Big Data in the Automotive Industry- Opportunities, Challenges, Strategies, Demand, Application, On-Going Trends , Forecast During 2018-2030

The market for Big Data in the Automotive Industry is growing with the expansion of this Industry Sector Worldwide. Market Research Hub (MRH) has added a new report titled “Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts” which offer details about the current trends and analysis, as well as scope for the near future. This research study also covers information about the production, consumption and market share based on different active regions. Furthermore, an anticipated growth at a double-digit CAGR for Big Data in the Automotive Industry is highlighted in the report which indicates a prosperous future.

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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.

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 subcategories:

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

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Table of Contents

1 Chapter 1: Introduction 24
1.1 Executive Summary 24
1.2 Topics Covered 26
1.3 Forecast Segmentation 27
1.4 Key Questions Answered 30
1.5 Key Findings 31
1.6 Methodology 32
1.7 Target Audience 33
1.8 Companies & Organizations Mentioned 34

2 Chapter 2: An Overview of Big Data 38
2.1 What is Big Data? 38
2.2 Key Approaches to Big Data Processing 38
2.2.1 Hadoop 39
2.2.2 NoSQL 41
2.2.3 MPAD (Massively Parallel Analytic Databases) 41
2.2.4 In-Memory Processing 42
2.2.5 Stream Processing Technologies 42
2.2.6 Spark 43
2.2.7 Other Databases & Analytic Technologies 43
2.3 Key Characteristics of Big Data 44
2.3.1 Volume 44
2.3.2 Velocity 44
2.3.3 Variety 44
2.3.4 Value 45
2.4 Market Growth Drivers 45
2.4.1 Awareness of Benefits 45
2.4.2 Maturation of Big Data Platforms 45
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 46
2.4.4 Growth of Data Volume, Velocity & Variety 46
2.4.5 Vendor Commitments & Partnerships 46
2.4.6 Technology Trends Lowering Entry Barriers 47
2.5 Market Barriers 47
2.5.1 Lack of Analytic Specialists 47
2.5.2 Uncertain Big Data Strategies 47
2.5.3 Organizational Resistance to Big Data Adoption 48
2.5.4 Technical Challenges: Scalability & Maintenance 48
2.5.5 Security & Privacy Concerns 48

3 Chapter 3: Big Data Analytics 49
3.1 What are Big Data Analytics? 49
3.2 The Importance of Analytics 49
3.3 Reactive vs. Proactive Analytics 50
3.4 Customer vs. Operational Analytics 50
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51

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