i Analytics, Datarpm, Rexer Analytics, Feature Labs and Others, and others.

Industry Development:

  • November 2021 IBM updates IBM SPSS Modeler 18.2.2. The new update comes with a sophisticated and versatile data mining workbench that allows users to easily and easily develop accurate predictive models without programming.
  • In May 2021, Google updates Google Vertex AI, a newly managed ML platform on Google Cloud. The platform is designed to make it easier for developers to deploy and maintain AI models. It's an unusual announcement from Google I/O, which is usually focused on mobile and web developers and doesn't usually feature much Google Cloud news, but the fact that Google has chosen to unveil Vertex today means that this new service has a broad spectrum of developers.
  • September 2021 Microsoft updates Microsoft Machine Learning Studio. A new update adds a new PyTorch extension library for agile deep learning experiments.

What are the major Applications, Types, and Regions for the Data Science Platform Market?

On the Basis of Component:

  • Platform
  • Services
    • Professional Services
    • Managed Services
  • Support and maintenance
  • Consulting
  • Deployment and Integration

On the Basis of Type:

  • Cloud
  • On-premise

On the Basis of Application:

  • Marketing
  • Sales
  • Logistics
  • Finance & Accounting
  • Customer Support
  • Others
On the Basis of Industry Vertical
  • BFSI
  • Retail & eCommerce
  • Telecom & IT
  • Media & Entertainment
  • Healthcare & Life Sciences
  • Government & Defense
  • Manufacturing
  • Transportation & Logistics

Data Science Platform Market Regional Insights:

In 2021, 36% of global income came from North America. This is because key regional market players are increasingly focusing on the ongoing development of these platforms. For example, technology company Oracle announced the introduction of a cloud-based data science platform in February 2020. Features of the new platform include shared projects, team security policies, suitability, reproducibility, and model catalogs.
In 2021, the European market held the second-highest share. As the use of data-driven digital transformation increases, more businesses in the region are implementing the technology to spur development. Asia Pacific (APAC) has continued to provide attractive market prospects for providers of data science platform solutions and services, with significant growth in data science platforms across developed and growing economies. There are several potential prospects for data science platform markets in Japan, China, and India. APAC is expected to be the fastest-growing region for the demand for data science platform software and services over the forecast period, owing to its rapidly expanding technology-based economy.

Geographically, the global Data Science Platform market has been analyzed in various regions such as North America, Europe, Asia-Pacific, Latin America, the Middle East, and India. The global region is dominating this market in the upcoming future.

  • North America
    • US
    • Canada
    • Mexico
    • Rest of North America
  • Europe
    • Germany
    • France
    • Italy
    • Spain
    • UK
    • Nordic Countries
      • Denmark
      • Finland
      • Iceland
      • Sweden
      • Norway
    • Benelux Union
      • Belgium
      • The Netherlands
      • Luxembourg
    • Rest of Europe
  • Asia-Pacific
    • Japan
    • China
    • India
    • Australia
    • South Korea
    • Southeast Asia
      • Indonesia
      • Thailand
      • Malaysia
      • Singapore
      • Rest of Southeast Asia
    • Rest of Asia-Pacific
  • The Middle East & Africa
    • Saudi Arabia
    • UAE
    • Egypt
    • South Africa
    • Rest of the Middle East & Africa
  • Latin America
    • Brazil
    • Argentina
    • Rest of Latin America

Market Dynamics

Trends:

AI and machine learning are becoming more popular. As AI and machine learning are used more, the need for data science platforms is growing.

Explosion of big data: As big data grows, organizations look for platforms that can handle large amounts of data and give real-time insights.

Cloud-based platforms: The rise of cloud-based data science platforms is a result of how popular cloud computing has become. These platforms give organizations more flexibility and scalability.

Open source technologies: Many data science platforms, like R and Python, are based on open source technologies, which give users more freedom and the ability to make their own changes.

Drivers:

Business intelligence and analytics: The growth of the data science platform market is being driven by the growing need for business intelligence and analytics.

Need to make better decisions: As business environments get more complicated, companies are looking for data science platforms that will help them make better decisions.

Predictive analytics are becoming more popular because companies are using them to predict how customers will act, predict sales, and improve business processes.

Data security and privacy are becoming more and more important. As organizations collect more and more data, they are looking for data science platforms with strong security and privacy features.

Opportunities:

Growth in the healthcare sector: As data science is used more and more in healthcare, data science platform providers have a big chance to make solutions for this sector.

Growth in emerging markets: The data science platform market is growing quickly in emerging markets like Asia-Pacific and Latin America, which gives providers a chance to grow globally.

Integration with other technologies: Data science platforms can be combined with other technologies, like blockchain and the Internet of Things (IoT), giving providers the chance to offer more services.

Focus on SMBs: Small and medium-sized businesses (SMBs) may not have the resources to build their own data science solutions, so data science platform providers should focus on them.

Data Science Platform Market Report Scope:

Report Attributes

Details

Growth Rate

CAGR of 16.43% from 2022 to 2030.

By Type

On-Premises, On-Demand

By Component

Platform, Services, Support and maintenance, Consulting, Deployment and Integration

By Application

Marketing, Sales, Logistics, Finance & Accounting, Customer Support, Others

By Industry Vertical

BFSI, Retail & eCommerce, Telecom & IT, Media & Entertainment, Healthcare & Life Sciences, Government & Defense, Manufacturing, Transportation & Logistics

By Companies 

Microsoft, IBM, Google, Wolfram, Datarobot, Cloudera, Rapidminer, Domino Data Lab, Dataiku, Alteryx, Continuum Analytics, Bridgei2i Analytics, Datarpm, Rexer Analytics, Feature Labs and Others

Regions and Countries Covered

  • North America: (US, Canada, Mexico, Rest of North America)
  • Europe(Germany, France, Italy, Spain, UK, Nordic Countries, Benelux Union, Rest of Europe)
  • Asia-Pacific (Japan, China, India, Australia, South Korea, Southeast Asia, Rest of Asia-Pacific)
  • The Middle East & Africa(Saudi Arabia, UAE, Egypt, South Africa, Rest of the Middle East & Africa)
  • Latin America(Brazil, Argentina, Rest of Latin America)
  • Rest Of the World

Base Year

 2022

Historical Year

 2017 to 2022

Forecast Year

 2023 to 2030

 Frequently Asked Questions (FAQ) about the Data Science Platform market:

What is the CAGR of the global Data Science Platform market?

The Data Science Platform Market is growing at a CAGR of 16.43% during the forecast period.

Which region would offer high growth for vendors in the Data Science Platform market?

North America accounted for the largest market vendors in the Data Science Platform market.

Which Is the Most potential market segment?

By Types- Cloud, On-Premises

By Applications - Marketing,Sales, Logistics, Finance & Accounting, Customer Support, Others

Which are the top industry players in the Data Science Platform market?

Microsoft, IBM, Google, Wolfram, Datarobot, Cloudera, Rapidminer, Domino Data Lab, Dataiku, Alteryx, Continuum Analytics, Bridgei2i Analytics, Datarpm, Rexer Analytics, Feature Labs and Others

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