Structured and Unstructured (Big) Data in Telecom Analytics
Overview:
Big Data represents a major inflection point for the ICT and Telecom sectors as it will transform business asset utility and value forever more. It isn't a revolution or a replacement for the current technologies, but it is rather a valued extension of business assets. There is so much discussion about 'Unstructured' Data (Big Data) that some people forget about Structured Data. Structured DB services in telecom are well established from an architecture and service model perspective. Telecom structured data sources are many and varied. Some sources are completely static or semi-static while others are very dynamic in nature.
This report provides the reader with a broad understanding of telecom data (structured and unstructured/big data) and related analytics. The report identifies market drivers and opportunities as well as forecasts for certain key growth areas such as deep packet inspection. The report also evaluates the relationship between Big Data and emerging telecom operational areas including NFV and telecom Cloud analytics. 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:
Big Data represents a major inflection point for the ICT and Telecom sectors as it will transform business asset utility and value forever more. It isn't a revolution or a replacement for the current technologies, but it is rather a valued extension of business assets. There is so much discussion about 'Unstructured' Data (Big Data) that some people forget about Structured Data. Structured DB services in telecom are well established from an architecture and service model perspective. Telecom structured data sources are many and varied. Some sources are completely static or semi-static while others are very dynamic in nature.
This report provides the reader with a broad understanding of telecom data (structured and unstructured/big data) and related analytics. The report identifies market drivers and opportunities as well as forecasts for certain key growth areas such as deep packet inspection. The report also evaluates the relationship between Big Data and emerging telecom operational areas including NFV and telecom Cloud analytics. 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
- Telecom analytics forecast 2015 - 2020
- Understand telecom data sources and mining
- Understand telecom structured data services
- Understand the role and importance of telecom DaaS
- Identify opportunities for third-party telecom data services
- Understand telecom data (structured and unstructured/big)
- Identify opportunities derived from both network and user data
- Recognize the role and importance of Deep Packet Inspection (DPI)
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
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
FIGURES
Figure 1: Telco Analytics Investments Driven by Big Data: 2015 - 2020
Figure 2: Different Data Types within Telco Environment
Figure 3: Presence-enabled Application
Figure 4: Calling Name (CNAM) Service Operation
Figure 5: Subscriber Data Management (SDM) Ecosystem
Figure 6: Data Fragmented across Telecom Databases
Figure 7: Telecom Deep Packet Inspection Revenue 2015 - 2020
Figure 8: Telecom Data and Third-party Applications
Figure 9: Telecom Data, Cloud, and Third-party Applications
Figure 1: Telco Analytics Investments Driven by Big Data: 2015 - 2020
Figure 2: Different Data Types within Telco Environment
Figure 3: Presence-enabled Application
Figure 4: Calling Name (CNAM) Service Operation
Figure 5: Subscriber Data Management (SDM) Ecosystem
Figure 6: Data Fragmented across Telecom Databases
Figure 7: Telecom Deep Packet Inspection Revenue 2015 - 2020
Figure 8: Telecom Data and Third-party Applications
Figure 9: Telecom Data, Cloud, and Third-party Applications
