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Report explores the big data in the automotive industry forecast during 2018-2030

Big Data defined as a term to describe data sets whose size is beyond the ability of traditional databases to capture, store, manage and analyze.


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: Make an Inquiry about this news 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.

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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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