자동차 산업 빅데이터 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the big data market size in the Automotive Industry market size in 2026 is estimated at USD 8.05 billion, growing from 2025 value of USD 6.91 billion with 2031 projections showing USD 17.31 billion, growing at 16.55% CAGR over 2026-2031.

This report is Segmented by Application (Product Development, Supply Chain and Manufacturing, OEM Warranty and Aftersales/Dealers, and More), Data Source (Power-Train and CAN-Bus Logs, and More), Model (On-Premises and Cloud/Edge Cloud), End-User (OEMs, Tier-1 Suppliers, and More), and Geography. The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Insights and Trends of Big Data Market In Automotive Industry

Increasing efforts to monetize vehicle-generated data

Automakers are redirecting revenue models toward digital subscriptions that ride on telematics feeds. Ford Pro recorded USD 2.6 billion EBIT on USD 17 billion revenue in Q2 2024, propelled by 600,000 paid software subscriptions that leverage fleet data for uptime optimization. Subscription services could generate as much as USD 310 per connected vehicle each year by 2030, bolstering margins. Marketplace platforms now let OEMs license data sets to insurers, municipalities, and energy providers. The Big Data market in the automotive industry, therefore, pivots on analytics engines that convert raw sensor logs into saleable insights. Scalability hinges on hyperscale partnerships that can ingest terabytes without latency.

Growing installed base of connected and software-defined vehicles

Global connected-vehicle stock is projected to hit 367 million units in 2027, a 91% leap over 2023. Japan is clearing 25 public roads for driverless cars and targets 100% electric-vehicle sales by 2035. Such regulations expand the addressable fleet for real-time data services. Software-centric architectures also allow continuous over-the-air feature upgrades, sharpening recurring revenue. Suppliers that master vehicle operating systems stand to capture the lion's share of emerging value pools. The Big Data market in the automotive industry relies on this hardware-agnostic shift to amplify data volume and quality.

Stricter privacy and data-sovereignty regulations

A web of statutes, including GDPR, California's CPRA, and China's PIPL, forces OEMs to invest heavily in governance. India's draft 2025 rules add a mandatory breach notice within 72 hours and annual impact assessments. Chinese localization clauses oblige global brands to build parallel infrastructure behind the firewall. Non-compliance risks fines, reputational harm, and service lockdowns, trimming the near-term expansion tempo of the Big Data market in the automotive industry. Vendors are responding with privacy-enhancing technologies such as federated learning to keep raw data in-country while sharing model weights.

Other drivers and restraints analyzed in the detailed report include:

1. Regulatory mandates spurring telematics data availability
2. Emergence of edge-cloud analytics loops for autonomous-driving model training
3. Lack of industry-wide standard schemas for automotive data sets

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Connected Vehicle and Intelligent Transportation logged the largest slice of Big Data market share in the automotive industry at 42.70% in 2025 and is advancing at a 16.95% CAGR. This growth rests on 5G build-outs, mandated ADAS functions, and rising consumer appetite for seamless infotainment. Real-time congestion rerouting, battery-health monitoring, and dynamic tolling are examples of revenue-generating use cases. Policy moves such as mandatory emergency braking systems in Europe further boost data volumes. OEM Warranty and Aftersales units mine service histories to predict parts demand, cutting downtime. Sales and marketing arms rely on behavioral analytics to tailor offers that lift conversion rates. Traffic optimization algorithms powered by generative AI now flag incidents with high precision, enhancing public-sector appeal. As these applications scale, the Big Data market in the automotive industry cements its role as a cross-sector data orchestrator.

Product Development, Supply Chain, and Manufacturing analytics add another layer of value. Stellantis employs its Mobilisights platform to refine fleet efficiency across Europe. Predictive maintenance models shave unplanned line stoppages, while digital twins of factories cut ramp-up time for new models. Telematics-driven service scheduling improves customer satisfaction scores. Marketing teams leverage usage patterns to craft subscription bundles, translating into steadier revenue streams. Together, these trends reinforce a feedback loop where data-centric services finance further investment in the Big Data market in the automotive industry.

ADAS and autonomous sensors commanded 36.85% of the Big Data market share in the automotive industry in 2025 and carry the fastest 17.72% CAGR. Vision, lidar, and radar units output high-resolution streams that underpin safety features such as lane keeping and collision avoidance. Regulatory edicts in the EU require autonomous emergency braking in all new models, ensuring sensor proliferation. Edge AI chips compress and classify feeds before forwarding summaries to the cloud for model retraining. In-car infotainment and HMI logs capture user preferences, feeding personalized content engines. Power-train and CAN-bus data flow into health-score algorithms that alert owners ahead of faults, cutting warranty costs.

Fleet and insurance databases round out the source mix. Cambridge Mobile Telematics shows that engaged telematics users reduce distracted driving by 20%. Usage-based insurance leverages those inputs to adjust premiums in near real time. As sensor fidelity improves, object-detection accuracy rises, enhancing autonomy. Suppliers like Aptiv have unveiled Gen 6 platforms with over-the-air upgrade paths. Each leap feeds back into the Big Data market in the automotive industry, enriching predictive power and monetization options.

Complete Report Scope:

• By Application
  • Product Development, Supply Chain and Manufacturing
  • OEM Warranty and Aftersales/Dealers
  • Connected Vehicle and Intelligent Transportation
  • Sales, Marketing and Other Applications
• By Data Source
  • Power-train and CAN-bus Logs
  • ADAS/Autonomous Sensor Data
  • In-car Infotainment and HMI Data
  • Fleet Operations and Usage-based Insurance Data
• By Deployment Model
  • On-premises
  • Cloud/Edge Cloud
• By End User
  • OEMs
  • Tier-1 Suppliers
  • Fleet Operators and Mobility Service Providers
  • Insurance and Finance Companies
  • Aftermarket and Dealer Networks
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Turkey
    • Rest of Middle East
  • Africa
    • South Africa
    • Nigeria
    • Rest of Africa

Geography Analysis

North America retained 34.10% of the Big Data market share in the automotive industry in 2025. Deep OEM-supplier linkages, favorable data-sharing regulations, and cross-border trade between the United States and Canada sustain scale advantages. U.S. automakers dispatched USD 17.2 billion worth of vehicles to Canada in 2022, reflecting integrated supply chains. Federal incentives for domestic chip fabrication and the USD 52 billion CHIPS Act underpin the compute supply for analytics workloads. General Motors is implementing NVIDIA Omniverse for plant simulation and DRIVE AGX for in-vehicle AI, highlighting regional leadership in data-driven manufacturing.

Asia Pacific, the fastest-growing territory at 18.08% CAGR, benefits from China's USD 500 billion autonomous-vehicle roadmap and Japan's goal to capture 30% global share in next-generation models. Japan has opened 25 public roads to driverless testing, accelerating data accrual. India's automotive vision targets USD 300 billion output by 2030, backed by a USD 500 million EV production policy and a ₹10,300 crore AI mission that funds national GPU clusters. Smartphone penetration and low-cost connectivity prime the region for telematics adoption, feeding the Big Data market in the automotive industry with high-velocity inputs.

Europe shows steady expansion underpinned by rigorous privacy safeguards. The EU Data Act could surface 30 TB of data per vehicle daily by 2025, enabling insurers to craft real-time policies while safeguarding consumer rights. Stellantis launched fleet, insurance, and EV-charging data packages through its Mobilisights arm in 2024, operating under consent-first principles. Sustainability targets and charging-infrastructure rollouts drive EV penetration, further enriching datasets. To comply with localization clauses, OEMs deploy regional clouds and privacy-enhancing encryption, supporting measured yet resilient growth in the Big Data market in the automotive industry.

1. IBM Corporation
2. Microsoft Corporation
3. Amazon Web Services
4. Google Cloud
5. SAP SE
6. SAS Institute
7. Teradata
8. Continental AG
9. Robert Bosch GmbH
10. HERE Technologies
11. Otonomo Technologies
12. Caruso GmbH
13. NVIDIA Corporation
14. Harman International
15. N-iX
16. Future Processing
17. Reply SpA (Data Reply)
18. Phocas
19. Sight Machine
20. Qburst
21. Monixo
22. Allerin
23. Positive Thinking Company
24. National Instruments

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Increasing efforts by ecosystem players to monetise vehicle-generated data
  • 4.2.2 Growing installed base of connected and software-defined vehicles
  • 4.2.3 Regulatory mandates (e.g., EU-GSR, Chinese MIIT) spurring telematics data availability
  • 4.2.4 Emergence of edge-cloud analytics loops for autonomous-driving model training
  • 4.2.5 OEM-led vehicle-data marketplaces unlocking new recurring-revenue streams
  • 4.2.6 Real-time ADAS log offload to hyperscale clouds reducing time-to-validation
• 4.3 Market Restraints
  • 4.3.1 Stricter privacy and data-sovereignty regulations (GDPR, CPRA, China PIPL)
  • 4.3.2 Lack of industry-wide standard schemas for automotive data sets
  • 4.3.3 High TCO of petabyte-scale, low-latency analytics infrastructure
  • 4.3.4 OEM reluctance to share proprietary driving-scenario IP
• 4.4 Value/Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry
• 4.8 Impact of COVID-19 on the Industry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Application
  • 5.1.1 Product Development, Supply Chain and Manufacturing
  • 5.1.2 OEM Warranty and Aftersales/Dealers
  • 5.1.3 Connected Vehicle and Intelligent Transportation
  • 5.1.4 Sales, Marketing and Other Applications
• 5.2 By Data Source
  • 5.2.1 Power-train and CAN-bus Logs
  • 5.2.2 ADAS/Autonomous Sensor Data
  • 5.2.3 In-car Infotainment and HMI Data
  • 5.2.4 Fleet Operations and Usage-based Insurance Data
• 5.3 By Deployment Model
  • 5.3.1 On-premises
  • 5.3.2 Cloud/Edge Cloud
• 5.4 By End User
  • 5.4.1 OEMs
  • 5.4.2 Tier-1 Suppliers
  • 5.4.3 Fleet Operators and Mobility Service Providers
  • 5.4.4 Insurance and Finance Companies
  • 5.4.5 Aftermarket and Dealer Networks
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 Germany
    • 5.5.3.2 United Kingdom
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East
    • 5.5.5.1 Saudi Arabia
    • 5.5.5.2 United Arab Emirates
    • 5.5.5.3 Turkey
    • 5.5.5.4 Rest of Middle East
  • 5.5.6 Africa
    • 5.5.6.1 South Africa
    • 5.5.6.2 Nigeria
    • 5.5.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
  • 6.4.1 IBM Corporation
  • 6.4.2 Microsoft Corporation
  • 6.4.3 Amazon Web Services
  • 6.4.4 Google Cloud
  • 6.4.5 SAP SE
  • 6.4.6 SAS Institute
  • 6.4.7 Teradata
  • 6.4.8 Continental AG
  • 6.4.9 Robert Bosch GmbH
  • 6.4.10 HERE Technologies
  • 6.4.11 Otonomo Technologies
  • 6.4.12 Caruso GmbH
  • 6.4.13 NVIDIA Corporation
  • 6.4.14 Harman International
  • 6.4.15 N-iX
  • 6.4.16 Future Processing
  • 6.4.17 Reply SpA (Data Reply)
  • 6.4.18 Phocas
  • 6.4.19 Sight Machine
  • 6.4.20 Qburst
  • 6.4.21 Monixo
  • 6.4.22 Allerin
  • 6.4.23 Positive Thinking Company
  • 6.4.24 National Instruments

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-need Assessment