Big Data in the Automotive Market By Technologies, Applications, Verticals, Strategies & Forecasts Up to 2017 – 2030

Supply Demand Market Research added a new report on the Big Data in the Automotive Era: 2017 – 2030 – Opportunities, Top Manufacturers, Business Modules, Challenges, Technologies, Applications, Verticals, Strategies & Forecasts.

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.

The 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.

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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.

Table  of  Contents:

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)

And more…….

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