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  4. > Strategic Analysis of Big Data in Rapid Transit : Strategies to Achieve Predictive Analytics Biggest Driver for Big Data Implementation in Rail

Key Findings

Overall growth in the Big Data implementation in rail is driven by factors such as digitization of sensors, growing digital content and strong push towards rail automation

By 2021, annual rail investment in Big Data will be over US $ x billion globally. Professional services, applications & analytics and storage to be biggest components of investment in rail
The main aim of implementing Big Data in rail is to enable the use of predictive analytics. Integrating media analytics to improve security of rail infrastructure and payload are key applications
Due to the nature of railways, hardware investment to implement big data expected to be greater than investment in software components. Vehicles outnumber control centers or corporate locations. Hence, Sensor, data storage hardware costs will be higher in implementing Big data in Rail
The data communication system is the backbone of the rail big data ecosystem. As more rail transit system push towards automated operation, the data communication system of CBTC or ERTMS systems can be leveraged to use for Big Data applications and data traffic

Key Findings and Future Outlook

Market Status
Extremely nascent condition. Handful of participants have implemented Big Data capabilities
Changing external environment, new infrastructure and upgrades expected to increase rail investment in big data

Competition
Market is heavily fragmented with many solution providers as the technology is extremely new to the market
Heavy consolidation is expected as smaller companies join forces or integrate with larger solution providers

Technology
Immediate needs are focused on storing large unstructured data sets, greater speeds for managing data
Future needs will be focussed on implementing machine learning to automate predictive analytics

Market Entry Barriers
Funding priorities, lack of understanding and familiarity to big data to restrain growth
Changed environment of commerce expected to force the rail environment to increase adoption of big data

Aim

The aim of this study is to evaluate the revenue growth opportunities available for big data in the rail industry. It identifies business cases and opportunities for future growth.

Objectives

•The study provides a strategic overview of the Big Data market—possible business cases and approaches
•To discuss potential opportunities within the rail industry and related case studies.
•To provide an overview of the level of involvement of OEMs in this space.
•To discuss strategic conclusions and recommendations.
•It provides the market size and penetration forecast of big data in rail transportation spend from 2014 to 2021.

Key Questions this Study will Answer

What are the possible business Big Data can generate across the rail industry?
Why is Big Data important for the rail environment and where it can help in terms of features and services?
What are the current opportunities within the rail industry using Big Data and related, successful case studies?
What market dynamics are influencing the implementation of Big Data?

Table Of Contents

Strategic Analysis of Big Data in Rapid Transit : Strategies to Achieve Predictive Analytics Biggest Driver for Big Data Implementation in Rail
Executive Summary 3
Research Scope, Objectives, Background, and Methodology 8
Definitions and Segmentation 14
Introduction to Big Data 16
Migration Strategy Towards Big Data 20
Implications of Big Data on Rail 28
Mega Trends and Industry Convergence Implications 36
Rail Big Data Market -
• Drivers and Restraints 40
• Forecast and Trends 45
• Market Share and Competitive Analysis 49
Case Studies 53
Conclusions 56
Appendix 59

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