Big Data in the Automotive Industry: 2017 – 2030
Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.
- Geography Covered
- SNS Telecom & IT
- Publication Date
- May. 2017
“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.
Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The automotive industry is no exception to this trend, where Big Data has found a host of applications ranging from product design and manufacturing to predictive vehicle maintenance and autonomous driving.
SNS Research estimates that Big Data investments in the automotive industry will account for over $2.8 Billion in 2017 alone. Led by a plethora of business opportunities for automotive OEMs, tier-1 suppliers, insurers, dealerships and other stakeholders, these investments are further expected to grow at a CAGR of approximately 12% over the next three years.
The “Big Data in the Automotive Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the automotive industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2017 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 4 application areas, 18 use cases, 6 regions and 35 countries.
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
The report covers the following topics:
- Big Data ecosystem
- Market drivers and barriers
- Enabling technologies, standardization and regulatory initiatives
- Big Data analytics and implementation models
- Business case, key applications and use cases in the automotive industry
- 30 case studies of Big Data investments by automotive OEMs and other stakeholders
- Future roadmap and value chain
- Company profiles and strategies of over 240 Big Data vendors
- Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
- Market analysis and forecasts from 2017 till 2030
Market forecasts are provided for each of the following submarkets and their subcategories:
Hardware, Software & Professional Services
- Professional Services
- Storage & Compute Infrastructure
- Networking Infrastructure
- Hadoop & Infrastructure Software
- Analytic Platforms & Applications
- Cloud Platforms
- Professional Services
- Product Development, Manufacturing & Supply Chain
- After-Sales, Warranty & Dealer Management
- Connected Vehicles & Intelligent Transportation
- Marketing, Sales & Other Applications
- Supply Chain Management
- Product Design & Planning
- Predictive Maintenance & Real-Time Diagnostics
- Recall & Warranty Management
- Parts Inventory & Pricing Optimization
- Dealer Management & Customer Support Services
- UBI (Usage-Based Insurance)
- Autonomous & Semi-Autonomous Driving
- Intelligent Transportation
- Fleet Management
- Driver Safety & Vehicle Cyber Security
- In-Vehicle Experience, Navigation & Infotainment
- Ride Sourcing, Sharing & Rentals
- Marketing & Sales
- Customer Retention
- Third Party Monetization
- Other Use Cases
- Asia Pacific
- Eastern Europe
- Latin & Central America
- Middle East & Africa
- North America
- Western Europe
- Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA
Key Questions Answered
The report provides answers to the following key questions:
- How big is the Big Data opportunity in the automotive industry?
- How is the market evolving by segment and region?
- What will the market size be in 2020 and at what rate will it grow?
- What trends, challenges and barriers are influencing its growth?
- Who are the key Big Data software, hardware and services vendors and what are their strategies?
- How much are automotive OEMs and other stakeholders investing in Big Data?
- What opportunities exist for Big Data analytics in the automotive industry?
- Which countries, application areas and use cases will see the highest percentage of Big Data investments in the automotive industry?
The report has the following key findings:
- In 2017, Big Data vendors will pocket over $2.8 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 12% over the next three years, eventually accounting for over $4 Billion by the end of 2020.
- In a bid to improve customer retention, automotive OEMs are heavily relying on Big Data and analytics to integrate an array of data-driven aftermarket services such as predictive vehicle maintenance, real-time mapping and personalized concierge services.
- In recent years, several prominent partnerships and M&A deals have taken place that highlight the growing importance of Big Data in the automotive industry. For example, tier-1 supplier Delphi recently led an investment round to raise over $25 Million for Otonomo, a startup that has developed a data exchange and marketplace platform for vehicle-generated data.
- Addressing privacy concerns is necessary in order to monetize the swaths of Big Data that will be generated by a growing installed base of connected vehicles and other segments of the automotive industry.