세계의 데이터 사이언스 시장 예측(-2032년) : 구성요소별, 전개 형태별, 기술별, 용도별, 최종사용자별, 지역별 분석

According to Stratistics MRC, the Global Data Science Market is accounted for $159.89 billion in 2025 and is expected to reach $1158.56 billion by 2032 growing at a CAGR of 32.7% during the forecast period. Data Science is an interdisciplinary domain dedicated to uncovering meaningful information from vast and intricate datasets through statistical analysis, machine learning algorithms, and advanced analytical practices. It combines programming abilities, subject expertise, and visualization techniques to detect patterns, predict outcomes, and guide well-informed strategic decisions. The field significantly influences sectors like finance, healthcare, retail, and telecommunications by helping organizations streamline processes, analyze customer behavior, and develop innovative solutions. As data-centric approaches become essential for business growth, the demand for skilled data scientists continues to rise, positioning data science as one of the most influential and fast-evolving fields in the modern digital landscape.

According to the Anaconda State of Data Science 2024 Report, data shows that 87% of practitioners are increasing AI adoption, with applications in data cleaning, task automation, and predictive modeling. Additionally, 49% of companies are adding AI Data Analysts and 46% are creating new AI Engineering roles, demonstrating workforce transformation driven by data science.

Market Dynamics:

Driver:

Growing volume of data

The explosive growth of digital ecosystems, IoT devices, and cloud-based applications has dramatically accelerated global data creation, making it a primary catalyst for the Data Science Market. Companies now accumulate extensive information from user behavior, smart sensors, financial activities, and operational systems, leading to greater reliance on sophisticated analytical methods. Data science helps convert unstructured data into actionable intelligence, strengthening business strategies and performance. As organizations compete to innovate and react quickly to changing conditions, the capability to analyze massive datasets becomes essential. This continuous rise in data production significantly drives the implementation of data science platforms and services across multiple sectors.

Restraint:

Shortage of skilled data science professionals

A significant limitation in the Data Science Market is the persistent shortage of trained data science professionals, leading to difficulties for companies seeking specialized analytical expertise. Data science roles require proficiency in programming, statistics, machine learning, and domain knowledge, resulting in a small and highly competitive talent pool. This gap hinders the adoption of advanced analytics, slows operational workflows, and drives up recruitment expenses. Although many organizations invest in upskilling, the fast-paced advancement of data technologies keeps widening the skills deficit. As firms increasingly depend on data-driven strategies, the lack of qualified professionals remains a major barrier to broader market expansion.

Opportunity:

Expansion of industry-specific data science solutions

The rise of industry-tailored data science applications offers strong opportunities for market expansion, as various sectors seek analytics tools built specifically for their operational needs. Healthcare, finance, manufacturing, and retail increasingly rely on solutions that incorporate domain expertise to improve accuracy, efficiency, and regulatory compliance. Customized systems support use cases such as personalized treatments, risk scoring, maintenance forecasting, supply chain planning, and pricing optimization. As digital transformation accelerates across industries, the demand for specialized data science platforms grows, encouraging technology providers to deliver focused, high-value solutions. This industry-specific approach enables vendors to strengthen competitiveness and address niche customer requirements effectively.

Threat:

Rapid technological changes outpacing workforce capabilities

The rapid evolution of data science technologies creates a significant threat, as organizations often lack the skilled workforce required to keep up. New advancements in AI, machine learning, big data frameworks, and automation appear frequently, demanding continuous learning and adaptation. Many companies struggle to train employees fast enough, resulting in outdated skills, suboptimal model performance, and stalled project execution. Fast-changing tools also make older systems irrelevant more quickly, adding upgrade costs and complexity. Without sustained upskilling and investment, businesses risk losing competitive advantage. This widening capability gap limits the efficiency and scalability of data science deployments across industries.

Covid-19 Impact:

The COVID-19 pandemic played a major role in accelerating the Data Science Market as businesses quickly adopted digital solutions, remote workflows, and advanced analytics. Organizations relied heavily on predictive models, real-time dashboards, and forecasting systems to navigate supply chain challenges, healthcare pressures, and shifts in consumer behavior. Data science enabled governments and enterprises to analyze infection trends, allocate resources efficiently, and improve emergency response planning. The crisis also boosted cloud-based analytics due to higher demand for scalable, flexible infrastructure. Although some industries postponed major IT spending, the pandemic ultimately reinforced the value of data science as an essential tool for operational continuity and future preparedness.

The software platforms segment is expected to be the largest during the forecast period

The software platforms segment is expected to account for the largest market share during the forecast period, significantly outpacing the services segment. According to various industry studies, this component contributes more than eighty percent of the total revenue. Businesses are gravitating toward all-in-one platforms that manage everything from data collection and model building to deployment and monitoring, driving heavy investment. These platform solutions provide scalable infrastructures and streamlined workflows, making them essential for data science teams. Therefore, the dominance of software platforms is a central anchor of the data science market's growth.

The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate due to its expanding use of AI-enabled insights, automation, and predictive analytics. Healthcare providers and researchers increasingly depend on advanced data techniques to enhance diagnostic accuracy, support evidence-based care, and accelerate scientific discovery. The rising emphasis on precision medicine, genetic profiling, and continuous patient monitoring fuels the sector's need for robust analytics systems. Moreover, the surge in digital health adoption, integration of electronic medical records, and widespread telehealth usage generates rich datasets, prompting healthcare institutions to prioritize investment in advanced, scalable data science solutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by its advanced digital landscape, strong enterprise readiness, and early adoption of intelligent analytics solutions. The region hosts leading technology firms, cloud platforms, and AI developers that accelerate innovation and expand data science use cases. Companies across key industries increasingly depend on analytics tools for operational optimization and strategic decision-making. Robust research initiatives, substantial funding toward emerging technologies, and a favorable regulatory environment strengthen its market advantage. Moreover, the growing use of big data architectures, automation, and machine learning enhances organizational capabilities, ensuring North America remains the primary hub for data science growth and investment.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its swift pace of digital transformation, expanding cloud ecosystem, and rising commitments toward analytics and AI adoption. Nations including India, China, Singapore, and South Korea are prioritizing data-centric policies to improve operational efficiency and accelerate innovation. Increasing internet connectivity, widespread mobile adoption, and growing data generation across key verticals stimulate the need for predictive insights and intelligent tools. Supportive government initiatives, strengthening digital infrastructure and a thriving startup environment focused on big data and AI solutions further elevate the region's momentum, positioning Asia-Pacific as the fastest-growing hub for data science expansion.

Key players in the market

Some of the key players in Data Science Market include Google, Microsoft, Amazon, IBM, Fractal Analytics, Mu Sigma, Accenture, Cloudera, Nvidia, Databricks, Tiger Analytics, LatentView Analytics, Teradata, Deloitte and Tata Consultancy Services (TCS).

Key Developments:

In November 2025, IBM and Atruvia AG have sealed a long-term collaboration that paves the way for sustainable and state-of-the-art IT platforms for the banking of tomorrow. Atruvia will use IBM z17, which was announced earlier this year, as a cornerstone supports its mission critical operations including the core banking system.

In September 2025, Microsoft and OpenAI have reached a non-binding agreement with Microsoft to restructure its for-profit arm into a Public Benefit Corporation (PBC), a move that could pave the way for the AI startup to raise new funding and eventually go public. In a blog post, OpenAI Board Chairman Bret Taylor explained that under the new arrangement, OpenAI's nonprofit parent will continue to exist and maintain control over the company's operations.

In August 2025, Accenture has agreed to acquire CyberCX, a leading privately-owned cybersecurity services provider serving both private and public sector organizations across Australia, New Zealand and internationally. The move represents Accenture's largest cybersecurity acquisition to date and will significantly bolster Accenture's cybersecurity services in Asia Pacific.

Components Covered:

• Software Platforms
• Tools & Frameworks
• Services

Deployments Covered:

• On-Premises
• Cloud-Native
• Hybrid

Technologies Covered:

• Machine Learning & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Predictive & Prescriptive Analytics
• Data Engineering & Pipelines

Applications Covered:

• Business Intelligence & Visualization
• Customer Analytics & Personalization
• Fraud Detection & Risk Management
• Healthcare Diagnostics & Genomics
• Supply Chain Optimization
• IoT & Edge Analytics

End Users Covered:

• IT & Telecom
• BFSI
• Healthcare & Life Sciences
• Retail & E-Commerce
• Manufacturing & Automotive
• Energy & Utilities
• Government & Defense
• Education & Research

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Data Science Market, By Component

• 5.1 Introduction
• 5.2 Software Platforms
• 5.3 Tools & Frameworks
• 5.4 Services

6 Global Data Science Market, By Deployment

• 6.1 Introduction
• 6.2 On-Premises
• 6.3 Cloud-Native
• 6.4 Hybrid

7 Global Data Science Market, By Technology

• 7.1 Introduction
• 7.2 Machine Learning & Deep Learning
• 7.3 Natural Language Processing (NLP)
• 7.4 Computer Vision
• 7.5 Predictive & Prescriptive Analytics
• 7.6 Data Engineering & Pipelines

8 Global Data Science Market, By Application

• 8.1 Introduction
• 8.2 Business Intelligence & Visualization
• 8.3 Customer Analytics & Personalization
• 8.4 Fraud Detection & Risk Management
• 8.5 Healthcare Diagnostics & Genomics
• 8.6 Supply Chain Optimization
• 8.7 IoT & Edge Analytics

9 Global Data Science Market, By End User

• 9.1 Introduction
• 9.2 IT & Telecom
• 9.3 BFSI
• 9.4 Healthcare & Life Sciences
• 9.5 Retail & E-Commerce
• 9.6 Manufacturing & Automotive
• 9.7 Energy & Utilities
• 9.8 Government & Defense
• 9.9 Education & Research

10 Global Data Science Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Google
• 12.2 Microsoft
• 12.3 Amazon
• 12.4 IBM
• 12.5 Fractal Analytics
• 12.6 Mu Sigma
• 12.7 Accenture
• 12.8 Cloudera
• 12.9 Nvidia
• 12.10 Databricks
• 12.11 Tiger Analytics
• 12.12 LatentView Analytics
• 12.13 Teradata
• 12.14 Deloitte
• 12.15 Tata Consultancy Services (TCS)