DUBLIN--(BUSINESS WIRE)--Research and Markets (http://www.researchandmarkets.com/research/x5rjsd/big_data_in) has announced the addition of the "Big Data in Global Retail Market: Key Trends, Market Opportunities and Industry Forecast 2015-2020" report to their offering.
Big Data in Global Retail market is expected to grow at a CAGR of 35% representing in huge opportunities in this sector
This growth is driven by increasing penetration of big data, increase in analytics services and availability of affordable big data solution and services to end users.
Big Data in Retail Industry controls the 9% market share in terms of revenue in Global Big Data market. It is expected to become fourth largest industry in terms of it's market share position in 2020.
Organizations worldwide are turning their attention to Big Data as a useful means to derive insights from the huge amount of data generated from various sources. Technologies such as NoSQL databases and MapReduce/Hadoop frameworks are at the core of the solutions heralding a paradigm shift.
The report has detailed company profiles including their position in big data market value chain, financial performance analysis, product and service wise business strategy, SWOT analysis and key customer details for 12 key players in Global market namely TEG Analytics, Heckyl Technologies, KloudData Inc., Gramener, Germin8, VIS Networks Pvt. Ltd., Abzooba, Fintellix, Latentview, Indix, Analytic-Edge and Tookitaki.
Key Topics Covered:
1. Executive Summary
2. Global Big Data Market - Overview
3. Need for Big Data in Retail Sector
4. Forecast for Big Data in Global Retail Market 2015-2020
5. Growth Drivers and Inhibitors for Big Data in Global Retail Market
6. Big Data Industry Value Chain
7. Profile of Key Players in Global Big Data Market
8. Case Study for Big Data in Retail
9. Analysis Models
10. Strategic Recommendations
- TEG Analytics
- Heckyl Technologies
- KloudData Inc.
- VIS Networks Pvt. Ltd.
For more information visit http://www.researchandmarkets.com/research/x5rjsd/big_data_in