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Structured and Unstructured (Big) Data in Telecom Analytics

Structured and Unstructured (Big) Data in Telecom Analytics

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

Report Benefits:

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)

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


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
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

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