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Market Research Report

Big Data in Global Telecom Market 2021

Published by NAVADHI Market Research Pvt Ltd Product code 637281
Published Content info 74 Pages
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Big Data in Global Telecom Market 2021
Published: May 4, 2018 Content info: 74 Pages
Description

Big Data is a vital strategic tool for telecom operators and MVNOs which can prevent revenue decline and identify the trend affecting markets globally. Big Data enables Telecom companies to leverage one of their strongest hidden assets, customer insights. Big Data technologies facilitates Telecom companies to capture, analyses and monetize large volumes of customer information and interaction data across multiple touch points in real time.

With the right information and feedback, telcos can improve profitability by optimizing network services/usage, enhancing customer experience, and improving security. With emergence of technologies like Hadoop framework and NoSQL databases, it became far more affordable to run analytics on large set of data.

The few Use cases for Telcos:

  • Predicting the periods of heaviest network traffic and solution to control congestion
  • Identifying the customers most likely to defect, and targeting steps to prevent churn
  • Identifying the customers most likely to have problems in using services like paying bills, data speed and other function which are accessed through mobile apps.

Predictive analytics can find out these pain points for companies and provide step by step recourse to nullify problems and necessary steps to prevent these to occurring in future.

The objective of this research study is to understand the current big data market in Telecom sector across the world and to estimate the growth rate for the next 5 years. The report covers detailed analysis of companies - value chain, financial performance, forecast, business strategy and SWOT analysis, which are involved in providing big data analytics solutions in the Telecom domain and have presence across different regions of the world.

The global big data market in Telecom services is currently valued at USD XX billion. It is expected that the market will grow at a CAGR of 18% and will reach USD XX billion by the year 2021.

This report concludes by analyzing the industry through PESTLE, porter's 5 forces and SWOT analysis, discusses the challenges faced by the new players entering the industry and present and future trends observed. Strategic recommendations are also discussed separately and in detail for Bigdata service providers and Telcos.

Scope of Report

  • This report provides a detailed view of global big data market with the current market value as well as projections for future market potential and growth rate.
  • This report identifies the need for big data analytics in Telecom Sector together.
  • This report provides detailed information on the value chain as well as the different market segments and their segment wise market share and growth potential.
  • This report provides detailed information on region wise growth forecasts for big data market in Telecom globally by 2021.
  • This report identifies the growth drivers and inhibitors for big data market in Telecom services globally.
  • This study also identifies policies related to big data in financial services market globally.
  • This report identifies various credit, policy and technical risks associated with big data in financial services market globally.
  • This report has detailed profiles of 4 key players in the world in big data analytics industry covering their business strategy, financial performance, future forecasts and SWOT analysis
  • This report covers in detail the competitive landscape in detail of global big data in financial services market.
  • This report provides PESTLE (political, economic, social, technological, legal and environmental) analysis for global big data in financial services market.
  • This report provides porters five forces analysis for global big data in financial services market
  • This report provides SWOT (strength, weaknesses, opportunities, threats) analysis for global big data in financial services market

Companies covered:

  • 1. VMware
  • 2. Cisco Systems
  • 3. SAS
  • 4. International Business Machines (IBM)
Table of Contents
Product Code: NAV0518010

Table of Contents

1. Executive Summary

  • Scope of Report
  • Research Methodology

2. Need for Big Data in Telecom sector

  • Characteristics of Big Data
  • 2.1. Volume
  • 2.2. Variety
  • 2.3. Velocity
  • 2.4. Veracity
  • 2.5. Value

3. Big Data in Telecom sector value chain

  • 3.1. Big Data Consultants
  • 3.2. Infrastructure Providers
  • 3.3. Technology Enablers
  • 3.4. Big Data Analytics Providers
  • 3.5. End Users

4. Global Big Data Market Forecast

  • 4.1. Global Big Data Market in Telecom Industry till 2021
  • 4.2. Asia Pacific Big Data Market in Telecom Industry till 2021
  • 4.3. North America Big Data Market in Telecom Industry till 2021
  • 4.4. Europe Big Data Market in Telecom Industry till 2021
  • 4.5. LASA Big Data Market in Telecom Industry till 2021
  • 4.6. MENA Big Data Market in Telecom Industry till 2021

5. Growth Drivers and Inhibitors for Big Data in Defense and Government sector

  • 5.1. Growth Inhibitors
    • 5.1.1. Data Security
    • 5.1.2. Budgetary requirements
    • 5.1.3. Technical skill requirements
  • 5.2. Growth Drivers
    • 5.2.1. Large variation of Data
    • 5.2.2. Accurate Results
    • 5.2.3. Potential for new insights
    • 5.2.4. Failure of Traditional systems
    • 5.2.5. Reduced cost in future
    • 5.2.6. Competitive advantage

6. Key Players

  • 6.1. VMware
    • 6.1.1. Company Profile
    • 6.1.2. VMware in Big Data Market in Industry & Manufacturing value chain
    • 6.1.3. Financial Performance of VMware
    • 6.1.4. Business Strategy
      • 6.1.4.1. Product Level Business Strategy
      • 6.1.4.2. Service Level Business Strategy
    • 6.1.5. SWOT Analysis for VMware 30
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 6.2. Cisco Systems
    • 6.2.1. Company Profile
    • 6.2.2. Cisco Systems in Big Data Market for Energy and Utility
    • 6.2.3. Financial Performance of Cisco Systems
    • 6.2.4. Business Strategy
      • 6.2.4.1. Product Level Business Strategy
      • 6.2.4.2. Service Level Business Strategy
    • 6.2.5. SWOT Analysis for Cisco Systems
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 6.3. SAS
    • 6.3.1. Company Profile
    • 6.3.2. SAS in Telecom Industry
    • 6.3.3. Financial Performance of SAS
    • 6.3.4. Business Strategy
      • 6.3.4.1. Product Level Business Strategy
      • 6.3.4.2. Service Level Business Strategy
    • 6.3.5. SWOT Analysis for SAS
  • 6.4. International Business Machines(IBM)
    • 6.4.1. Company Profile
    • 6.4.2. IBM Limited in Big Data Value Chain
    • 6.4.3. Financial Performance of IBM Limited
    • 6.4.4. Business Strategy
      • 6.4.4.1. Product Level Business Strategy
      • 6.4.4.2. Service Level Business Strategy
    • 6.4.5. SWOT Analysis for IBM Limited
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats

7. Case Study

  • 7.1. Ufone and IBM
  • 7.2. Vodafone and Argyle Data

8. Analysis Model

  • 8.1. PEST analysis of big data in Telecom sector
    • Political
    • Economical
    • Social
    • Technological
  • 8.2. Porters Five Force Model
  • 8.3. SWOT Analysis

9. Market Opportunities

  • Big Data Opportunities in Telecom Industry
    • 9.1.1. Effective Marketing
    • 9.1.2. Reducing Churn
    • 9.1.3. Customer Satisfaction
    • 9.1.4. Reduce Operational costs
      • Big Data Opportunities in IT sector
    • 9.2.1. Huge Employment Opportunity
    • 9.2.2. Technological Advancement
    • 9.2.3. Improvement in Skills

10. Strategic Recommendations

  • 10.1. Recommendation for Telecom sector
  • 10.2. Recommendation for Big Data suppliers
    • List of Exhibits
    • Notes
    • Company Information

List of Exhibits

  • Exhibit 3: Big Data in Telecom sector Value Chain
  • Exhibit 4.1: Market Share of Various Geographies in Big Data Market in Telecom(in %)
  • Exhibit 4.2: Geography Wise CAGR Growth Forecast for Global Big Data Market in Telecom 2016-21(in %)
  • Exhibit 4.3: Forecast of Global Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 4.4: Forecast of Asia Pacific Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 4.5: Forecast of North America in Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 4.6: Forecast of Europe in Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 4.7: Forecast of South America in Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 4.8: Forecast of Middle East & Africa in Big Data Market in Telecom 2016-21(in US$ billion)
  • Exhibit 5.1: Growth Drivers and Inhibitors of the industry
  • Exhibit 6.1: Company Profile - VMware
  • Exhibit 6.2: Contact Details - VMware
  • Exhibit 6.3: VMware in Big Data Market value chain
  • Exhibit 6.4: VMware Revenue from 2012-13 to 2016-17(in USD)
  • Exhibit 6.5: Year-wise VMware Revenue Growth from 2012-13 to 2016-17(in %)
  • Exhibit 6.6: Estimated Year-wise VMware Revenue from 2016to 2021(in USD)
  • Exhibit 6.7: Estimated Year-wise VMware Revenue from 2017-18 to 2021-22(in %)
  • Exhibit 6.8: Major Products and Services of VMware
  • Exhibit 6.9: SWOT Analysis of VMware
  • Exhibit 6.10: Company Profile - Cisco Systems
  • Exhibit 6.11: Contact Details - Cisco Systems
  • Exhibit 6.12: Cisco Systems in Big Data Market for Energy and Utilities Value Chain
  • Exhibit 6.13: Cisco Systems Revenue from 2011-12 to 2015-16(in USD million)
  • Exhibit 6.14: Year-wise Cisco Systems Revenue Growth from 2011-12 to 2015-16(in %)
  • Exhibit 6.15: Estimated Cisco Systems Revenue from 2015-16 to 2020-21(in million USD)
  • Exhibit 6.16: Estimated Year-wise Cisco Systems Revenue Growth from 2015-16 to 2020-21(in %)
  • Exhibit 6.17: Major Products and Services of Cisco Systems
  • Exhibit 6.18: SWOT Analysis of Cisco Systems
  • Exhibit 6.19: Company Profile - SAS
  • Exhibit 6.20: Contact Details - SAS
  • Exhibit 6.21: SAS in Big Data market in Retail Value Chain
  • Exhibit 6.22: SAS Revenue from 2011-12 to 2015-16(in Billion $)
  • Exhibit 6.23: Year-wise SAS Revenue Growth from 2012-13 to 2015-16(in USD)
  • Exhibit 6.24: Estimated Revenue from 2016-17 to 2020-21(in Billion USD )
  • Exhibit 6.25: Estimated Year-wise Revenue from 2015-16 to 2020-21(in %)
  • Exhibit 6.26: Major Products and Services of SAS
  • Exhibit 6.27: SWOT Analysis of SAS
  • Exhibit 6.28: Company Profile - IBM Limited
  • Exhibit 6.29: Contact Details - IBM Limited
  • Exhibit 6.30: IBM Limited in Big Data Value Chain
  • Exhibit 6.31: IBM Revenue from 2012 to 2016(in USD million)
  • Exhibit 6.32: Year-wise IBM Revenue Growth from 2012 to 2016(in %)
  • Exhibit 6.33: Estimated IBM Revenue from 2016 to 2021(in USD million)
  • Exhibit 6.34: Estimated Year-wise IBM Revenue Growth from 2016 to 2021(in %)
  • Exhibit 6.35: Major Products and Services of IBM Limited
  • Exhibit 6.36: SWOT Analysis of IBM Limited
  • Exhibit 8.1: Porter's Five Forces Model
  • Exhibit 8.2: SWOT Analysis
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