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市場調查報告書

通訊業的結構化資料、巨量資料、分析功能的市場、商務案例、市場分析、未來預測 (2015∼2020年)

Market for Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 - 2020

出版商 Mind Commerce 商品編碼 325991
出版日期 內容資訊 英文 153 Pages
商品交期: 最快1-2個工作天內
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通訊業的結構化資料、巨量資料、分析功能的市場、商務案例、市場分析、未來預測 (2015∼2020年) Market for Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 - 2020
出版日期: 2015年03月16日 內容資訊: 英文 153 Pages
簡介

通訊產業投入投資分析工具服務巨大的資金,打算有效利用傳統結構化資料和非結構型資料 (巨量資料)。目標雖然依各電信業者而不同,不過,商業智慧 (BI) 功能的改善,及提高客戶服務、運用效率等也有幾個共同點。各家電信業者積極努力理解關於自家公司資料資產的更佳利用方法,那在B2B層級產生出新產品與服務的契機。

本報告提供全球通訊業的巨量資料及分析功能的有效利用方法相關分析、實際的利用案例,及相關供應商的環境、價值鏈 (相關市場) 概況、今後的市場趨勢預測等調查、考察。

第1章 簡介

第2章 巨量資料的技術和商務案例

  • 結構化 vs. 非結構化資料
  • 巨量資料定義
  • 巨量資料的主要特徵
  • 透過偵測設備、社群系統採集資料
  • 巨量資料技術
    • Hadoop
    • NoSQL
    • MPP Databases
    • 其他及新技術
  • 通訊企業的巨量資料、分析功能的商務推動因素
    • 行動寬頻的持續擴大
    • 新種服務供應商的競爭
    • 新技術的投資
    • 新KPI的必要性
    • 人工智能與機器學習
  • 市場障礙
    • 隱私和安全性:「大」的障礙
    • 勞工的再教育與有組織的抵抗
    • 缺乏明確的策略
    • 技術課題:規模擴充性與整備

第3章 巨量資料投資的主要領域

  • 產業用網際網路與M2M (機器間通訊)
  • 零售業、旅館產業
  • 媒體
  • 公共事業產業
  • 金融服務
  • 醫療、製藥產業
  • 通訊企業
  • 政府、國防安全保障 (HLS) 部門
  • 其他的部門

第4章 巨量資料的價值鏈

  • 巨量資料的價值鏈細分化
  • 資料的獲得和提供
  • 資料儲倉和商業智慧 (BI)
  • 分析功能和虛擬化
  • 實踐和BPM (商務流程管理)
  • 資料的管治

第5章 在通訊分析中的巨量資料

  • 通訊分析市場未來展望 (今後6年份)
  • 用戶經驗的改善
  • 更智慧的網路建立
  • 客戶流失/風險削減新的收益來源
  • 通訊分析的案例研究
  • 通訊電信業者和分析功能和「Data-As-A-Service資料服務」 (DaaS)
  • 雲端分析上的通訊電信業者的市場機會

第6章 通訊分析的結構化資料

  • 通訊企業情況的資料來源和儲存庫
    • 用戶資料
    • 用戶的存在感、位置資料
    • 商務資料:免費通話以及其他的名錄服務
    • 來自網路數據:網路運用的資料提供
  • 通訊企業用的資料探勘
    • 資料來源:評估、收費的案例
    • 隱私的問題
  • 通訊企業用的資料庫、服務
    • 電話號碼的認證
    • SDM服務
    • 其他資料集中的服務領域
    • 新的服務領域:ID認證
  • 通訊企業情況的結構化資料分析
    • 通訊資料細分化的應對
    • 詳細的封包檢驗

第7章 巨量資料市場主要企業

  • Vendor Assessment Matrix
  • Apache Software Foundation
  • Accenture
  • Amazon
  • APTEAN (舊CDC Software)
  • Cisco Systems
  • Cloudera
  • Dell
  • EMC
  • Facebook
  • GoodData Corporation
  • Google
  • Guavus
  • Hitachi Data Systems
  • Hortonworks
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • Microsoft
  • MongoDB (舊10Gen)
  • MU Sigma
  • Netapp
  • Opera Solutions
  • Oracle
  • ParStream
  • 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
  • VMware (EMC的一部份)

第8章 市場分析

  • 通訊業的結構化資訊服務的市場
  • 通訊業的非結構化資料 (巨量資料) 服務的市場
    • 巨量資料、服務的市場收益額 (今後6年份)
    • 巨量資料、服務的市場收益額:各功能領域 (今後6年份)
    • 巨量資料、服務的市場收益額:各地區 (今後6年份)

第9章 摘要與建議

  • 通訊電信業者而言的主要的成功因素
    • 即時、資料的有效利用
    • 「分析功能和BI不同」的認識
    • 提供資料發現服務
    • 提供財團客戶巨量資料的分析功能
  • 生態系統內部中間企業所扮演的角色
    • 雲端和巨量資料的中介
    • 安全、通訊、收費、付款
    • 「Data-As-A-Service資料服務」 (DaaS) 的情況

第10章 附錄:巨量資料分析相關理解

圖表一覽

目錄

The telecommunications industry is investing heavily in developing the analytical tools and services to take advantage of both their traditional structured data and unstructured (big) data resources. The goals of each carrier program vary, but share some commonalities including the desire to improve business intelligence gathering, customer care and operations. Carriers are also working diligently to better understand how to monetize data assets, which is often manifest in new products and services at the business-to-business (B2B) level.

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 2015 to 2020. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

  • Telecom network operators
  • Telecom infrastructure suppliers
  • Big Data and analytics companies
  • Data as a Service (DaaS) companies
  • Cloud-based service providers of all types
  • Data processing and management companies
  • Application Programmer Interface (API) companies
  • Public investment organizations including investment banks
  • Private investment including hedge funds and private equity

Report Benefits:

  • Forecasts telecom related Big Data from 2015 to 2020
  • Understand the emerging need for Big Data mediation
  • Identify telecom structured data services and solutions
  • Identify sources of data from next generation applications
  • Understand unstructured (Big) data systems and solutions
  • Learn about sources of data in telecom systems and processes
  • Understand the role and importance of deep packet inspection

Table of Contents

1 Introduction

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

2 Big Data Technology and Business Case

  • 2.1 Structured vs. Unstructured Data
    • 2.1.1 Structured Database Services in Telecom
    • 2.1.2 Unstructured Data from Apps and Databases in Telecom
    • 2.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
    • 2.2 Defining Big Data
  • 2.3 Key Characteristics of Big Data
    • 2.3.1 Volume
    • 2.3.2 Variety
    • 2.3.3 Velocity
    • 2.3.4 Variability
    • 2.3.5 Complexity
  • 2.4 Capturing Data through Detection and Social Systems
    • 2.4.1 Data in Social Systems
    • 2.4.2 Detection and Sensors
    • 2.4.3 Sensors in the Consumer Sector
    • 2.4.4 Sensors in Industry
  • 2.5 Big Data Technology
    • 2.5.1 Hadoop
      • 2.5.1.1 MapReduce
      • 2.5.1.2 HDFS
      • 2.5.1.3 Other Apache Projects
    • 2.5.2 NoSQL
      • 2.5.2.1 Hbase
      • 2.5.2.2 Cassandra
      • 2.5.2.3 Mongo DB
      • 2.5.2.4 Riak
      • 2.5.2.5 CouchDB
    • 2.5.3 MPP Databases
    • 2.5.4 Others and Emerging Technologies
      • 2.5.4.1 Storm
      • 2.5.4.2 Drill
      • 2.5.4.3 Dremel
      • 2.5.4.4 SAP HANA
      • 2.5.4.5 Gremlin & Giraph
  • 2.6 Business Drivers for Telecom Big Data and Analytics
    • 2.6.1 Continued Growth of Mobile Broadband
    • 2.6.2 Competition from New Types of Service Providers
    • 2.6.3 New Technology Investment
    • 2.6.4 Need for New KPIs
    • 2.6.5 Artificial Intelligence and Machine Learning
  • 2.7 Market Barriers
    • 2.7.1 Privacy and Security: The 'Big' Barrier
    • 2.7.2 Workforce Re-skilling and Organizational Resistance
    • 2.7.3 Lack of Clear Big Data Strategies
    • 2.7.4 Technical Challenges: Scalability and Maintenance

3 Key Big Data Investment Sectors

  • 3.1 Industrial Internet and M2M
    • 3.1.1 Big Data in M2M
    • 3.1.2 Vertical Opportunities
  • 3.2 Retail and Hospitality
    • 3.2.1 Improving Accuracy of Forecasts and 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 50
    • 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 and Pharmaceutical
    • 3.6.1 Drug Development
    • 3.6.2 Medical Data Analytics
    • 3.6.3 Case Study: Identifying Heartbeat Patterns
  • 3.7 Telecom Companies
    • 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 and 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 and Revenue Generation
  • 3.9 Other Sectors
    • 3.9.1 Aviation: Air Traffic Control
    • 3.9.2 Transportation and Logistics: Optimizing Fleet Usage
    • 3.9.3 Sports: Real-Time Processing of Statistics

4 The Big Data Value Chain

  • 4.1 Fragmentation in the Big Data Value Chain
  • 4.2 Data Acquisitioning and Provisioning
  • 4.3 Data Warehousing and Business Intelligence
  • 4.4 Analytics and Virtualization
  • 4.5 Actioning and Business Process Management (BPM)
  • 4.6 Data Governance

5 Big Data in Telecom Analytics

  • 5.1 Telecom Analytics Market 2015 - 2020
  • 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 Network Utilization
    • 5.3.2 Improving Network Quality and Coverage
    • 5.3.3 Combining Telecom Data with Public Data Sets: Real-Time Event Management
    • 5.3.4 Leveraging M2M for Telecom Analytics
    • 5.3.5 M2M, Deep Packet Inspection and 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 and Bandwidth Theft
    • 5.4.3 Creating New Revenue Streams
  • 5.5 Telecom Analytics Case Studies
    • 5.5.1 T-Mobile USA: Churn Reduction by 50%
    • 5.5.2 Vodafone: Using Telco Analytics to Enable Navigation
  • 5.6 Carriers, Analytics, and Data as a Service (DaaS)
    • 5.6.1 Carrier Data Management Operational Strategies
    • 5.6.2 Network vs. Subscriber Analytics
    • 5.6.3 Data and Analytics Opportunities to Third Parties
    • 5.6.4 Carriers to offer Data as s Service (DaaS) on B2B Basis
    • 5.6.5 DaaS Planning and Strategies
    • 5.6.6 Carrier Monetization of Data with DaaS
  • 5.7 Opportunities for Carriers in Cloud Analytics
    • 5.7.1 Carrier NFV and Cloud Analytics
    • 5.7.2 Carrier Cloud OSS/BSS Analytics
    • 5.7.3 Carrier Cloud Services, Data, and Analytics
    • 5.7.4 Carrier Performance Management and the Cloud Analytics

6 Structured Data in Telecom Analytics

  • 6.1 Telecom Data Sources and Repositories
    • 6.1.1 Subscriber Data
    • 6.1.2 Subscriber Presence and Location Data
    • 6.1.3 Business Data: Toll-free and other Directory Services
    • 6.1.4 Network Data: Deriving Data from Network Operations
  • 6.2 Telecom Data Mining
    • 6.2.1 Data Sources: Rating, Charging, and Billing Examples
    • 6.2.2 Privacy Issues
  • 6.3 Telecom Database Services
    • 6.3.1 Calling Name Identity
    • 6.3.2 Subscriber Data Management (SDM) Services
    • 6.3.3 Other Data-intensive Service Areas
    • 6.3.4 Emerging Service Area: Identity Verification
  • 6.4 Structured Telecom Data Analytics
    • 6.4.1 Dealing with Telecom Data Fragmentation
    • 6.4.2 Deep Packet Inspection

7 Key Players in the Big Data Market

  • 7.1 Vendor Assessment Matrix
  • 7.2 Apache Software Foundation
  • 7.3 Accenture
  • 7.4 Amazon
  • 7.5 APTEAN (Formerly CDC Software)
  • 7.6 Cisco Systems
  • 7.7 Cloudera
  • 7.8 Dell
  • 7.9 EMC
  • 7.10 Facebook
  • 7.11 GoodData Corporation
  • 7.12 Google
  • 7.13 Guavus
  • 7.14 Hitachi Data Systems
  • 7.15 Hortonworks
  • 7.16 HP
  • 7.17 IBM
  • 7.18 Informatica
  • 7.19 Intel
  • 7.20 Jaspersoft
  • 7.21 Microsoft
  • 7.22 MongoDB (Formerly 10Gen)
  • 7.23 MU Sigma
  • 7.24 Netapp
  • 7.25 Opera Solutions
  • 7.26 Oracle
  • 7.27 ParStream
  • 7.28 Pentaho
  • 7.29 Platfora
  • 7.30 Qliktech
  • 7.31 Quantum
  • 7.32 Rackspace
  • 7.33 Revolution Analytics
  • 7.34 Salesforce
  • 7.35 SAP
  • 7.36 SAS Institute
  • 7.37 Sisense
  • 7.38 Software AG/Terracotta
  • 7.39 Splunk
  • 7.40 Sqrrl
  • 7.41 Supermicro
  • 7.42 Tableau Software
  • 7.43 Teradata
  • 7.44 Think Big Analytics
  • 7.45 Tidemark Systems
  • 7.46 VMware (Part of EMC)

8 Market Analysis

  • 8.1 Market for Structured Telecom Data Services
  • 8.2 Market for Unstructured (Big) Data Services
    • 8.2.1 Big Data Revenue 2015 - 2020
    • 8.2.2 Big Data Revenue by Functional Area 2015 - 2020
    • 8.2.3 Big Data Revenue by Region 2015 - 2020

9 Summary and Recommendations

  • 9.1 Key Success Factors for Carriers
    • 9.1.1 Leverage Real-time Data
    • 9.1.2 Recognize that Analytics is Not Business Intelligence
    • 9.1.3 Provide Data Discovery Services
    • 9.1.4 Provide Big Data and Analytics to Enterprise Customers
  • 9.2 The Role of Intermediaries in the Ecosystem
    • 9.2.1 Cloud and Big Data Intermediation
    • 9.2.2 Security, Communications, Billing, and Settlement
    • 9.2.3 The Case for Data as a Service (DaaS)

10 Appendix: Understanding Big Data Analytics

  • 10.1 What is Big Data Analytics?
  • 10.2 The Importance of Big Data Analytics
  • 10.3 Reactive vs. Proactive Analytics
  • 10.4 Technology and Implementation Approaches
    • 10.4.1 Grid Computing
    • 10.4.2 In-Database processing
    • 10.4.3 In-Memory Analytics
    • 10.4.4 Data Mining
    • 10.4.5 Predictive Analytics
    • 10.4.6 Natural Language Processing
    • 10.4.7 Text Analytics
    • 10.4.8 Visual Analytics
    • 10.4.9 Association Rule Learning
    • 10.4.10 Classification Tree Analysis
    • 10.4.11 Machine Learning
      • 10.4.11.1 Neural Networks
      • 10.4.11.2 Multilayer Perceptron (MLP)
      • 10.4.11.3 Radial Basis Functions
      • 10.4.11.4 Support Vector Machines
      • 10.4.11.5 Naïve Bayes
      • 10.4.11.6 k-nearest Neighbours
      • 10.4.11.7 Geospatial Predictive Modelling
    • 10.4.12 Regression Analysis
    • 10.4.13 Social Network Analysis

Figures

  • Figure 1: Hybrid Data in Next Generation Applications
  • Figure 2: Big Data Components
  • Figure 3: Big Data Sources
  • Figure 4: Capturing Data from Detection Systems and Sensors
  • Figure 5: Capturing Data across Sectors
  • Figure 6: AI Structure
  • Figure 7: The Big Data Value Chain
  • Figure 8: Telco Analytics Investments Driven by Big Data: 2015 - 2020
  • Figure 9: Different Data Types within Telco Environment
  • Figure 10: Presence-enabled Application
  • Figure 11: Calling Name (CNAM) Service Operation
  • Figure 12: Subscriber Data Management (SDM) Ecosystem
  • Figure 13: Data Fragmented across Telecom Databases
  • Figure 14: Telecom Deep Packet Inspection Revenue 2015 - 2020
  • Figure 15: Big Data Vendor Ranking Matrix
  • Figure 16: Unified Communications Incoming Call Routing
  • Figure 17: Network Level Outbound Call Management
  • Figure 18: Big Data Revenue: 2015 - 2020
  • Figure 19: Big Data Revenue by Functional Area: 2015 - 2020
  • Figure 20: Big Data Revenue by Region: 2015 - 2020
  • Figure 21: Data Mediation for Structured and Unstructured Data
  • Figure 21: Cloud and Big Data Intermediation
  • Figure 22: Data Security, Billing and Settlement
  • Figure 24: Big Data as a Service (BDaaS)
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