Site icon

Analysis of Potential Impact of COVID-19 on Big Data in the Automotive Market Opportunities : Biggest Innovation to Boost Global Growth Rate till 2025

Prince

“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.

Download PDF Brochure for Latest Research Study: https://www.researchmoz.us/enquiry.php?type=S&repid=1170819

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.

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.

The report covers the following topics:

Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services

Horizontal Submarkets

Application Areas

Use Cases

Regional Markets

Country Markets

Make An Enquiry for Discount Before Buying The Report: https://www.researchmoz.us/enquiry.php?type=D&repid=1170819

Key Questions Answered

The report provides answers to the following key questions:

Key Findings

The report has the following key findings:

List of Companies Mentioned

Inquire More About This Report: https://www.researchmoz.us/enquiry.php?type=E&repid=1170819

Table of Contents

1 Chapter 1: Introduction 22
1.1 Executive Summary 22
1.2 Topics Covered 24
1.3 Forecast Segmentation 25
1.4 Key Questions Answered 28
1.5 Key Findings 29
1.6 Methodology 30
1.7 Target Audience 31
1.8 Companies & Organizations Mentioned 32

2 Chapter 2: An Overview of Big Data 36
2.1 What is Big Data? 36
2.2 Key Approaches to Big Data Processing 36
2.2.1 Hadoop 37
2.2.2 NoSQL 39
2.2.3 MPAD (Massively Parallel Analytic Databases) 39
2.2.4 In-Memory Processing 40
2.2.5 Stream Processing Technologies 40
2.2.6 Spark 41
2.2.7 Other Databases & Analytic Technologies 41
2.3 Key Characteristics of Big Data 42
2.3.1 Volume 42
2.3.2 Velocity 42
2.3.3 Variety 42
2.3.4 Value 43
2.4 Market Growth Drivers 44
2.4.1 Awareness of Benefits 44
2.4.2 Maturation of Big Data Platforms 44
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 45
2.4.4 Growth of Data Volume, Velocity & Variety 45
2.4.5 Vendor Commitments & Partnerships 45
2.4.6 Technology Trends Lowering Entry Barriers 46
2.5 Market Barriers 46
2.5.1 Lack of Analytic Specialists 46
2.5.2 Uncertain Big Data Strategies 46
2.5.3 Organizational Resistance to Big Data Adoption 47
2.5.4 Technical Challenges: Scalability & Maintenance 47
2.5.5 Security & Privacy Concerns 47

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 51
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51
3.5.2 In-Database Processing 52
3.5.3 In-Memory Analytics 52
3.5.4 Machine Learning & Data Mining 52
3.5.5 Predictive Analytics 53
3.5.6 NLP (Natural Language Processing) 53
3.5.7 Text Analytics 54
3.5.8 Visual Analytics 55
3.5.9 Graph Analytics 55
3.5.10 Social Media, IT & Telco Network Analytics 56

Continue…

For More Information Kindly Contact: 
ResearchMoz
Mr. Rohit Bhisey,
Tel: +1-518-621-2074
USA-Canada Toll Free: 866-997-4948
Email: sales@researchmoz.us
Follow us on LinkedIn @ http://bit.ly/1TBmnVG
Media Release: https://www.researchmoz.us/pressrelease
Follow me on : http://marketresearchlatestreports.blogspot.com/

Exit mobile version