【レポートの概要（一部）】
1 Executive Summary
2 Big Data in Telecom Analytics
2.1 Telecom Analytics Market 2015 – 2020
2.2 Improving Subscriber Experience
2.2.1 Generating a Full Spectrum View of the Subscriber
2.2.2 Creating Customized Experiences and Targeted Promotions
2.2.3 Central Big Data Repository: Key to Customer Satisfaction
2.2.4 Reduce Costs and Increase Market Share
2.3 Building Smarter Networks
2.3.1 Understanding Network Utilization
2.3.2 Improving Network Quality and Coverage
2.3.3 Combining Telecom Data with Public Data Sets: Real-Time Event Management
2.3.4 Leveraging M2M for Telecom Analytics
2.3.5 M2M, Deep Packet Inspection and Big Data: Identifying & Fixing Network Defects
2.4 Churn/Risk Reduction and New Revenue Streams
2.4.1 Predictive Analytics
2.4.2 Identifying Fraud and Bandwidth Theft
2.4.3 Creating New Revenue Streams
2.5 Telecom Analytics Case Studies
2.5.1 T-Mobile USA: Churn Reduction by 50%
2.5.2 Vodafone: Using Telco Analytics to Enable Navigation
2.6 Carriers, Analytics, and Data as a Service (DaaS)
2.6.1 Carrier Data Management Operational Strategies
2.6.2 Network vs. Subscriber Analytics
2.6.3 Data and Analytics Opportunities to Third Parties
2.6.4 Carriers to offer Data as s Service (DaaS) on B2B Basis
2.6.5 DaaS Planning and Strategies
2.6.6 Carrier Monetization of Data with DaaS
2.7 Opportunities for Carriers in Cloud Analytics
2.7.1 Carrier NFV and Cloud Analytics
2.7.2 Carrier Cloud OSS/BSS Analytics
2.7.3 Carrier Cloud Services, Data, and Analytics
2.7.4 Carrier Performance Management and the Cloud Analytics
3 Structured Data in Telecom Analytics
3.1 Telecom Data Sources and Repositories
3.1.1 Subscriber Data
3.1.2 Subscriber Presence and Location Data
3.1.3 Business Data: Toll-free and other Directory Services
3.1.4 Network Data: Deriving Data from Network Operations
3.2 Telecom Data Mining
3.2.1 Data Sources: Rating, Charging, and Billing Examples
3.2.2 Privacy Issues
3.3 Telecom Database Services
3.3.1 Calling Name Identity
3.3.2 Subscriber Data Management (SDM) Services
3.3.3 Other Data-intensive Service Areas
3.3.4 Emerging Service Area: Identity Verification
3.4 Structured Telecom Data Analytics
3.4.1 Dealing with Telecom Data Fragmentation
3.4.2 Deep Packet Inspection
4 Summary and Recommendations
【レポート販売概要】
■ タイトル：通信分析における構造化/非構造化ビックデータ市場■ 英文：Structured and Unstructured (Big) Data in Telecom Analytics
■ 発行日：2015年4月
■ 調査会社：Mind Commerce
■ 商品コード：MIND50404
■ 調査対象地域：グローバル
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