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市場調査レポート

ビッグデータおよび通信分析市場:ビジネスケース・市場分析・予測

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014 - 2019

発行 Mind Commerce 商品コード 282176
出版日 ページ情報 英文 72 Pages
納期: 即日から翌営業日
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ビッグデータおよび通信分析市場:ビジネスケース・市場分析・予測 Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014 - 2019
出版日: 2013年09月16日 ページ情報: 英文 72 Pages
概要

ビッグデータ市場は電気通信分析市場に促進され、2014年から2019年間に約50%のCAGRで成長し、年間収益は2019年までに54億米ドル規模になると予測されています。

当レポートでは、世界のビッグデータおよび電気通信分析市場について、ビジネスケースの調査、アプリケーションの利用例、ベンダー情勢、バリューチェーン分析、ケーススタディおよび定量的評価などを含めた詳細な分析を提供しており、概略以下の構成でお届けいたします。

第1章 イントロダクション

第2章 ビッグデータ技術・ビジネスケース

  • ビッグデータの定義
  • ビッグデータの主な特徴
  • ビッグデータ技術
  • 市場成長促進因子
  • 市場障壁

第3章 ビッグデータの主な投資部門

  • 産業用インターネット&M2M
  • 小売り&サービス
  • メディア
  • ユーティリティ
  • 金融サービス
  • 医療&医薬品
  • 電気通信
  • 政府&国土安全保障
  • その他の部門

第4章 ビッグデータのバリューチェーン

  • ビッグデータのバリューチェーンはどれだけ細分化しているか?
  • データ獲得&プロビジョニング
  • データウェアハウジング&ビジネスインテリジェンス
  • 分析&仮想化
  • アクショニング&ビジネスプロセス管理(BPM)
  • データガバナンス

第5章 電気通信分析におけるビッグデータ

  • 電気通信分析市場の規模
  • 加入者経験の改善
  • スマートネットワークの構築
  • チャーン/リスク削減および新しい収益ストリーム
  • 電気通信分析のケーススタディ

第6章 ビッグデータ市場の主要企業

第7章 市場分析

  • ビッグデータ収益
  • ビッグデータ収益:機能的領域
  • ビッグデータ収益:地域別

図表リスト

目次

Big Data refers to a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. While the presence of such datasets is not something new, the past few years have witnessed immense commercial investments in solutions that address the processing and analysis of Big Data.

Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.

With access to vast amounts of data sets, telecommunications companies are emerging as major proponents of the Big Data movement. Big Data technologies, and in particular their analytics abilities, offer a multitude of benefits to telecom companies including improved subscriber experience, building and maintaining smarter networks, reducing churn, and generation of new revenue streams.

Mind commerce, thus expects the Big Data driven telecom analytics market to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue.

This report provides an in-depth assessment of the global Big Data and telecom analytics markets, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2013 to 2019.

Topics covered in the report include:

  • The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
  • Big Data Technology: A review of the underlying technologies that resolve big data complexities
  • Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
  • The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
  • Big Data in Telco Analytics: How telecom can utilize Big Data technology to reduce churn, optimize their networks, reduce risks and create new revenue streams
  • Telco Case Studies: Case Studies of two major wireless telecom capitalizing on Big Data to reduce churn and improve revenue
  • Vendor Assessment & Key Player Profiles: An assessment of the vendor landscape for leading players within the Big Data market
  • Market Analysis and Forecasts: A global and regional assessment of the market size and forecasts for the Big Data market from 2014 to 2019

Key Findings:

  • Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.
  • Mind Commerce has determined that IBM leads the Big Data market in terms of current investments (from a vendor perspective), with estimated revenue for $1.3 Billion in 2012 for its Big Data services, software and hardware sale
  • Despite challenges such as the lack of clear big data strategies, security concerns and the need for workforce re-skilling, the growth potential of Big Data is unprecedented. Mind Commerce estimates that global spending on Big Data will grow at a CAGR of 48% between 2014 and 2019. Big Data revenues will reach $135 Billion by the end of 2019
  • Big Data technologies, and in particular their analytics abilities offer a multitude of benefits to telecom including improving subscriber experience, building & maintaining smarter networks, reducing churn and even the generation of new revenue streams
  • The Big Data driven telecom analytics market to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue.

Companies in Report:

  • Accenture
  • Adaptive
  • Adobe
  • Amazon
  • Apache Software Foundation
  • APTEAN (Formerly CDC Software)
  • BoA
  • Bristol Myers Squibb
  • Brooks Brothers
  • Centre for Economics and Business Research
  • CIA
  • Cisco Systems
  • Cloud Security Alliance (CSA)
  • Cloudera
  • Dell
  • EMC
  • Facebook
  • Facebook
  • GoodData Corporation
  • Google
  • Google
  • Guavus
  • Hitachi Data Systems
  • Hortonworks
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • JPMC
  • McLaren
  • Microsoft
  • MongoDB (Formerly 10Gen)
  • Morgan Stanley
  • MU Sigma
  • Netapp
  • NSA
  • Opera Solutions
  • Oracle
  • Pentaho
  • Platfora
  • Qliktech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Teradata
  • Think Big Analytics
  • Tidemark Systems
  • T-Mobile
  • TomTom
  • US Xpress
  • Vmware (Part of EMC)
  • Vodafone

Table of Contents

Chapter 1: Introduction

  • 1.1. Executive Summary
  • 1.2. Topics Covered
  • 1.3. Key Findings
  • 1.4. Target Audience
  • 1.5. Companies Mentioned

Chapter 2: Big Data Technology & Business Case

  • 2.1. Defining Big Data
  • 2.2. Key Characteristics of Big Data
    • 2.2.1. Volume
    • 2.2.2. Variety
    • 2.2.3. Velocity
    • 2.2.4. Variability
    • 2.2.5. Complexity
  • 2.3. Big Data Technology
    • 2.3.1. Hadoop
      • 2.3.1.1. MapReduce
      • 2.3.1.2. HDFS
      • 2.3.1.3. Other Apache Projects
    • 2.3.2. NoSQL
      • 2.3.2.1. Hbase
      • 2.3.2.2. Cassandra
      • 2.3.2.3. Mongo DB
      • 2.3.2.4. Riak
      • 2.3.2.5. CouchDB
    • 2.3.3. MPP Databases
    • 2.3.4. Others and Emerging Technologies
      • 2.3.4.1. Storm
      • 2.3.4.2. Drill
      • 2.3.4.3. Dremel
      • 2.3.4.4. SAP HANA
      • 2.3.4.5. Gremlin & Giraph
  • 2.4. Market Drivers
    • 2.4.1. Data Volume & Variety
    • 2.4.2. Increasing Adoption of Big Data by Enterprises & Telcos
    • 2.4.3. Maturation of Big Data Software
    • 2.4.4. Continued Investments in Big Data by Web Giants
  • 2.5. Market Barriers
    • 2.5.1. Privacy & Security: The 'Big' Barrier
    • 2.5.2. Workforce Re-skilling & Organizational Resistance
    • 2.5.3. Lack of Clear Big Data Strategies
    • 2.5.4. Technical Challenges: Scalability & Maintenance

Chapter 3: Key Investment Sectors for Big Data

  • 3.1. Industrial Internet & M2M
    • 3.1.1. Big Data in M2M
    • 3.1.2. Vertical Opportunities
  • 3.2. Retail & Hospitality
    • 3.2.1. Improving Accuracy of Forecasts & Stock Management
    • 3.2.2. Determining Buying Patterns
    • 3.2.3. Hospitality Use Cases
  • 3.3. Media
    • 3.3.1. Social Media
    • 3.3.2. Social Gaming Analytics
    • 3.3.3. Usage of Social Media Analytics by Other Verticals
  • 3.4. Utilities
    • 3.4.1. Analysis of Operational Data
    • 3.4.2. Application Areas for the Future
  • 3.5. Financial Services
    • 3.5.1. Fraud Analysis & Risk Profiling
    • 3.5.2. Merchant-Funded Reward Programs
    • 3.5.3. Customer Segmentation
    • 3.5.4. Insurance Companies
  • 3.6. Healthcare & Pharmaceutical
    • 3.6.1. Drug Development
    • 3.6.2. Medical Data Analytics
    • 3.6.3. Case Study: Identifying Heartbeat Patterns
  • 3.7. Telcos
    • 3.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 3.7.2. Speech Analytics
    • 3.7.3. Other Use Cases
  • 3.8. Government & Homeland Security
    • 3.8.1. Developing New Applications for the Public
    • 3.8.2. Tracking Crime
    • 3.8.3. Intelligence Gathering
    • 3.8.4. Fraud Detection & Revenue Generation
  • 3.9. Other Sectors
    • 3.9.1. Aviation: Air Traffic Control
    • 3.9.2. Transportation & Logistics: Optimizing Fleet Usage
    • 3.9.3. Sports: Real-Time Processing of Statistics

Chapter 4: The Big Data Value Chain

  • 4.1. How Fragmented is the Big Data Value Chain?
  • 4.2. Data Acquisitioning & Provisioning
  • 4.3. Data Warehousing & Business Intelligence
  • 4.4. Analytics & Virtualization
  • 4.5. Actioning & Business Process Management (BPM)
  • 4.6. Data Governance

Chapter 5: Big Data in Telco Analytics

  • 5.1. How Big is the Market for Telco Analytics?
  • 5.2. Improving Subscriber Experience
    • 5.2.1. Generating a Full Spectrum View of the Subscriber
    • 5.2.2. Creating Customized Experiences and Targeted Promotions
    • 5.2.3. Central 'Big Data' Repository: Key to Customer Satisfaction
    • 5.2.4. Reduce Costs and Increase Market Share
  • 5.3. Building Smarter Networks
    • 5.3.1. Understanding the Usage of the Network
    • 5.3.2. The Magic of Analytics: Improving Network Quality and Coverage
    • 5.3.3. Combining Telco Data with Public Data Sets: Real-Time Event Management
    • 5.3.4. Leveraging M2M for Telco Analytics
    • 5.3.5. M2M, Deep Packet Inspection & Big Data: Identifying & Fixing Network Defects
  • 5.4. Churn/Risk Reduction and New Revenue Streams
    • 5.4.1. Predictive Analytics
    • 5.4.2. Identifying Fraud & Bandwidth Theft
    • 5.4.3. Creating New Revenue Streams
  • 5.5. Telco Analytics Case Studies
    • 5.5.1. T-Mobile USA: Churn Reduction by 50%
    • 5.5.2. Vodafone: Using Telco Analytics to Enable Navigation

Chapter 6: Key Players in the Big Data Market

  • 6.1. Vendor Assessment Matrix
  • 6.2. Apache Software Foundation
  • 6.3. Accenture
  • 6.4. Amazon
  • 6.5. APTEAN (Formerly CDC Software)
  • 6.6. Cisco Systems
  • 6.7. Cloudera
  • 6.8. Dell
  • 6.9. EMC
  • 6.10. Facebook
  • 6.11. GoodData Corporation
  • 6.12. Google
  • 6.13. Guavus
  • 6.14. Hitachi Data Systems
  • 6.15. Hortonworks
  • 6.16. HP
  • 6.17. IBM
  • 6.18. Informatica
  • 6.19. Intel
  • 6.20. Jaspersoft
  • 6.21. Microsoft
  • 6.22. MongoDB (Formerly 10Gen)
  • 6.23. MU Sigma
  • 6.24. Netapp
  • 6.25. Opera Solutions
  • 6.26. Oracle
  • 6.27. Pentaho
  • 6.28. Platfora
  • 6.29. Qliktech
  • 6.30. Quantum
  • 6.31. Rackspace
  • 6.32. Revolution Analytics
  • 6.33. Salesforce
  • 6.34. SAP
  • 6.35. SAS Institute
  • 6.36. Sisense
  • 6.37. Software AG/Terracotta
  • 6.38. Splunk
  • 6.39. Sqrrl
  • 6.40. Supermicro
  • 6.41. Tableau Software
  • 6.42. Teradata
  • 6.43. Think Big Analytics
  • 6.44. Tidemark Systems
  • 6.45. VMware (Part of EMC)

Chapter 7: Market Analysis

  • 7.1. Big Data Revenue: 2014 - 2019
  • 7.2. Big Data Revenue by Functional Area: 2014 - 2019
    • 7.2.1. Supply Chain Management
    • 7.2.2. Business Intelligence
    • 7.2.3. Application Infrastructure & Middleware
    • 7.2.4. Data Integration Tools & Data Quality Tools
    • 7.2.5. Database Management Systems
    • 7.2.6. Big Data Social & Content Analytics
    • 7.2.7. Big Data Storage Management
    • 7.2.8. Big Data Professional Services
  • 7.3. Big Data Revenue by Region 2014 - 2019
    • 7.3.1. Asia Pacific
    • 7.3.2. Eastern Europe
    • 7.3.3. Latin & Central America
    • 7.3.4. Middle East & Africa
    • 7.3.5. North America
    • 7.3.6. Western Europe

List of Figures

  • Figure 1: The Big Data Value Chain
  • Figure 2: Telco Analytics Investments Driven by Big Data: 2013 - 2019 ($ Million)
  • Figure 3: Big Data Vendor Ranking Matrix 2013
  • Figure 4: Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 5: Big Data Revenue by Functional Area: 2013 - 2019 ($ Million)
  • Figure 6: Big Data Supply Chain Management Revenue: 2013 - 2019 ($ Million)
  • Figure 7: Big Data Supply Business Intelligence Revenue: 2013 - 2019 ($ Million)
  • Figure 8: Big Data Application Infrastructure & Middleware Revenue: 2013 - 2019 ($ Million)
  • Figure 9: Big Data Integration Tools & Data Quality Tools Revenue: 2013 - 2019 ($ Million)
  • Figure 10: Big Data Database Management Systems Revenue: 2013 - 2019 ($ Million)
  • Figure 11: Big Data Social & Content Analytics Revenue: 2013 - 2019 ($ Million)
  • Figure 12: Big Data Storage Management Revenue: 2013 - 2019 ($ Million)
  • Figure 13: Big Data Professional Services Revenue: 2013 - 2019 ($ Million)
  • Figure 14: Big Data Revenue by Region: 2013 - 2019 ($ Million)
  • Figure 15: Asia Pacific Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 16: Eastern Europe Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 17: Latin & Central America Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 18: Middle East & Africa Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 19: North America Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 20: Western Europe Big Data Revenue: 2013 - 2019 ($ Million)
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