Global Big Data in Automotive

Big Data in Automotive Comprehensive Study by Application (Research & Development, Manufacturing, Procurement, Supply Chain Management, Marketing, Transportation & Distribution, Sales & Service, Aftermarket, Customer Behavior Analytics), Data type (Structured, Unstructured, Semi-Structured), Software deployment type (Cloud, On premise), Component (Software, Services) Players and Region - Global Market Outlook to 2028

Big Data in Automotive Market by XX Submarkets | Forecast Years 2023-2028  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
About Big Data in Automotive
Big data analytics in automotive industry is helping the manufacturers to increase the efficiency in marketing as well as sales. It has improved the working by incorporating utilities like predictive maintenance. The big data analytics is helping the automotive industry in various departments such as design and production, supply chain improvements, and automobile financing. For instance, data related to real world driving experience is allowing the manufacturers to improve parameters like safety, fuel economy, engine efficiency, and battery power in automobiles. In supply chain, the company can compare cost, reliability and quality of concerned product components and machinery using big data.

AttributesDetails
Study Period2018-2028
Base Year2022
UnitValue (USD Million)


The companies are now exploring the market by adopting mergers & acquisitions, expansions, investments, new developments in existing products and collaborations as their preferred strategies. The players are also exploring new geographies and industries through expansions and acquisitions so as to avail a competitive advantage through combined synergies. Analyst at AMA Research estimates that United States Players will contribute the maximum growth to Global Big Data in Automotive market throughout the forecasted period. Established and emerging Players should take a closer view at their existing organizations and reinvent traditional business and operating models to adapt to the future.

Accenture (Ireland), HCL Technologies Limited (India), IBM Corporation (United States), Infosys Limited (India), LHP Engineering Solutions (United States), Adobe Systems Inc. (United States), Allerin Tech Pvt Ltd (New Zealand), Auriga, Inc. (United States), Capgemini SE (France), Dataiku (United States) and Deloitte Touche Tohmatsu Limited (United States) are some of the key players that are part of study coverage. Additionally, the Players which are also part of the research coverage are DXC Technology (United States), Happiest Minds (India) and Others.

Segmentation Overview
AMA Research has segmented the market of Global Big Data in Automotive market by , Application (Research & Development, Manufacturing, Procurement, Supply Chain Management, Marketing, Transportation & Distribution, Sales & Service, Aftermarket and Customer Behavior Analytics) and Region.



On the basis of geography, the market of Big Data in Automotive has been segmented into South America (Brazil, Argentina, Rest of South America), Asia Pacific (China, Japan, India, South Korea, Taiwan, Australia, Rest of Asia-Pacific), Europe (Germany, France, Italy, United Kingdom, Netherlands, Rest of Europe), MEA (Middle East, Africa), North America (United States, Canada, Mexico). If we see Market by Data type, the sub-segment i.e. Structured will boost the Big Data in Automotive market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Software deployment type, the sub-segment i.e. Cloud will boost the Big Data in Automotive market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Component, the sub-segment i.e. Software will boost the Big Data in Automotive market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth.

Influencing Trend:
Increasing Demand of Machine Learning in Various Industries

Market Growth Drivers:
Benefits of Big Data Analytics Such as Operational Efficiency is Fuelling the Market and Usage of Smart Technologies Such as Sensors, Advanced Processors, and Others are Generating Data

Challenges:
Lack of Skilled Professionals with Technical Skills

Restraints:
Inability to Manage Huge Amount of Data Effectively

Opportunities:
Increasing Dependencies by Enterprises on Big Data and Growing Importance of Big Data for Designing and Manufacturing Processes

Market Leaders and their expansionary development strategies
In July 2020, IBM has acquired WDG Automation which designs and develops robotic process automation (RPA), intelligent virtual agents (IVA) and artificial intelligence software.
On October 2019, Fujitsu Limited has announced that it will launch a new stream data processing platform for service providers for maximising the use of big data collected from connected cars. It facilitates simple and efficient automotive big data analysis by leveraging Fujitsu's data processing technology.


Key Target Audience
Automobile manufacturers, Software vendors, Government associations, Research organisations and Others

About Approach
To evaluate and validate the market size various sources including primary and secondary analysis is utilized. AMA Research follows regulatory standards such as NAICS/SIC/ICB/TRCB, to have a better understanding of the market. The market study is conducted on basis of more than 200 companies dealing in the market regional as well as global areas with the purpose to understand the companies positioning regarding the market value, volume, and their market share for regional as well as global.

Further to bring relevance specific to any niche market we set and apply a number of criteria like Geographic Footprints, Regional Segments of Revenue, Operational Centres, etc. The next step is to finalize a team (In-House + Data Agencies) who then starts collecting C & D level executives and profiles, Industry experts, Opinion leaders, etc., and work towards appointment generation.

The primary research is performed by taking the interviews of executives of various companies dealing in the market as well as using the survey reports, research institute, and latest research reports. Meanwhile, the analyst team keeps preparing a set of questionnaires, and after getting the appointee list; the target audience is then tapped and segregated with various mediums and channels that are feasible for making connections that including email communication, telephonic, skype, LinkedIn Group & InMail, Community Forums, Community Forums, open Survey, SurveyMonkey, etc.

Report Objectives / Segmentation Covered

By Application
  • Research & Development
  • Manufacturing
  • Procurement
  • Supply Chain Management
  • Marketing
  • Transportation & Distribution
  • Sales & Service
  • Aftermarket
  • Customer Behavior Analytics
By Data type
  • Structured
  • Unstructured
  • Semi-Structured

By Software deployment type
  • Cloud
  • On premise

By Component
  • Software
  • Services

By Regions
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Taiwan
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • Italy
    • United Kingdom
    • Netherlands
    • Rest of Europe
  • MEA
    • Middle East
    • Africa
  • North America
    • United States
    • Canada
    • Mexico
  • 1. Market Overview
    • 1.1. Introduction
    • 1.2. Scope/Objective of the Study
      • 1.2.1. Research Objective
  • 2. Executive Summary
    • 2.1. Introduction
  • 3. Market Dynamics
    • 3.1. Introduction
    • 3.2. Market Drivers
      • 3.2.1. Benefits of Big Data Analytics Such as Operational Efficiency is Fuelling the Market
      • 3.2.2. Usage of Smart Technologies Such as Sensors, Advanced Processors, and Others are Generating Data
    • 3.3. Market Challenges
      • 3.3.1. Lack of Skilled Professionals with Technical Skills
    • 3.4. Market Trends
      • 3.4.1. Increasing Demand of Machine Learning in Various Industries
  • 4. Market Factor Analysis
    • 4.1. Porters Five Forces
    • 4.2. Supply/Value Chain
    • 4.3. PESTEL analysis
    • 4.4. Market Entropy
    • 4.5. Patent/Trademark Analysis
  • 5. Global Big Data in Automotive, by Application, Data type, Software deployment type, Component and Region (value) (2017-2022)
    • 5.1. Introduction
    • 5.2. Global Big Data in Automotive (Value)
      • 5.2.1. Global Big Data in Automotive by: Application (Value)
        • 5.2.1.1. Research & Development
        • 5.2.1.2. Manufacturing
        • 5.2.1.3. Procurement
        • 5.2.1.4. Supply Chain Management
        • 5.2.1.5. Marketing
        • 5.2.1.6. Transportation & Distribution
        • 5.2.1.7. Sales & Service
        • 5.2.1.8. Aftermarket
        • 5.2.1.9. Customer Behavior Analytics
      • 5.2.2. Global Big Data in Automotive by: Data type (Value)
        • 5.2.2.1. Structured
        • 5.2.2.2. Unstructured
        • 5.2.2.3. Semi-Structured
      • 5.2.3. Global Big Data in Automotive by: Software deployment type (Value)
        • 5.2.3.1. Cloud
        • 5.2.3.2. On premise
      • 5.2.4. Global Big Data in Automotive by: Component (Value)
        • 5.2.4.1. Software
        • 5.2.4.2. Services
      • 5.2.5. Global Big Data in Automotive Region
        • 5.2.5.1. South America
          • 5.2.5.1.1. Brazil
          • 5.2.5.1.2. Argentina
          • 5.2.5.1.3. Rest of South America
        • 5.2.5.2. Asia Pacific
          • 5.2.5.2.1. China
          • 5.2.5.2.2. Japan
          • 5.2.5.2.3. India
          • 5.2.5.2.4. South Korea
          • 5.2.5.2.5. Taiwan
          • 5.2.5.2.6. Australia
          • 5.2.5.2.7. Rest of Asia-Pacific
        • 5.2.5.3. Europe
          • 5.2.5.3.1. Germany
          • 5.2.5.3.2. France
          • 5.2.5.3.3. Italy
          • 5.2.5.3.4. United Kingdom
          • 5.2.5.3.5. Netherlands
          • 5.2.5.3.6. Rest of Europe
        • 5.2.5.4. MEA
          • 5.2.5.4.1. Middle East
          • 5.2.5.4.2. Africa
        • 5.2.5.5. North America
          • 5.2.5.5.1. United States
          • 5.2.5.5.2. Canada
          • 5.2.5.5.3. Mexico
  • 6. Big Data in Automotive: Manufacturers/Players Analysis
    • 6.1. Competitive Landscape
      • 6.1.1. Market Share Analysis
        • 6.1.1.1. Top 3
        • 6.1.1.2. Top 5
    • 6.2. Peer Group Analysis (2022)
    • 6.3. BCG Matrix
    • 6.4. Company Profile
      • 6.4.1. Accenture (Ireland)
        • 6.4.1.1. Business Overview
        • 6.4.1.2. Products/Services Offerings
        • 6.4.1.3. Financial Analysis
        • 6.4.1.4. SWOT Analysis
      • 6.4.2. HCL Technologies Limited (India)
        • 6.4.2.1. Business Overview
        • 6.4.2.2. Products/Services Offerings
        • 6.4.2.3. Financial Analysis
        • 6.4.2.4. SWOT Analysis
      • 6.4.3. IBM Corporation (United States)
        • 6.4.3.1. Business Overview
        • 6.4.3.2. Products/Services Offerings
        • 6.4.3.3. Financial Analysis
        • 6.4.3.4. SWOT Analysis
      • 6.4.4. Infosys Limited (India)
        • 6.4.4.1. Business Overview
        • 6.4.4.2. Products/Services Offerings
        • 6.4.4.3. Financial Analysis
        • 6.4.4.4. SWOT Analysis
      • 6.4.5. LHP Engineering Solutions (United States)
        • 6.4.5.1. Business Overview
        • 6.4.5.2. Products/Services Offerings
        • 6.4.5.3. Financial Analysis
        • 6.4.5.4. SWOT Analysis
      • 6.4.6. Adobe Systems Inc. (United States)
        • 6.4.6.1. Business Overview
        • 6.4.6.2. Products/Services Offerings
        • 6.4.6.3. Financial Analysis
        • 6.4.6.4. SWOT Analysis
      • 6.4.7. Allerin Tech Pvt Ltd (New Zealand)
        • 6.4.7.1. Business Overview
        • 6.4.7.2. Products/Services Offerings
        • 6.4.7.3. Financial Analysis
        • 6.4.7.4. SWOT Analysis
      • 6.4.8. Auriga, Inc. (United States)
        • 6.4.8.1. Business Overview
        • 6.4.8.2. Products/Services Offerings
        • 6.4.8.3. Financial Analysis
        • 6.4.8.4. SWOT Analysis
      • 6.4.9. Capgemini SE (France)
        • 6.4.9.1. Business Overview
        • 6.4.9.2. Products/Services Offerings
        • 6.4.9.3. Financial Analysis
        • 6.4.9.4. SWOT Analysis
      • 6.4.10. Dataiku (United States)
        • 6.4.10.1. Business Overview
        • 6.4.10.2. Products/Services Offerings
        • 6.4.10.3. Financial Analysis
        • 6.4.10.4. SWOT Analysis
      • 6.4.11. Deloitte Touche Tohmatsu Limited (United States)
        • 6.4.11.1. Business Overview
        • 6.4.11.2. Products/Services Offerings
        • 6.4.11.3. Financial Analysis
        • 6.4.11.4. SWOT Analysis
  • 7. Global Big Data in Automotive Sale, by Application, Data type, Software deployment type, Component and Region (value) (2023-2028)
    • 7.1. Introduction
    • 7.2. Global Big Data in Automotive (Value)
      • 7.2.1. Global Big Data in Automotive by: Application (Value)
        • 7.2.1.1. Research & Development
        • 7.2.1.2. Manufacturing
        • 7.2.1.3. Procurement
        • 7.2.1.4. Supply Chain Management
        • 7.2.1.5. Marketing
        • 7.2.1.6. Transportation & Distribution
        • 7.2.1.7. Sales & Service
        • 7.2.1.8. Aftermarket
        • 7.2.1.9. Customer Behavior Analytics
      • 7.2.2. Global Big Data in Automotive by: Data type (Value)
        • 7.2.2.1. Structured
        • 7.2.2.2. Unstructured
        • 7.2.2.3. Semi-Structured
      • 7.2.3. Global Big Data in Automotive by: Software deployment type (Value)
        • 7.2.3.1. Cloud
        • 7.2.3.2. On premise
      • 7.2.4. Global Big Data in Automotive by: Component (Value)
        • 7.2.4.1. Software
        • 7.2.4.2. Services
      • 7.2.5. Global Big Data in Automotive Region
        • 7.2.5.1. South America
          • 7.2.5.1.1. Brazil
          • 7.2.5.1.2. Argentina
          • 7.2.5.1.3. Rest of South America
        • 7.2.5.2. Asia Pacific
          • 7.2.5.2.1. China
          • 7.2.5.2.2. Japan
          • 7.2.5.2.3. India
          • 7.2.5.2.4. South Korea
          • 7.2.5.2.5. Taiwan
          • 7.2.5.2.6. Australia
          • 7.2.5.2.7. Rest of Asia-Pacific
        • 7.2.5.3. Europe
          • 7.2.5.3.1. Germany
          • 7.2.5.3.2. France
          • 7.2.5.3.3. Italy
          • 7.2.5.3.4. United Kingdom
          • 7.2.5.3.5. Netherlands
          • 7.2.5.3.6. Rest of Europe
        • 7.2.5.4. MEA
          • 7.2.5.4.1. Middle East
          • 7.2.5.4.2. Africa
        • 7.2.5.5. North America
          • 7.2.5.5.1. United States
          • 7.2.5.5.2. Canada
          • 7.2.5.5.3. Mexico
  • 8. Appendix
    • 8.1. Acronyms
  • 9. Methodology and Data Source
    • 9.1. Methodology/Research Approach
      • 9.1.1. Research Programs/Design
      • 9.1.2. Market Size Estimation
      • 9.1.3. Market Breakdown and Data Triangulation
    • 9.2. Data Source
      • 9.2.1. Secondary Sources
      • 9.2.2. Primary Sources
    • 9.3. Disclaimer
List of Tables
  • Table 1. Big Data in Automotive: by Application(USD Million)
  • Table 2. Big Data in Automotive Research & Development , by Region USD Million (2017-2022)
  • Table 3. Big Data in Automotive Manufacturing , by Region USD Million (2017-2022)
  • Table 4. Big Data in Automotive Procurement , by Region USD Million (2017-2022)
  • Table 5. Big Data in Automotive Supply Chain Management , by Region USD Million (2017-2022)
  • Table 6. Big Data in Automotive Marketing , by Region USD Million (2017-2022)
  • Table 7. Big Data in Automotive Transportation & Distribution , by Region USD Million (2017-2022)
  • Table 8. Big Data in Automotive Sales & Service , by Region USD Million (2017-2022)
  • Table 9. Big Data in Automotive Aftermarket , by Region USD Million (2017-2022)
  • Table 10. Big Data in Automotive Customer Behavior Analytics , by Region USD Million (2017-2022)
  • Table 11. Big Data in Automotive: by Data type(USD Million)
  • Table 12. Big Data in Automotive Structured , by Region USD Million (2017-2022)
  • Table 13. Big Data in Automotive Unstructured , by Region USD Million (2017-2022)
  • Table 14. Big Data in Automotive Semi-Structured , by Region USD Million (2017-2022)
  • Table 15. Big Data in Automotive: by Software deployment type(USD Million)
  • Table 16. Big Data in Automotive Cloud , by Region USD Million (2017-2022)
  • Table 17. Big Data in Automotive On premise , by Region USD Million (2017-2022)
  • Table 18. Big Data in Automotive: by Component(USD Million)
  • Table 19. Big Data in Automotive Software , by Region USD Million (2017-2022)
  • Table 20. Big Data in Automotive Services , by Region USD Million (2017-2022)
  • Table 21. South America Big Data in Automotive, by Country USD Million (2017-2022)
  • Table 22. South America Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 23. South America Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 24. South America Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 25. South America Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 26. Brazil Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 27. Brazil Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 28. Brazil Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 29. Brazil Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 30. Argentina Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 31. Argentina Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 32. Argentina Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 33. Argentina Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 34. Rest of South America Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 35. Rest of South America Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 36. Rest of South America Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 37. Rest of South America Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 38. Asia Pacific Big Data in Automotive, by Country USD Million (2017-2022)
  • Table 39. Asia Pacific Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 40. Asia Pacific Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 41. Asia Pacific Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 42. Asia Pacific Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 43. China Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 44. China Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 45. China Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 46. China Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 47. Japan Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 48. Japan Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 49. Japan Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 50. Japan Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 51. India Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 52. India Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 53. India Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 54. India Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 55. South Korea Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 56. South Korea Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 57. South Korea Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 58. South Korea Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 59. Taiwan Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 60. Taiwan Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 61. Taiwan Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 62. Taiwan Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 63. Australia Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 64. Australia Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 65. Australia Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 66. Australia Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 67. Rest of Asia-Pacific Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 68. Rest of Asia-Pacific Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 69. Rest of Asia-Pacific Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 70. Rest of Asia-Pacific Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 71. Europe Big Data in Automotive, by Country USD Million (2017-2022)
  • Table 72. Europe Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 73. Europe Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 74. Europe Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 75. Europe Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 76. Germany Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 77. Germany Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 78. Germany Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 79. Germany Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 80. France Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 81. France Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 82. France Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 83. France Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 84. Italy Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 85. Italy Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 86. Italy Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 87. Italy Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 88. United Kingdom Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 89. United Kingdom Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 90. United Kingdom Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 91. United Kingdom Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 92. Netherlands Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 93. Netherlands Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 94. Netherlands Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 95. Netherlands Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 96. Rest of Europe Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 97. Rest of Europe Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 98. Rest of Europe Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 99. Rest of Europe Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 100. MEA Big Data in Automotive, by Country USD Million (2017-2022)
  • Table 101. MEA Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 102. MEA Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 103. MEA Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 104. MEA Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 105. Middle East Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 106. Middle East Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 107. Middle East Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 108. Middle East Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 109. Africa Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 110. Africa Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 111. Africa Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 112. Africa Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 113. North America Big Data in Automotive, by Country USD Million (2017-2022)
  • Table 114. North America Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 115. North America Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 116. North America Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 117. North America Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 118. United States Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 119. United States Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 120. United States Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 121. United States Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 122. Canada Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 123. Canada Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 124. Canada Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 125. Canada Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 126. Mexico Big Data in Automotive, by Application USD Million (2017-2022)
  • Table 127. Mexico Big Data in Automotive, by Data type USD Million (2017-2022)
  • Table 128. Mexico Big Data in Automotive, by Software deployment type USD Million (2017-2022)
  • Table 129. Mexico Big Data in Automotive, by Component USD Million (2017-2022)
  • Table 130. Company Basic Information, Sales Area and Its Competitors
  • Table 131. Company Basic Information, Sales Area and Its Competitors
  • Table 132. Company Basic Information, Sales Area and Its Competitors
  • Table 133. Company Basic Information, Sales Area and Its Competitors
  • Table 134. Company Basic Information, Sales Area and Its Competitors
  • Table 135. Company Basic Information, Sales Area and Its Competitors
  • Table 136. Company Basic Information, Sales Area and Its Competitors
  • Table 137. Company Basic Information, Sales Area and Its Competitors
  • Table 138. Company Basic Information, Sales Area and Its Competitors
  • Table 139. Company Basic Information, Sales Area and Its Competitors
  • Table 140. Company Basic Information, Sales Area and Its Competitors
  • Table 141. Big Data in Automotive: by Application(USD Million)
  • Table 142. Big Data in Automotive Research & Development , by Region USD Million (2023-2028)
  • Table 143. Big Data in Automotive Manufacturing , by Region USD Million (2023-2028)
  • Table 144. Big Data in Automotive Procurement , by Region USD Million (2023-2028)
  • Table 145. Big Data in Automotive Supply Chain Management , by Region USD Million (2023-2028)
  • Table 146. Big Data in Automotive Marketing , by Region USD Million (2023-2028)
  • Table 147. Big Data in Automotive Transportation & Distribution , by Region USD Million (2023-2028)
  • Table 148. Big Data in Automotive Sales & Service , by Region USD Million (2023-2028)
  • Table 149. Big Data in Automotive Aftermarket , by Region USD Million (2023-2028)
  • Table 150. Big Data in Automotive Customer Behavior Analytics , by Region USD Million (2023-2028)
  • Table 151. Big Data in Automotive: by Data type(USD Million)
  • Table 152. Big Data in Automotive Structured , by Region USD Million (2023-2028)
  • Table 153. Big Data in Automotive Unstructured , by Region USD Million (2023-2028)
  • Table 154. Big Data in Automotive Semi-Structured , by Region USD Million (2023-2028)
  • Table 155. Big Data in Automotive: by Software deployment type(USD Million)
  • Table 156. Big Data in Automotive Cloud , by Region USD Million (2023-2028)
  • Table 157. Big Data in Automotive On premise , by Region USD Million (2023-2028)
  • Table 158. Big Data in Automotive: by Component(USD Million)
  • Table 159. Big Data in Automotive Software , by Region USD Million (2023-2028)
  • Table 160. Big Data in Automotive Services , by Region USD Million (2023-2028)
  • Table 161. South America Big Data in Automotive, by Country USD Million (2023-2028)
  • Table 162. South America Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 163. South America Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 164. South America Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 165. South America Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 166. Brazil Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 167. Brazil Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 168. Brazil Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 169. Brazil Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 170. Argentina Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 171. Argentina Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 172. Argentina Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 173. Argentina Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 174. Rest of South America Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 175. Rest of South America Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 176. Rest of South America Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 177. Rest of South America Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 178. Asia Pacific Big Data in Automotive, by Country USD Million (2023-2028)
  • Table 179. Asia Pacific Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 180. Asia Pacific Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 181. Asia Pacific Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 182. Asia Pacific Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 183. China Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 184. China Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 185. China Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 186. China Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 187. Japan Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 188. Japan Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 189. Japan Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 190. Japan Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 191. India Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 192. India Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 193. India Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 194. India Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 195. South Korea Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 196. South Korea Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 197. South Korea Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 198. South Korea Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 199. Taiwan Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 200. Taiwan Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 201. Taiwan Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 202. Taiwan Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 203. Australia Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 204. Australia Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 205. Australia Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 206. Australia Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 207. Rest of Asia-Pacific Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 208. Rest of Asia-Pacific Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 209. Rest of Asia-Pacific Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 210. Rest of Asia-Pacific Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 211. Europe Big Data in Automotive, by Country USD Million (2023-2028)
  • Table 212. Europe Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 213. Europe Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 214. Europe Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 215. Europe Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 216. Germany Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 217. Germany Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 218. Germany Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 219. Germany Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 220. France Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 221. France Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 222. France Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 223. France Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 224. Italy Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 225. Italy Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 226. Italy Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 227. Italy Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 228. United Kingdom Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 229. United Kingdom Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 230. United Kingdom Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 231. United Kingdom Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 232. Netherlands Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 233. Netherlands Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 234. Netherlands Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 235. Netherlands Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 236. Rest of Europe Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 237. Rest of Europe Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 238. Rest of Europe Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 239. Rest of Europe Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 240. MEA Big Data in Automotive, by Country USD Million (2023-2028)
  • Table 241. MEA Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 242. MEA Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 243. MEA Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 244. MEA Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 245. Middle East Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 246. Middle East Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 247. Middle East Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 248. Middle East Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 249. Africa Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 250. Africa Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 251. Africa Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 252. Africa Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 253. North America Big Data in Automotive, by Country USD Million (2023-2028)
  • Table 254. North America Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 255. North America Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 256. North America Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 257. North America Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 258. United States Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 259. United States Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 260. United States Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 261. United States Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 262. Canada Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 263. Canada Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 264. Canada Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 265. Canada Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 266. Mexico Big Data in Automotive, by Application USD Million (2023-2028)
  • Table 267. Mexico Big Data in Automotive, by Data type USD Million (2023-2028)
  • Table 268. Mexico Big Data in Automotive, by Software deployment type USD Million (2023-2028)
  • Table 269. Mexico Big Data in Automotive, by Component USD Million (2023-2028)
  • Table 270. Research Programs/Design for This Report
  • Table 271. Key Data Information from Secondary Sources
  • Table 272. Key Data Information from Primary Sources
List of Figures
  • Figure 1. Porters Five Forces
  • Figure 2. Supply/Value Chain
  • Figure 3. PESTEL analysis
  • Figure 4. Global Big Data in Automotive: by Application USD Million (2017-2022)
  • Figure 5. Global Big Data in Automotive: by Data type USD Million (2017-2022)
  • Figure 6. Global Big Data in Automotive: by Software deployment type USD Million (2017-2022)
  • Figure 7. Global Big Data in Automotive: by Component USD Million (2017-2022)
  • Figure 8. South America Big Data in Automotive Share (%), by Country
  • Figure 9. Asia Pacific Big Data in Automotive Share (%), by Country
  • Figure 10. Europe Big Data in Automotive Share (%), by Country
  • Figure 11. MEA Big Data in Automotive Share (%), by Country
  • Figure 12. North America Big Data in Automotive Share (%), by Country
  • Figure 13. Global Big Data in Automotive share by Players 2022 (%)
  • Figure 14. Global Big Data in Automotive share by Players (Top 3) 2022(%)
  • Figure 15. Global Big Data in Automotive share by Players (Top 5) 2022(%)
  • Figure 16. BCG Matrix for key Companies
  • Figure 17. Accenture (Ireland) Revenue, Net Income and Gross profit
  • Figure 18. Accenture (Ireland) Revenue: by Geography 2022
  • Figure 19. HCL Technologies Limited (India) Revenue, Net Income and Gross profit
  • Figure 20. HCL Technologies Limited (India) Revenue: by Geography 2022
  • Figure 21. IBM Corporation (United States) Revenue, Net Income and Gross profit
  • Figure 22. IBM Corporation (United States) Revenue: by Geography 2022
  • Figure 23. Infosys Limited (India) Revenue, Net Income and Gross profit
  • Figure 24. Infosys Limited (India) Revenue: by Geography 2022
  • Figure 25. LHP Engineering Solutions (United States) Revenue, Net Income and Gross profit
  • Figure 26. LHP Engineering Solutions (United States) Revenue: by Geography 2022
  • Figure 27. Adobe Systems Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 28. Adobe Systems Inc. (United States) Revenue: by Geography 2022
  • Figure 29. Allerin Tech Pvt Ltd (New Zealand) Revenue, Net Income and Gross profit
  • Figure 30. Allerin Tech Pvt Ltd (New Zealand) Revenue: by Geography 2022
  • Figure 31. Auriga, Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 32. Auriga, Inc. (United States) Revenue: by Geography 2022
  • Figure 33. Capgemini SE (France) Revenue, Net Income and Gross profit
  • Figure 34. Capgemini SE (France) Revenue: by Geography 2022
  • Figure 35. Dataiku (United States) Revenue, Net Income and Gross profit
  • Figure 36. Dataiku (United States) Revenue: by Geography 2022
  • Figure 37. Deloitte Touche Tohmatsu Limited (United States) Revenue, Net Income and Gross profit
  • Figure 38. Deloitte Touche Tohmatsu Limited (United States) Revenue: by Geography 2022
  • Figure 39. Global Big Data in Automotive: by Application USD Million (2023-2028)
  • Figure 40. Global Big Data in Automotive: by Data type USD Million (2023-2028)
  • Figure 41. Global Big Data in Automotive: by Software deployment type USD Million (2023-2028)
  • Figure 42. Global Big Data in Automotive: by Component USD Million (2023-2028)
  • Figure 43. South America Big Data in Automotive Share (%), by Country
  • Figure 44. Asia Pacific Big Data in Automotive Share (%), by Country
  • Figure 45. Europe Big Data in Automotive Share (%), by Country
  • Figure 46. MEA Big Data in Automotive Share (%), by Country
  • Figure 47. North America Big Data in Automotive Share (%), by Country
List of companies from research coverage that are profiled in the study
  • Accenture (Ireland)
  • HCL Technologies Limited (India)
  • IBM Corporation (United States)
  • Infosys Limited (India)
  • LHP Engineering Solutions (United States)
  • Adobe Systems Inc. (United States)
  • Allerin Tech Pvt Ltd (New Zealand)
  • Auriga, Inc. (United States)
  • Capgemini SE (France)
  • Dataiku (United States)
  • Deloitte Touche Tohmatsu Limited (United States)
Additional players considered in the study are as follows:
DXC Technology (United States) , Happiest Minds (India) , Others
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Key Highlights of Report


Dec 2023 217 Pages 62 Tables Base Year: 2022 Coverage: 15+ Companies; 18 Countries

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Global Big Data in Automotive Quantitative research analysis
Global Big Data in Automotive Qualitative Industry Analysis
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Frequently Asked Questions (FAQ):

The standard version of the report profiles players such as Accenture (Ireland), HCL Technologies Limited (India), IBM Corporation (United States), Infosys Limited (India), LHP Engineering Solutions (United States), Adobe Systems Inc. (United States), Allerin Tech Pvt Ltd (New Zealand), Auriga, Inc. (United States), Capgemini SE (France), Dataiku (United States) and Deloitte Touche Tohmatsu Limited (United States) etc.
The Study can be customized subject to feasibility and data availability. Please connect with our sales representative for further information.
"Increasing Demand of Machine Learning in Various Industries" is seen as one of major influencing trends for Big Data in Automotive Market during projected period 2022-2028.
The Big Data in Automotive market study includes a random mix of players, including both market leaders and some top growing emerging players. Connect with our sales executive to get a complete company list in our research coverage.

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