Big Data in the Automotive Market Witnessing Robust Growth and Growing at the CAGR of 16% during forecast period

“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.

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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.

 
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1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

2 Chapter 2: An Overview of Big Data
2.1 What is Big Data?
2.2 Key Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 In-Memory Processing
2.2.5 Stream Processing Technologies
2.2.6 Spark
2.2.7 Other Databases & Analytic Technologies
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Velocity
2.3.3 Variety
2.3.4 Value
2.4 Market Growth Drivers
2.4.1 Awareness of Benefits
2.4.2 Maturation of Big Data Platforms
2.4.3 Continued Investments by Web Giants, Governments & Enterprises
2.4.4 Growth of Data Volume, Velocity & Variety
2.4.5 Vendor Commitments & Partnerships
2.4.6 Technology Trends Lowering Entry Barriers
2.5 Market Barriers
2.5.1 Lack of Analytic Specialists
2.5.2 Uncertain Big Data Strategies
2.5.3 Organizational Resistance to Big Data Adoption
2.5.4 Technical Challenges: Scalability & Maintenance
2.5.5 Security & Privacy Concerns

3 Chapter 3: Big Data Analytics
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics

4 Chapter 4: Business Case & Applications in the Automotive Industry
4.1 Overview & Investment Potential
4.2 Industry Specific Market Growth Drivers
4.3 Industry Specific Market Barriers
4.4 Key Applications
4.4.1 Product Development, Manufacturing & Supply Chain
4.4.1.1 Optimizing the Supply Chain
4.4.1.2 Eliminating Manufacturing Defects
4.4.1.3 Customer-Driven Product Design & Planning
4.4.2 After-Sales, Warranty & Dealer Management
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics
4.4.2.2 Streamlining Recalls & Warranty
4.4.2.3 Parts Inventory & Pricing Optimization
4.4.2.4 Dealer Management & Customer Support Services
4.4.3 Connected Vehicles & Intelligent Transportation
4.4.3.1 UBI (Usage-Based Insurance)
4.4.3.2 Autonomous & Semi-Autonomous Driving
4.4.3.3 Intelligent Transportation
4.4.3.4 Fleet Management
4.4.3.5 Driver Safety & Vehicle Cyber Security
4.4.3.6 In-Vehicle Experience, Navigation & Infotainment
4.4.3.7 Ride Sourcing, Sharing & Rentals
4.4.4 Marketing, Sales & Other Applications
4.4.4.1 Marketing & Sales
4.4.4.2 Customer Retention
4.4.4.3 Third Party Monetization
4.4.4.4 Other Applications

5 Chapter 5: Automotive Industry Case Studies
5.1 Automotive OEMs
5.1.1 Audi: Facilitating Efficient Production Processes with Big Data
5.1.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
5.1.3 Daimler: Ensuring Quality Assurance with Big Data
5.1.4 Dongfeng Motor Corporation: Enriching Network-Connected Autonomous Vehicles with Big Data
5.1.5 FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data
5.1.6 Ford Motor Company: Making Efficient Transportation Decisions with Big Data
5.1.7 GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data
5.1.8 Groupe PSA: Reducing Industrial Energy Bills with Big Data
5.1.9 Groupe Renault: Boosting Driver Safety with Big Data
5.1.10 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data
5.1.11 Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data
5.1.12 Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data
5.1.13 Mazda Motor Corporation: Creating Better Engines with Big Data
5.1.14 Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth
5.1.15 SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data
5.1.16 Subaru: Turbocharging Dealer Interaction with Big Data
5.1.17 Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data
5.1.18 Tesla: Achieving Customer Loyalty with Big Data
5.1.19 Toyota Motor Corporation: Powering Smart Cars with Big Data
5.1.20 Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data
5.1.21 Volvo Cars: Reducing Breakdowns and Failures with Big Data
......

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