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Big Data in Automotive Market: Perspective on Upcoming Impacts and Forecast Analysis (2024-2030) by Component, Software Deployment Type, Data Type, Application, and Region.
Big Data in Automotive Market size was valued at US$ 5.29 Billion in 2023 and the total Big Data in Automotive Market revenue is expected to grow at 18% through 2024 to 2030, reaching nearly US$ 16.85 Billion.
Format : PDF | Report ID : SMR_426
Big Data in Automotive Market Overview:
The car industry and smart technologies have transformed the way adventurers seek thrills and even the most technologically challenged drivers navigate nowadays. Thanks to big data analytics, the vehicle industry has seen remarkable growth throughout the years. Big data is assisting the automobile sector in a variety of ways, including improving vehicle safety with cognitive IoT, lowering maintenance costs with large volumes of data, and increasing uptime with predictive analysis, among other things. This digital transformation in the automobile industry has opened up a plethora of new chances for industry experts to upskill and profit from this rising trend. Big data in automotive market is expected to register CAGR of 18% during the forecast period.
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Big Data in Automotive Market Dynamics:
The automotive industry is growing at a rapid pace, with new advancements being launched every year, from cloud computing and artificial intelligence to electrification and autonomous driving. The big data in automotive market is expected to reach $66.95 billion by 2022, vehicle electrification is expected to reach $126 billion by 2025, and the global autonomous vehicle market is expected to grow at an 18.06 percent compound annual growth rate (CAGR) between 2020 and 2025, after being valued at $24.1 billion in 2019.
Key Drivers:
Connected Cars:
Cars and their owners have evolved in today's linked society. Driver experiences have been altered thanks to big data analytics, which captures real-time data insights from inside and outside vehicles to improve driver safety, vehicle services, and the driving experience. Our driving experiences have been altered thanks to big data analytics. Apart from our phones, laptops, and other electronic gadgets, our automobiles are the second most technologically advanced items we use. Cars today are packed with technology, from the internet and WiFi to sensors and powerful CPUs, and this is just the beginning of the era of smart vehicles. By 2020, it is expected that 90% of new automobiles will have connectivity installed, making Big Data and Analytics a crucial aspect of the technology. It's no surprise that automakers are quickly adopting linked car technology and embedding it within their vehicles, with the connected car market exceeding $130 billion in the United States. GM has stated that it will provide monthly subscriptions, one-time top-ups, daily passes, and even the opportunity to connect the car to a shared data plan with other devices for connected vehicles powered by the AT&T network.
Big Data in the F1 Circuits:
F1 racing teams have advanced to the next level thanks to automotive analytics. High-speed racing combined with data science has created a new high-tech metric for measuring performance based on data points such as tyre pressure, corner braking patterns, fuel burn efficiency, acceleration time, and so on. Every team is getting their own offline data centre, which will provide real-time on-track data to improve performance and fix bugs. Racing teams at the US Grand Prix collected over 243 TB of data, according to a report, all of which was cleaned, processed, and evaluated off-site so that teams could make the necessary changes on-site.
Pre-race simulations, real-time decision-making by analysts and pit crew, post-race analysis, and the broadcast experience are all driven by real-time data streams today. Across cars and drivers, several sensors constantly monitor and communicate data. These data streams provide teams with hidden insights that are not obvious to the naked eye. The Mercedes AMG F1 W08 EQ Power+ vehicles, for example, are outfitted with 200 sensors that transmit millions of data points over the course of a race weekend. According to reports, the automobile transmits around 300GB of data. It has over a thousand channels of data being captured at any given time during a race, according to engineers. The Red Bull RB12 car, on the other hand, is equipped with roughly 100 sensors that collect data on 10,000 components.
Customer Satisfaction and Building Smart Cities:
Cars have 50 or more sensors that collect information on speed, pollutants, fuel consumption, resource usage, and security. All of this information may be utilized to spot patterns and rectify quality concerns quickly or prevent them altogether. Customer happiness and quality management can both be improved with analytics at a low cost. Progressive companies are also collaborating with the government to use predictive analytics to anticipate and identify high congestion zones based on data collected from autos for town planning and smart city development. Using insights from automobile data and other sources such as satellite, cellphone, and GPS data, urban metropolis problems such as effective traffic management, resource allocation, and environmental challenges can be addressed.
IIOT in Automotive Market Segmentation:
Automobile Financing and Warranty Segment: New face of Big Data in Automotive Market
Automobile finance companies amass vast volumes of client information. This data aids them in better understanding their clients, but the sheer volume prevents them from interpreting the data and acting on it. Automobile loan companies examine data to learn more about a customer's financial history and preferences. Companies are now able to deliver more customized financial solutions that are suited for a customer based on their needs thanks to these insights. This will produce more business leads since businesses will be able to differentiate their services, keeping customers away from the fraudsters and defaulters.
Customer Satisfaction and Building Smart Cities:
Cars have 50 or more sensors that collect information on speed, pollutants, fuel consumption, resource usage, and security. All of this information may be utilized to spot patterns and rectify quality concerns quickly or prevent them altogether. Customer happiness and quality management can both be improved with analytics at a low cost. Progressive companies are also collaborating with the government to use predictive analytics to anticipate and identify high congestion zones based on data collected from autos for town planning and smart city development. Using insights from automobile data and other sources such as satellite, cellphone, and GPS data, urban metropolis problems such as effective traffic management, resource allocation, and environmental challenges can be addressed.
IIOT in Automotive Market Segmentation:
Automobile Financing and Warranty Segment: New face of Big Data in Automotive Market
Automobile finance companies amass vast volumes of client information. This data aids them in better understanding their clients, but the sheer volume prevents them from interpreting the data and acting on it. Automobile loan companies examine data to learn more about a customer's financial history and preferences. Companies are now able to deliver more customized financial solutions that are suited for a customer based on their needs thanks to these insights. This will produce more business leads since businesses will be able to differentiate their services, keeping customers away from the fraudsters and defaulters.
Some Real-World Examples of Big Data in the Automobile Industry:
General Motors:
General Motors, one of the largest American automakers, was a forerunner in the automotive sector when it came to Big Data and analytics. Sensors and processors are now commonplace in automobiles. Sensors and telematics within the car are a focus for General Motors, making its vehicles more secure and reliable. Telematics is a goldmine for them because it allows them to save up to $800 per car, and what made this possible was Big Data and analytics.
BMW:
Every year, the German giant produces 2.5 million vehicles and sells them all over the world. But technology isn't restricted to the cars it makes; its entire business strategy is based on Big Data, which informs everything it does from design to engineering to production to sales and customer service. For their production system, Big Data Analytics is setting new benchmarks. They may now produce accurate forecasts and proactively optimize operations by combining employee experience with new opportunities for efficient processing of massive data volumes. This expedites their production system's ongoing improvement.
Maruti Suzuki:
Maruti Suzuki, which has been named number one for the past 14 years, exemplifies how a business should treat its customers. Their customer retention policy is largely due to their "Market to One" methodology. The strategy focuses on offering a tailored experience to each of its clients, and it appears to be working, as Maruti Suzuki saw a 3% increase in overall sales in the first seven months after implementing it. SAS, a software suite for advanced data analytics, was at the heart of this strategy, allowing them to construct a 360-degree perspective of more than 10 million of their clients.
Data can be collected from automobiles using a variety of methods, including GPS, sensors, cameras, and ECUs. Numerous real-time data insights are collected from these sources, then abstracted and merged to deliver critical services or improve future cars. Structured, unstructured, and semi-structured data are generated by all of these components (just 5-10 percent of the entire data). The data from various sources can be processed and analyzed using a variety of technologies. The data is analyzed and processed using tools such as Apache Hadoop, Tableau, Splunk, Zoho Analytics, and others.
Big Data in Automotive Market Regional Insights:
Asia-Pacific segment is expected to grow at a significant rate:
Asia-Pacific has the most people of any of the continents. Asia-Pacific is one of the greatest automobile industry markets, thanks to rising urban populations and rising spending power.
China has long dominated the market as the country with the greatest number of car owners worldwide. Chinese automakers sold over 25.76 million automobiles in 2019, according to the Chinese Association of Automobile Manufacturers. The market for big data in the automotive industry in Asia-Pacific is being driven by the desire for connected cars. Despite a general recession in China, where auto sales have fallen for 14 months in a row as of the end of August 2019, Audi managed an 11%t increase in sales to 660,000 cars last year, owing to strong demand for its popular sport utility vehicles. China, Japan, India, and South Korea accounted for 50% of worldwide vehicle manufacturing in 2019, making Asia and Oceania the largest market for car electrification. By 2025, the global vehicle electrification market is anticipated to be valued $129.6 billion, rising at a CAGR of 11.9%. Stringent government rules for emissions and fuel economy standards, as well as an increase in need for reliable electrical systems, are to blame for this market expansion.
Increased usage, accuracy, and lower pricing of sensors, cameras, and software are some of the drivers pushing the adoption of big data in the region's automotive industry. For example, Changan, a Chinese automaker, released the UNI-T with facial recognition for driver monitoring and human-computer interaction in March 2020. The UNI-T also has an L3 self-driving technology, which the business intends to launch after autonomous driving is fully established in China.
The objective of the report is to present a comprehensive analysis of the Big Data in Automotive Market to the stakeholders in the industry. The report provides trends that are most dominant in the Big Data in Automotive Market and how these trends will influence new business investments and market development throughout the forecast period. The report also aids in the comprehension of the Big Data in Automotive Market dynamics and competitive structure of the market by analyzing market leaders, market followers, and regional players.
The qualitative and quantitative data provided in the Big Data in Automotive Market report is to help understand which market segments, regions are expected to grow at higher rates, factors affecting the market, and key opportunity areas, which will drive the industry and market growth through the forecast period. The report also includes the competitive landscape of key players in the industry along with their recent developments in the Big Data in Automotive Market. The report studies factors such as company size, market share, market growth, revenue, production volume, and profits of the key players in the Big Data in Automotive Market.
The report provides Porter's Five Force Model, which helps in designing the business strategies in the market. The report helps in identifying how many rivals are existing, who they are, and how their product quality is in the Market. The report also analyses if the Big Data in Automotive Market is easy for a new player to gain a foothold in the market, do they enter or exit the market regularly if the market is dominated by a few players, etc.
The report also includes a PESTEL Analysis, which aids in the development of company strategies. Political variables help in figuring out how much a government can influence the Market. Economic variables aid in the analysis of economic performance drivers that have an impact on the Market. Understanding the impact of the surrounding environment and the influence of environmental concerns on the Big Data in Automotive Market is aided by legal factors.
Big Data in Automotive Market Scope:
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Big Data in Automotive Market |
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Market Size in 2023 |
USD 6.24 Bn. |
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Market Size in 2030 |
USD 19.88 Bn. |
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CAGR (2024-2030) |
18% |
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Historic Data |
2018-2022 |
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Base Year |
2023 |
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Forecast Period |
2024-2030 |
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Segment Scope |
by Component
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by Software Deployment Type
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by Data Type
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by Application
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Regional Scope |
North America- United States, Canada, and Mexico Europe – UK, France, Germany, Italy, Spain, Sweden, Austria, and Rest of Europe Asia Pacific – China, India, Japan, South Korea, Australia, ASEAN, Rest of APAC Middle East and Africa - South Africa, GCC, Egypt, Nigeria, Rest of the Middle East and Africa South America – Brazil, Argentina, Rest of South America |
Big Data in Automotive Market Players:
- Accenture (Ireland)
- Adobe Systems Inc. (USA)
- Allerin Tech Pvt Ltd. (India)
- SAS Institute Inc. (USA)
- Stratio Automotive (Portugal)
- Telefonaktiebolaget LM Ericsson (Sweden)
- Auriga, Inc. (USA)
- HCL Technologies Limited (India)
- IBM Corporation (USA)
- Infosys Limited (India)
- LHP Engineering Solutions (USA)
- Mu Sigma (India)
- Oracle Corporation (USA)
- Reply S.p.A. (Italy)
- Capgemini SE (France)
- Dataiku (USA)
- DXC Technology (USA)
- Happiest Minds (India)
Frequently Asked Questions
Asia Pacific region have the highest growth rate in the Big Data in Automotive market.
Accenture (Ireland), Adobe Systems Inc. (USA), Allerin Tech Pvt. Ltd. (India), SAS Institute Inc. (USA), Stratio Automotive (Portugal), Telefonaktiebolaget LM Ericsson (Sweden), Auriga, Inc. (USA), HCL Technologies Limited (India), IBM Corporation (USA), Infosys Limited (India), LHP Engineering Solutions (USA), Mu Sigma (India), Oracle Corporation (USA), Reply S.p.A. (Italy), Capgemini SE (France), Dataiku (USA), DXC Technology (USA), Happiest Minds (India) and others are the key players in the Big Data in Automotive market.
Connected Vehicle & Intelligent Transportation segment is dominating the market owing to increasing penetration of data analytics in the market.
Chapter 1 Scope of the Report
Chapter 2 Research Methodology
2.1.Research Process
2.2.Global Big data in Automotive Market: Target Audience
2.3.Global Big data in Automotive Market: Primary Research (As per Client Requirement)
2.4.Global Big data in Automotive Market: Secondary Research
Chapter 3 Executive Summary
Chapter 4 Competitive Landscape
4.1.Market Share Analysis, By Value, 2023-2030
4.1.1.Market Share Analysis, By Region, By Value, 2023-2030 (In %)
4.1.1.1.North America Market Share Analysis, By Value, 2023-2030 (In %)
4.1.1.2.Europe Market Share Analysis, By Value, 2023-2030 (In %)
4.1.1.3.Asia Pacific Market Share Analysis, By Value, 2023-2030 (In %)
4.1.1.4.South America Market Share Analysis, By Value, 2023-2030 (In %)
4.1.1.5.Middle East and Africa Market Share Analysis, By Value, 2023-2030 (In %)
4.1.2.Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.1.North America Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.1.1.USA Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.1.2.Canada Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.1.3.Mexico Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.Europe Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.1.UK Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.2.France Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.3.Germany Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.4.Italy Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.5.Spain Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.6.Sweden Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.7.Austria Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.2.8.Rest of Europe Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.Asia Pacific Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.1.China Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.2.India Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.3.Japan Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.4.South Korea Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.5.Australia Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.6.ASEAN Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.3.7.Rest of APAC Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.4.South America Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.4.1.Brazil Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.4.2.Argentina Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.4.3.Rest of South America Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5. Middle East and Africa Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5.1.South Africa Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5.2.GCC Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5.3.Egypt Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5.4.Nigeria Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.2.5.5.Rest of Middle East and Africa Market Share Analysis, By Component, By Value, 2023-2030 (In %)
4.1.3.Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.North America Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.1.1.USA Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.1.2.Canada Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.1.3.Mexico Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.Europe Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.1.UK Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.2.France Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.3.Germany Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.4.Italy Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.5.Spain Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.6.Sweden Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.7.Austria Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.2.8.Rest of Europe Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.Asia Pacific Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.1.China Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.2.India Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.3.Japan Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.4.South Korea Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.5.Australia Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.6.ASEAN Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.3.7.Rest of APAC Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.4.South America Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.4.1.Brazil Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.4.2.Argentina Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.4.3.Rest of South America Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5. Middle East and Africa Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5.1.South Africa Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5.2.GCC Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5.3.Egypt Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5.4.Nigeria Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.4.5.5.Rest of Middle East and Africa Market Share Analysis, By Software Deployment Type, By Value, 2023-2030 (In %)
4.1.5.Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.North America Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.1.1.USA Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.1.2.Canada Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.1.3.Mexico Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.Europe Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.1.UK Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.2.France Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.3.Germany Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.4.Italy Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.5.Spain Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.6.Sweden Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.7.Austria Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.2.8.Rest of Europe Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.Asia Pacific Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.1.China Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.2.India Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.3.Japan Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.4.South Korea Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.5.Australia Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.6.ASEAN Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.3.7.Rest of APAC Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.4.South America Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.4.1.Brazil Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.4.2.Argentina Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.4.3.Rest of South America Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.Middle East and Africa Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.1.South Africa Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.2.GCC Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.3.Egypt Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.4.Nigeria Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.6.5.5.Rest of Middle East and Africa Market Share Analysis, By Data Type, By Value, 2023-2030 (In %)
4.1.7.Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.North America Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.1.1.USA Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.1.2.Canada Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.1.3.Mexico Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.Europe Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.1.UK Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.2.France Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.3.Germany Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.4.Italy Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.5.Spain Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.6.Sweden Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.7.Austria Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.2.8.Rest of Europe Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.Asia Pacific Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.1.China Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.2.India Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.3.Japan Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.4.South Korea Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.5.Australia Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.6.ASEAN Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.3.7.Rest of APAC Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.4. South America Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.4.1.Brazil Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.4.2.Argentina Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.4.3.Rest of South America Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5. Middle East and Africa Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5.1.South Africa Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5.2.GCC Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5.3.Egypt Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5.4.Nigeria Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.1.8.5.5.Rest of Middle East and Africa Market Share Analysis, By Application, By Value, 2023-2030 (In %)
4.2. Stellar Competition matrix
4.2.1.Global Stellar Competition Matrix
4.2.2.North America Stellar Competition Matrix
4.2.3.Europe Stellar Competition Matrix
4.2.4.Asia Pacific Stellar Competition Matrix
4.2.5.South America Stellar Competition Matrix
4.2.6.Middle East and Africa Stellar Competition Matrix
4.3.Key Players Benchmarking
4.3.1.Key Players Benchmarking by Product, Pricing, Market Share, Investments, Expansion Plans, Physical Presence and Presence in the Market.
4.4. Mergers and Acquisitions in Industry
4.4.1.M&A by Region, Value and Strategic Intent
4.5. Market Dynamics
4.5.1.Market Drivers
4.5.2.Market Restraints
4.5.3.Market Opportunities
4.5.4.Market Challenges
4.5.5.PESTLE Analysis
4.5.6.PORTERS Five Force Analysis
4.5.7.Value Chain Analysis
Chapter 5 Global Big data in Automotive Market Segmentation: By Component
5.1.Global Big data in Automotive Market, By Component, Overview/Analysis, 2023-2030
5.2.Global Big data in Automotive Market, By Component, By Value, Market Share (%), 2023-2030 (USD Million)
5.3.Global Big data in Automotive Market, By Component, By Value,
5.3.1.Software
5.3.1.1.Data analytics
5.3.1.2.Data Collection
5.3.1.3.Data Discovery & Visualization
5.3.1.4.Data Management
5.3.2.Services
5.3.2.1.Managed/Outsourced
5.3.2.2.Professional Services
Chapter 6 Global Big data in Automotive Market Segmentation: By Software Deployment Type
6.1.Global Big data in Automotive Market, By Software Deployment Type, Overview/Analysis, 2023-2030
6.2.Global Big data in Automotive Market Size, By Software Deployment Type, By Value, Market Share (%), 2023-2030 (USD Million)
6.3.Global Big data in Automotive Market, By Software Deployment Type, By Value,
6.3.1.Cloud
6.3.2.On-premise
Chapter 7 Global Big data in Automotive Market Segmentation: By Data Type
7.1.Global Big data in Automotive Market, By Data Type, Overview/Analysis, 2023-2030
7.2.Global Big data in Automotive Market Size, By Data Type, By Value, Market Share (%), 2023-2030 (USD Million)
7.3.Global Big data in Automotive Market, By Data Type, By Value, -
7.3.1.Structured
7.3.2.Unstructured
7.3.3.Semi-Structured
Chapter 8 Global Big data in Automotive Market Segmentation: By Application
8.1.Global Big data in Automotive Market, By Application, Overview/Analysis, 2023-2030
8.2.Global Big data in Automotive Market Size, By Application, By Value, Market Share (%), 2023-2030 (USD Million)
8.3.Global Big data in Automotive Market, By Application, By Value, -
8.3.1.Product Development
8.3.2.Supply Chain and Manufacturing
8.3.3.OEM Warranty & Aftersales/Dealers
8.3.4.Connected Vehicle & Intelligent Transportation
8.3.5.Sales, Marketing and Others
Chapter 9 Global Big data in Automotive Market Segmentation: By Region
9.1.Global Big data in Automotive Market, By Region – North America
9.1.1.North America Big data in Automotive Market Size, By Component, By Value, 2023-2030 (USD Million)
9.1.2.North America Big data in Automotive Market Size, By Software Deployment Type, By Value, 2023-2030 (USD Million)
9.1.3.North America Big data in Automotive Market Size, By Application, By Value, 2023-2030 (USD Million)
9.1.4.North America Big data in Automotive Market Size, By Data Type, By Value, 2023-2030 (USD Million)
9.1.5.By Country – U.S.
9.1.5.1.U.S. Big data in Automotive Market Size, By Component, By Value, 2023-2030 (USD Million)
9.1.5.2.U.S. Big data in Automotive Market Size, By Software Deployment Type, By Value, 2023-2030 (USD Million)
9.1.5.3.U.S. Big data in Automotive Market Size, By Application, By Value, 2023-2030 (USD Million)
9.1.5.4.U.S. Big data in Automotive Market Size, By Data Type, By Value, 2023-2030 (USD Million)
9.1.5.5.Canada Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.1.5.6.Mexico Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.2. Europe Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.2.1.UK
9.2.2.France
9.2.3.Germany
9.2.4.Italy
9.2.5.Spain
9.2.6.Sweden
9.2.7.Austria
9.2.8.Rest of Europe
9.3. Asia Pacific Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.3.1.China
9.3.2.India
9.3.3.Japan
9.3.4.South Korea
9.3.5.Australia
9.3.6.ASEAN
9.3.7.Rest of APAC
9.4. Middle East and Africa Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.4.1.South Africa
9.4.2.GCC
9.4.3.Egypt
9.4.4.Nigeria
9.4.5.Rest of Middle East and Africa
9.5. South America Big data in Automotive Market Size, By Value, 2023-2030 (USD Million)
9.5.1.Brazil
9.5.2.Argentina
9.5.3.Rest of South America
Chapter 10 Company Profiles
10.1.Key Players
10.1.1.Accenture (Ireland)
10.1.1.1.Company Overview
10.1.1.2.Component Portfolio
10.1.1.3.Financial Overview
10.1.1.4.Business Strategy
10.1.1.5.Key Developments
10.1.2.Adobe Systems Inc. (USA)
10.1.3.Allerin Tech Pvt Ltd. (India)
10.1.4.SAS Institute Inc. (USA)
10.1.5.Stratio Automotive (Portugal)
10.1.6.Telefonaktiebolaget LM Ericsson (Sweden)
10.1.7.Auriga, Inc. (USA)
10.1.8.HCL Technologies Limited (India)
10.1.9.IBM Corporation (USA)
10.1.10.Infosys Limited (India)
10.1.11.LHP Engineering Solutions (USA)
10.1.12.Mu Sigma (India)
10.1.13.Oracle Corporation (USA)
10.1.14.Reply S.p.A. (Italy)
10.1.15.Capgemini SE (France)
10.1.16.Dataiku (USA)
10.1.17.DXC Technology (USA)
10.1.18.Happiest Minds (India)
10.2. Key Findings
10.3. Recommendations
