데이터 사이언스 플랫폼 시장 보고서 : 구성요소별, 용도별, 업종별, 지역별(2025-2033년)

The global data science platform market size reached USD 15.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 144.9 Billion by 2033, exhibiting a growth rate (CAGR) of 27.08% during 2025-2033. The rising utilization of data science platforms in the healthcare industry, the growing demand for cloud-based programs in various business organizations, and the rising integration of advanced technologies in data science platforms represent some of the key factors driving the market.

A data science platform is a comprehensive software and hardware infrastructure that provides the tools, technologies, and resources necessary for various aspects of the data science process. Data science is a multidisciplinary field that involves collecting, cleaning, analyzing, and interpreting data to extract valuable insights and make data-driven decisions. These platforms include tools for data extraction, transformation, and loading (ETL), as well as connectors to databases, data warehouses, APIs, and other data sources. They also offer a wide range of machine learning algorithms and modeling tools for building predictive and descriptive models.

Currently, the increased adoption of data science platforms within the healthcare sector, owing to their ability to efficiently analyze, oversee, and integrate vast volumes of structured and unstructured data is primarily driving the market growth. Furthermore, the increasing preference for cloud-based solutions across diverse global business entities is fostering a favorable market landscape. Additionally, there is a growing demand for cost-effective, efficient, and enhanced decision-making tools on a global scale. This surge in demand, coupled with the expanding utilization of data science platforms, which enhance enterprise analysis and productivity, is propelling market growth. Moreover, the integration of artificial intelligence (AI), the internet of things (IoT), and machine learning (ML) into data science platforms is presenting lucrative growth opportunities for industry stakeholders. Furthermore, the increasing appetite for data science platforms, which offer a cohesive and integrated approach to constructing, managing, and optimizing predictive models for businesses, is exerting a positive influence on the market. Additionally, the escalating demand for data science platforms, driven by the evolution of big data technologies, is contributing to market expansion. Furthermore, the heightened need for data science platforms within the BFSI sector due to the growing utilization of banking services is further strengthening the market growth.

Data Science Platform Market Trends/Drivers:

Rising utilization of data science platforms in the healthcare industry

Healthcare generates an enormous amount of data, both structured (patient records) and unstructured such as medical images and clinical notes. Data science platforms enable healthcare providers to effectively analyze, manage, and assimilate this wealth of information. For instance, they can use data analytics to identify trends, patterns, and potential health risks among patient populations. Besides, these platforms empower healthcare professionals to leverage predictive analytics. They can forecast disease outbreaks, identify high-risk patients who may require more attention, and even predict patient outcomes. This predictive capability enhances patient care and resource allocation. Moreover, in the pharmaceutical and biotechnology sectors, data science platforms are instrumental in drug discovery and development. Researchers can analyze genetic data, clinical trial results, and drug interactions to accelerate the process of bringing new treatments to market.

Growing demand for cloud-based programs in various business organizations

Cloud-based platforms offer scalability to handle large datasets and computational demands. Businesses can scale their resources up or down as needed, providing flexibility in managing their data science projects. Besides, these solutions often require lower upfront investment in hardware and infrastructure. This cost-effectiveness appeals to organizations of all sizes, especially startups and small businesses. Moreover, cloud-based platforms enable remote access, facilitating collaboration among geographically dispersed teams. This accessibility is crucial in today's globalized business environment. Additionally, cloud providers handle software updates and infrastructure maintenance, reducing the burden on in-house IT teams and ensuring that organizations always have access to the latest features and security patches.

Rising integration of advanced technologies in data science platforms

AI and ML algorithms are becoming integral parts of data science platforms. They enable automation, predictive modeling, natural language processing, and anomaly detection. These advanced capabilities are essential for extracting valuable insights from complex datasets. Moreover, with the proliferation of IoT devices in various industries, data science platforms are adapting to handle the massive influx of data generated by these devices. They can analyze data from sensors, devices, and machines to provide real-time insights and improve decision-making. Besides, advanced technologies enable data science platforms to offer more sophisticated data visualization techniques. This enhances the ability to convey insights to stakeholders effectively.

Data Science Platform Industry Segmentation:

Breakup by Component:

• Software
• Services

Software represents the most popular component

Data science software offers a wide range of tools and capabilities for data collection, cleaning, analysis, modeling, and visualization. It provides data scientists with the flexibility to perform a multitude of tasks within a single platform. Moreover, it is readily available and accessible to organizations of all sizes. Many software solutions are user-friendly, making them accessible to both data science experts and those with less technical expertise. Besides, software solutions can be scaled up or down to accommodate different data volumes and complexities. This scalability is crucial in handling the ever-increasing amount of data generated by organizations.

Breakup by Application:

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

Marketing and sales hold the largest market share

Marketing and sales are inherently data-intensive fields. They heavily rely on data to make informed decisions about product development, pricing strategies, customer segmentation, and sales forecasting. Data science platforms provide the tools and capabilities to process and analyze vast datasets, enabling more accurate and data-driven decision-making. Besides, understanding customer behavior, preferences, and needs is critical for effective marketing and sales strategies. Data science platforms help organizations gather, analyze, and extract actionable insights from customer data. This allows businesses to tailor their marketing campaigns and sales efforts to target specific customer segments more effectively. Moreover, these platforms assist in optimizing marketing campaigns by analyzing campaign performance metrics and identifying which strategies are most effective. This allows marketers to allocate resources to the most successful campaigns and refine their approaches in real-time.

Breakup by Vertical:

• IT and Telecommunication
• Healthcare
• BFSI
• Manufacturing
• Retail and E-Commerce
• Others

BFSI accounts for the majority of market share

The BFSI industry deals with vast volumes of data, including customer transactions, financial records, market data, and risk assessments. Data science platforms are essential for processing and analyzing this extensive data to extract valuable insights, detect fraudulent activities, and make informed decisions. Besides, risk assessment is a critical aspect of the BFSI sector. Data science platforms equipped with machine learning and predictive analytics help banks and financial institutions assess and mitigate risks effectively. These platforms can identify potential credit defaults, market fluctuations, and fraudulent transactions, which is crucial for maintaining financial stability.

Breakup by Region:

• North America
  • United States
  • Canada
• Asia-Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa

North America leads the market, accounting for the majority of the data science platform market share

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America was the largest market.

North America, particularly the United States, is home to many technology hubs such as Silicon Valley, which is known for innovation and technological advancements. This region fosters a fertile ground for the development and adoption of cutting-edge data science technologies and platforms. Moreover, the region hosts a vast number of large enterprises, including Fortune 500 companies, across various industries. These enterprises have substantial budgets and resources to invest in data science platforms to gain a competitive edge, improve operational efficiency, and drive innovation. Besides, North America leads in research and development activities related to data science and artificial intelligence (AI). Leading universities, research institutions, and tech companies in the region continually push the boundaries of data science capabilities, leading to the development of state-of-the-art platforms and tools.

Competitive Landscape:

The competitive landscape of the market is characterized by the presence of multiple players that include established brands, emerging startups, and specialty manufacturers. Presently, leading companies are investing in research and development to enhance their data science platforms. They are introducing new features, tools, and capabilities to stay ahead of evolving industry trends and customer demands. This includes the integration of artificial intelligence (AI), machine learning (ML), and automation to improve data analytics and predictive modeling. Besides, many key players are expanding their cloud-based data science platform offerings. Cloud platforms provide scalability, flexibility, and accessibility, which are highly valued by businesses. This expansion enables organizations to harness the power of data science without significant infrastructure investments. Moreover, they are acquiring innovative startups and smaller companies in the data science and analytics space. These acquisitions enable them to quickly gain access to cutting-edge technologies, talent, and customer bases.

The market research report has provided a comprehensive analysis of the competitive landscape. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

• Alteryx Inc.
• Cloudera Inc.
• Dataiku Inc.
• Google LLC (Alphabet Inc.)
• H2O.ai Inc.
• International Business Machines Corporation
• Microsoft Corporation
• RapidMiner Inc.
• SAP SE
• SAS Institute Inc.
• The MathWorks Inc.
• TIBCO Software Inc.

Key Questions Answered in This Report

• 1.How big is the global data science platform market?
• 2.What is the expected growth rate of the global data science platform market during 2025-2033?
• 3.What are the key factors driving the global data science platform market?
• 4.What has been the impact of COVID-19 on the global data science platform market?
• 5.What is the breakup of the global data science platform market based on the component?
• 6.What is the breakup of the global data science platform market based on the application?
• 7.What is the breakup of the global data science platform market based on the vertical?
• 8.What are the key regions in the global data science platform market?
• 9.Who are the key players/companies in the global data science platform market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global Data Science Platform Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Component

• 6.1 Software
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Services
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast

7 Market Breakup by Application

• 7.1 Marketing and Sales
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Logistics
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Finance and Accounting
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast
• 7.4 Customer Support
  • 7.4.1 Market Trends
  • 7.4.2 Market Forecast
• 7.5 Others
  • 7.5.1 Market Trends
  • 7.5.2 Market Forecast

8 Market Breakup by Vertical

• 8.1 IT and Telecommunication
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Healthcare
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 BFSI
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Manufacturing
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast
• 8.5 Retail and E-Commerce
  • 8.5.1 Market Trends
  • 8.5.2 Market Forecast
• 8.6 Others
  • 8.6.1 Market Trends
  • 8.6.2 Market Forecast

9 Market Breakup by Region

• 9.1 North America
  • 9.1.1 United States
    • 9.1.1.1 Market Trends
    • 9.1.1.2 Market Forecast
  • 9.1.2 Canada
    • 9.1.2.1 Market Trends
    • 9.1.2.2 Market Forecast
• 9.2 Asia-Pacific
  • 9.2.1 China
    • 9.2.1.1 Market Trends
    • 9.2.1.2 Market Forecast
  • 9.2.2 Japan
    • 9.2.2.1 Market Trends
    • 9.2.2.2 Market Forecast
  • 9.2.3 India
    • 9.2.3.1 Market Trends
    • 9.2.3.2 Market Forecast
  • 9.2.4 South Korea
    • 9.2.4.1 Market Trends
    • 9.2.4.2 Market Forecast
  • 9.2.5 Australia
    • 9.2.5.1 Market Trends
    • 9.2.5.2 Market Forecast
  • 9.2.6 Indonesia
    • 9.2.6.1 Market Trends
    • 9.2.6.2 Market Forecast
  • 9.2.7 Others
    • 9.2.7.1 Market Trends
    • 9.2.7.2 Market Forecast
• 9.3 Europe
  • 9.3.1 Germany
    • 9.3.1.1 Market Trends
    • 9.3.1.2 Market Forecast
  • 9.3.2 France
    • 9.3.2.1 Market Trends
    • 9.3.2.2 Market Forecast
  • 9.3.3 United Kingdom
    • 9.3.3.1 Market Trends
    • 9.3.3.2 Market Forecast
  • 9.3.4 Italy
    • 9.3.4.1 Market Trends
    • 9.3.4.2 Market Forecast
  • 9.3.5 Spain
    • 9.3.5.1 Market Trends
    • 9.3.5.2 Market Forecast
  • 9.3.6 Russia
    • 9.3.6.1 Market Trends
    • 9.3.6.2 Market Forecast
  • 9.3.7 Others
    • 9.3.7.1 Market Trends
    • 9.3.7.2 Market Forecast
• 9.4 Latin America
  • 9.4.1 Brazil
    • 9.4.1.1 Market Trends
    • 9.4.1.2 Market Forecast
  • 9.4.2 Mexico
    • 9.4.2.1 Market Trends
    • 9.4.2.2 Market Forecast
  • 9.4.3 Others
    • 9.4.3.1 Market Trends
    • 9.4.3.2 Market Forecast
• 9.5 Middle East and Africa
  • 9.5.1 Market Trends
  • 9.5.2 Market Breakup by Country
  • 9.5.3 Market Forecast

10 SWOT Analysis

• 10.1 Overview
• 10.2 Strengths
• 10.3 Weaknesses
• 10.4 Opportunities
• 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

• 12.1 Overview
• 12.2 Bargaining Power of Buyers
• 12.3 Bargaining Power of Suppliers
• 12.4 Degree of Competition
• 12.5 Threat of New Entrants
• 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

• 14.1 Market Structure
• 14.2 Key Players
• 14.3 Profiles of Key Players
  • 14.3.1 Alteryx Inc.
    • 14.3.1.1 Company Overview
    • 14.3.1.2 Product Portfolio
    • 14.3.1.3 Financials
  • 14.3.2 Cloudera Inc.
    • 14.3.2.1 Company Overview
    • 14.3.2.2 Product Portfolio
    • 14.3.2.3 Financials
  • 14.3.3 Dataiku Inc.
    • 14.3.3.1 Company Overview
    • 14.3.3.2 Product Portfolio
  • 14.3.4 Google LLC (Alphabet Inc.)
    • 14.3.4.1 Company Overview
    • 14.3.4.2 Product Portfolio
    • 14.3.4.3 SWOT Analysis
  • 14.3.5 H2O.ai Inc.
    • 14.3.5.1 Company Overview
    • 14.3.5.2 Product Portfolio
  • 14.3.6 International Business Machines Corporation
    • 14.3.6.1 Company Overview
    • 14.3.6.2 Product Portfolio
    • 14.3.6.3 Financials
    • 14.3.6.4 SWOT Analysis
  • 14.3.7 Microsoft Corporation
    • 14.3.7.1 Company Overview
    • 14.3.7.2 Product Portfolio
    • 14.3.7.3 Financials
    • 14.3.7.4 SWOT Analysis
  • 14.3.8 RapidMiner Inc.
    • 14.3.8.1 Company Overview
    • 14.3.8.2 Product Portfolio
  • 14.3.9 SAP SE
    • 14.3.9.1 Company Overview
    • 14.3.9.2 Product Portfolio
    • 14.3.9.3 Financials
    • 14.3.9.4 SWOT Analysis
  • 14.3.10 SAS Institute Inc.
    • 14.3.10.1 Company Overview
    • 14.3.10.2 Product Portfolio
    • 14.3.10.3 SWOT Analysis
  • 14.3.11 The MathWorks Inc.
    • 14.3.11.1 Company Overview
    • 14.3.11.2 Product Portfolio
  • 14.3.12 TIBCO Software Inc.
    • 14.3.12.1 Company Overview
    • 14.3.12.2 Product Portfolio
    • 14.3.12.3 SWOT Analysis