Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts
Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts
Abstract
Synopsis:
“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 Telecom & IT estimates that Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 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 16% over the next three years.
The “Big Data in the Automotive Industry: 2018 – 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 2018 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.
Key Findings:
The report has the following key findings:
In 2018, Big Data vendors will pocket more than $3.3 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 16% over the next three years, eventually accounting for over $5 Billion by the end of 2021.
Through the use of Big Data technologies, automotive OEMs and other stakeholders are beginning to exploit vehicle-generated data assets in a number of innovative ways ranging from predictive vehicle maintenance and UBI (Usage-Based Insurance) to real-time mapping, personalized concierge, autonomous driving and beyond.
Edge analytics, which refers to the processing and analysis of information closer to the point of origin, is increasingly becoming an indispensable capability for applications such as autonomous driving where real-time data – from cameras, LiDAR and other on-board sensors – needs to be acted upon instantly and reliably.
Privacy continues to remain a major concern, and ensuring the protection of sensitive information – through creative anonymization and dedicated cybersecurity investments – 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.
Topics Covered:
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, application areas and use cases in the automotive industry
Over 35 case studies of Big Data investments by automotive OEMs and other stakeholders
Future roadmap and value chain
Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
Market analysis and forecasts from 2018 till 2030
Forecast Segmentation:
Market forecasts are provided for each of the following submarkets and their categories:
Hardware, Software & Professional Services
Hardware
Software
Professional Services
Horizontal Submarkets
Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services
Application Areas
Product Development, Manufacturing & Supply Chain
After-Sales, Warranty & Dealer Management
Connected Vehicles & Intelligent Transportation
Marketing, Sales & Other Applications
Use Cases
Supply Chain Management
Manufacturing
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
Regional Markets
Asia Pacific
Eastern Europe
Latin & Central America
Middle East & Africa
North America
Western Europe
Country Markets
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 2021, 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?
List of Companies Mentioned:
Content
Table of Contents
Chapter 1: Introduction
Executive Summary
Topics Covered
Forecast Segmentation
Key Questions Answered
Key Findings
Methodology
Target Audience
Companies & Organizations Mentioned
Chapter 2: An Overview of Big Data
What is Big Data?
Key Approaches to Big Data Processing
Hadoop
NoSQL
MPAD (Massively Parallel Analytic Databases)
In-Memory Processing
Stream Processing Technologies
Spark
Other Databases & Analytic Technologies
Key Characteristics of Big Data
Volume
Velocity
Variety
Value
Market Growth Drivers
Awareness of Benefits
Maturation of Big Data Platforms
Continued Investments by Web Giants, Governments & Enterprises
Growth of Data Volume, Velocity & Variety
Vendor Commitments & Partnerships
Technology Trends Lowering Entry Barriers
Market Barriers
Lack of Analytic Specialists
Uncertain Big Data Strategies
Organizational Resistance to Big Data Adoption
Technical Challenges: Scalability & Maintenance
Security & Privacy Concerns
Chapter 3: Big Data Analytics
What are Big Data Analytics?
The Importance of Analytics
Reactive vs. Proactive Analytics
Customer vs. Operational Analytics
Technology & Implementation Approaches
Grid Computing
In-Database Processing
In-Memory Analytics
Machine Learning & Data Mining
Predictive Analytics
NLP (Natural Language Processing)
Text Analytics
Visual Analytics
Graph Analytics
Social Media, IT & Telco Network Analytics
Chapter 4: Business Case & Applications in the Automotive Industry
Overview & Investment Potential
Industry Specific Market Growth Drivers
Industry Specific Market Barriers
Key Applications
Product Development, Manufacturing & Supply Chain
Optimizing the Supply Chain
Eliminating Manufacturing Defects
Customer-Driven Product Design & Planning
After-Sales, Warranty & Dealer Management
Predictive Maintenance & Real-Time Diagnostics
Streamlining Recalls & Warranty
Parts Inventory & Pricing Optimization
Dealer Management & Customer Support Services
Connected Vehicles & Intelligent Transportation
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 & Other Applications
Marketing & Sales
Customer Retention
Third Party Monetization
Other Applications
Synopsis:
“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 Telecom & IT estimates that Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 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 16% over the next three years.
The “Big Data in the Automotive Industry: 2018 – 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 2018 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.
Key Findings:
The report has the following key findings:
In 2018, Big Data vendors will pocket more than $3.3 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 16% over the next three years, eventually accounting for over $5 Billion by the end of 2021.
Through the use of Big Data technologies, automotive OEMs and other stakeholders are beginning to exploit vehicle-generated data assets in a number of innovative ways ranging from predictive vehicle maintenance and UBI (Usage-Based Insurance) to real-time mapping, personalized concierge, autonomous driving and beyond.
Edge analytics, which refers to the processing and analysis of information closer to the point of origin, is increasingly becoming an indispensable capability for applications such as autonomous driving where real-time data – from cameras, LiDAR and other on-board sensors – needs to be acted upon instantly and reliably.
Privacy continues to remain a major concern, and ensuring the protection of sensitive information – through creative anonymization and dedicated cybersecurity investments – 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.
Topics Covered:
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, application areas and use cases in the automotive industry
Over 35 case studies of Big Data investments by automotive OEMs and other stakeholders
Future roadmap and value chain
Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
Market analysis and forecasts from 2018 till 2030
Forecast Segmentation:
Market forecasts are provided for each of the following submarkets and their categories:
Hardware, Software & Professional Services
Hardware
Software
Professional Services
Horizontal Submarkets
Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services
Application Areas
Product Development, Manufacturing & Supply Chain
After-Sales, Warranty & Dealer Management
Connected Vehicles & Intelligent Transportation
Marketing, Sales & Other Applications
Use Cases
Supply Chain Management
Manufacturing
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
Regional Markets
Asia Pacific
Eastern Europe
Latin & Central America
Middle East & Africa
North America
Western Europe
Country Markets
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 2021, 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?
List of Companies Mentioned:
Table of Contents
Chapter 1: Introduction
Executive Summary
Topics Covered
Forecast Segmentation
Key Questions Answered
Key Findings
Methodology
Target Audience
Companies & Organizations Mentioned
Chapter 2: An Overview of Big Data
What is Big Data?
Key Approaches to Big Data Processing
Hadoop
NoSQL
MPAD (Massively Parallel Analytic Databases)
In-Memory Processing
Stream Processing Technologies
Spark
Other Databases & Analytic Technologies
Key Characteristics of Big Data
Volume
Velocity
Variety
Value
Market Growth Drivers
Awareness of Benefits
Maturation of Big Data Platforms
Continued Investments by Web Giants, Governments & Enterprises
Growth of Data Volume, Velocity & Variety
Vendor Commitments & Partnerships
Technology Trends Lowering Entry Barriers
Market Barriers
Lack of Analytic Specialists
Uncertain Big Data Strategies
Organizational Resistance to Big Data Adoption
Technical Challenges: Scalability & Maintenance
Security & Privacy Concerns
Chapter 3: Big Data Analytics
What are Big Data Analytics?
The Importance of Analytics
Reactive vs. Proactive Analytics
Customer vs. Operational Analytics
Technology & Implementation Approaches
Grid Computing
In-Database Processing
In-Memory Analytics
Machine Learning & Data Mining
Predictive Analytics
NLP (Natural Language Processing)
Text Analytics
Visual Analytics
Graph Analytics
Social Media, IT & Telco Network Analytics
Chapter 4: Business Case & Applications in the Automotive Industry
Overview & Investment Potential
Industry Specific Market Growth Drivers
Industry Specific Market Barriers
Key Applications
Product Development, Manufacturing & Supply Chain
Optimizing the Supply Chain
Eliminating Manufacturing Defects
Customer-Driven Product Design & Planning
After-Sales, Warranty & Dealer Management
Predictive Maintenance & Real-Time Diagnostics
Streamlining Recalls & Warranty
Parts Inventory & Pricing Optimization
Dealer Management & Customer Support Services
Connected Vehicles & Intelligent Transportation
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 & Other Applications
Marketing & Sales
Customer Retention
Third Party Monetization
Other Applications
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