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Global Data Science Platform Market – Industry Trends and Forecast to 2030

ICT

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Global Data Science Platform Market – Industry Trends and Forecast to 2030

  • ICT
  • Upcoming Report
  • May 2023
  • Global
  • 350 Pages
  • No of Tables: 220
  • No of Figures: 60

Global Data Science Platform Market – Industry Trends and Forecast to 2030

Market Size in USD Billion

CAGR :  % Diagram

Diagram Forecast Period 2022–2030
Diagram Market Size (Base Year) USD 122.94 Billion
Diagram Market Size (Forecast Year) USD 942.76 Billion
Diagram CAGR %

Major Markets Players

  • List provided in description

Global Data Science Platform Market, Component Type (Platform, Services), Function Division (Marketing, Sales, Logistics, Finance and Accounting, Customer Support, Business Operations, Others), Deployment Model (On-Premises, Cloud based), Organization Size (Small and Medium-sized Enterprises (SMEs), Large Enterprises), End User Application (Banking, Financial Services, and Insurance (BFSI), Telecom and IT, Retail and E-commerce, Healthcare and Life sciences, Manufacturing, Energy and Utilities, Media and Entertainment, Transportation and Logistics, Government, Others) – Industry Trends and Forecast to 2030.

Data science platform Market

Data Science Platform Market Analysis and Sizes

The platform facilitates the integration of data from various sources, such as databases, APIs, files, and streaming data. It allows users to connect to data sources, extract data, and transform it for analysis. Data science platforms often provide tools for data cleaning, preprocessing, and feature engineering.

Data Bridge Market Research analyses that the data science platform market which was USD 122.94  billion in 2022, would rocket up to USD 942.76  billion by 2030, and is expected to undergo a CAGR of 29.00% during the forecast period. In addition to the market insights such as market value, growth rate, market segments, geographical coverage, market players, and market scenario, the market report curated by the Data Bridge Market Research team includes in-depth expert analysis, import/export analysis, pricing analysis, production consumption analysis, and pestle analysis.

Data Science Platform Market Scope and Segmentation

Report Metric

Details

Forecast Period

2023 to 2030

Base Year

2022

Historic Years

2021 (Customizable to 2015-2020)

Quantitative Units

Revenue in USD Billion, Volumes in Units, Pricing in USD

Segments Covered

Component Type (Platform, Services), Function Division (Marketing, Sales, Logistics, Finance and Accounting, Customer Support, Business Operations, Others), Deployment Model (On-Premises, Cloud based), Organization Size (Small and Medium-sized Enterprises (SMEs), Large Enterprises), End User Application (Banking, Financial Services, and Insurance (BFSI), Telecom and IT, Retail and E-commerce, Healthcare and Life sciences, Manufacturing, Energy and Utilities, Media and Entertainment, Transportation and Logistics, Government, Others)

Countries Covered

U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.
East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America

Market Players Covered

IBM (U.S.), DataRobot Inc., (U.S.), apheris AI GmbH (Germany), The Digital Talent Ecosystem (U.S.), Databand (Israel), dotData (U.S.), Explorium Inc., (U.S.), Noogata (Israel), Tecton Inc., (U.S.), Spell Designs Pty Ltd  (U.S.), Arrikto Inc., (U.S.), Iterative (U.S.), Google Inc (U.S.), Microsoft (U.S.), SAS Institute Inc., (U.S.), Amazon Web Services, Inc. (U.S.),  The MathWorks, Inc. (U.S.), Cloudera Inc.,(U.S.), Teradata (U.S.),  TIBCO Software Inc. (U.S.), ALTERYX, INC. (U.S.), RapidMiner (U.S.), Databricks (U.S.), Snowflake Inc., (U.S.), H2O.ai (U.S.), Altair Inc., (U.S.), Anaconda Inc., (U.S.), SAP SE (U.S.), Domino Data Lab Inc., (U.S.) and Dataiku (U.S.)

Market Opportunities

  • Rapid advancements in technologies such as artificial intelligence (AI), machine learning (ML), and internet of things (IoT)
  • Increasing investment in research and development

Market Definition

A data science platform refers to a software or toolset that facilitates and streamlines the end-to-end data science workflow. It provides a unified environment for data scientists, analysts, and other stakeholders to perform tasks related to data acquisition, preparation, analysis, modeling, visualization, and deployment of machine learning models. A data science platform typically offers a range of integrated tools and capabilities that enable users to work with data effectively.

Global Data Science Platform Dynamics

Drivers

  • Rapid growth of big data

The size of data captured by professional is frequently growing because of rise in social media, IOT and other media. Data science platform have created a prodigious flow of data in both structured and unstructured forms. The development of machine-based and human-generated data is generally 10 times greater than that of old-style corporate data, the growing rate of machine data is generated 50 times quicker. The huge growth in data offerings chances for businesses to acquire new things, which led the rise of demand for fresh approaches and plays a crucial role to drive the market of data science platform.

  •  High investment in research and development

Huge investment in research and development have headed to the rapid progression of technology. Modern data handling coordination and solutions are significant for business growth, the demand for technologies enhancing proficiency is growing with the increasing number of business. Data science platforms are in demand because it make simpler to train, design, scale. Technology such as artificial intelligence, edge computing and machine learning are in their growing phage which help to propel the data science platform market.

Opportunities

  • High Investment and Advancements in Technologies

The high investment in research and development is estimated to generate lucrative opportunities for the market, which will further expand the data science platform market's growth rate in the future. Moreover, the rapid advancements in technologies such as artificial intelligence (AI), machine learning (ML), and internet of things (IoT) further offer numerous growth opportunities within the market.

Restraints/Challenges

  • Uncertainty over the Business Issues

It is essential for businesses to conduct thorough research on the issues they intend to address using a data science platform. Without a clear understanding of the business problem at hand, simply selecting datasets and conducting data analysis can result in low productivity. The effectiveness of using a data science platform for making informed decisions is significantly diminished. Furthermore, even with a defined objective in mind, the efforts of a company can be ineffective if their expectations regarding the implementation of a data science platform do not align with their objectives. This particular factor is anticipated to create several obstacles to growth throughout the projected period.

  • Lack of Technical Expertise

In the current business landscape, advanced analytics methods such as streaming analytics, machine learning, and predictive analytics are widely employed. However, these methods present challenges as they require extensive analytical expertise. Developing a machine learning model, for instance, demands technical proficiency, analytical skills, and critical thinking abilities. Unfortunately, many end users lack personnel who possess the necessary knowledge and skills. Moreover, a significant portion of an organization's efforts is dedicated to collecting and cleaning data from multiple sources. It is not necessary for every employee handling data to be well-versed in data science. Nevertheless, to foster a culture of data-driven decision-making, a combination of business expertise and appropriate training is essential. Consequently, the scarcity of trained staff and technical expertise is expected to pose a significant challenge for the data science platform market in the foreseeable future.

This data science platform market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the data science platform market contact Data Bridge Market Research for an analyst brief, our team will help you take an informed market decision to achieve market growth.

Global Data Science Platform Market Scope

The data science platform market is segmented on the basis of component type, function division, deployment model, organization size and end user application. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.

Component Type

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

Function Division

  • Marketing
  • Sales
  • Logistics
  • Finance and Accounting
  • Customer Support
  • Business Operations
  • Others

Deployment Model

  • On-Premises
  • Cloud-based

Organization Size

  • Small and Medium-sized Enterprises (SMEs)
  • Large Enterprises

End User Application

  • Banking, Financial Services, and Insurance (BFSI)
  • Telecom and IT
  • Retail and E-commerce
  • Healthcare and Life sciences
  • Manufacturing, Energy and Utilities
  • Media and Entertainment
  • Transportation and Logistics
  • Government
  • Others

Global Data Science Platform Market Regional Analysis/Insights

The data science platform market is analyzed and market size insights and trends are provided by country, component type, function division, deployment model, organization size and end user application as referenced above.

The countries covered in data science platform market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the data science platform market due to the presence of a well-established infrastructure and low labor costs in the advancing countries. Moreover, the effective after-sale services offered by manufacturers within the economies is further estimated to accelerate the expansion over the forecast period.

Asia-Pacific is expected to witness significant growth during the forecast period of 2023 to 2030 due to rapid growth in the oil and gas exploration operation in the area within the region. China's large base for producing electronics items makes it a significant contributor to the regional market expansion.

 The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impacts the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.   

Competitive Landscape and Data Science Platform Market Share Analysis

The data science platform market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to data science platform market.

Some of the major players operating in the data science platform Market are:

  • IBM (U.S.)
  • DataRobot Inc., (U.S.)
  • apheris AI GmbH (Germany)
  • The Digital Talent Ecosystem (U.S.)
  • Databand (Israel)
  • dotData (U.S.)
  • Explorium Inc., (U.S.)
  • Noogata (Israel)
  • Tecton Inc., (U.S.)
  • Spell Designs Pty Ltd (U.S.)
  • Arrikto Inc., (U.S.)
  • Iterative (U.S.)
  • Google Inc (U.S.)
  • Microsoft (U.S.)
  • SAS Institute Inc., (U.S.)
  • Amazon Web Services, Inc. (U.S.)
  • The MathWorks, Inc. (U.S.)
  • Cloudera Inc., (U.S.)
  • Teradata (U.S.)
  • TIBCO Software Inc. (U.S.)
  • ALTERYX, INC. (U.S.)
  • RapidMiner (U.S.),
  • Databricks (U.S.)
  • Snowflake Inc., (U.S.)
  • H2O.ai (U.S.)
  • Altair Inc., (U.S.)
  • Anaconda Inc., (U.S.)
  • SAP SE (U.S.)
  • Domino Data Lab Inc., (U.S.)
  • Dataiku (U.S.)


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Research Methodology:

Data collection and base year analysis are done using data collection modules with large sample sizes. The stage includes obtaining market information or related data through various sources and strategies. It includes examining and planning all the data acquired from the past in advance. It likewise envelops the examination of information inconsistencies seen across different information sources. The market data is analysed and estimated using market statistical and coherent models. Also, market share analysis and key trend analysis are the major success factors in the market report. To know more, please request an analyst call or drop down your inquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market and primary (industry expert) validation. Data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Patent Analysis, Pricing Analysis, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.

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FREQUENTLY ASK QUESTIONS

The data science platform market size will be worth USD 942.76 billion by 2030 during the forecast period.
The growth rate of the data science platform market is 29.0% in the forecast period by 2030.
The major companies in the data science platform market are IBM (U.S.), DataRobot Inc., (U.S.), apheris AI GmbH (Germany), The Digital Talent Ecosystem (U.S.), Databand (Israel), dotData (U.S.), Explorium Inc., (U.S.), Noogata (Israel), Tecton Inc., (U.S.), etc.
The Component type, function division, deployment model, organization size, and end-user application are the factors on which the data science platform market research is based.
The Major companies in the data science platform market are IBM (U.S.), DataRobot Inc., (U.S.), apheris AI GmbH (Germany), The Digital Talent Ecosystem (U.S.), Databand (Israel), dotData (U.S.), Explorium Inc., (U.S.), Noogata (Israel), Tecton Inc., (U.S.), Spell Designs Pty Ltd (U.S.), Arrikto Inc., (U.S.), Iterative (U.S.), Google Inc (U.S.), Microsoft (U.S.), SAS Institute Inc., (U.S.), Amazon Web Services, Inc. (U.S.), The MathWorks, Inc. (U.S.), Cloudera Inc.,(U.S.), Teradata (U.S.), TIBCO Software Inc. (U.S.), ALTERYX, INC. (U.S.), RapidMiner (U.S.), Databricks (U.S.), Snowflake Inc., (U.S.), H2O.ai (U.S.), Altair Inc., (U.S.), Anaconda Inc., (U.S.), SAP SE (U.S.), Domino Data Lab Inc., (U.S.) and Dataiku (U.S.).
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