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Carrier Big Data, Deep Packet Inspection, Analytics, and Data as a Service 2015 - 2020

Carrier Big Data, Deep Packet Inspection, Analytics, and Data as a Service 2015 - 2020

There are many opportunities for communication service providers in the area of leveraging structured and unstructured data. These opportunities range from leveraging data for new value added services to leveraging the data itself through the use of deep packet inspection to gain inferences and knowledge about customer behavior. These insights can be used to develop new services as well as optimize quality of experience for the customer base.

Deep Packet Inspection (DPI) represents a form packet filtering that examines the data part (and possibly also the header) of a packet as it passes an inspection point. Data as a Service (DaaS) is defined as any service offered wherein users can access vendor provided databases or host their own databases on vendor managed systems.

This report evaluates Big Data, Analytics, and DPI opportunities and provides a forecast for 2015 through 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:

DaaS service providers
Telecom service providers
Wireless device manufacturers
Big Data and Analytics companies
Wireless infrastructure companies
Telecom managed service companies
Cloud infrastructure and XaaS providers
Intermediaries and mediation companies

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 Deep Packet Inspection (DPI)
2.1 DPI Drivers
2.2 Deep Packet Inspection Forecast
3 Carriers, Analytics, and Data as a Service (DaaS)
3.1 Carrier Data Management Operational Strategies
3.2 Network vs. Subscriber Analytics
3.3 Data and Analytics Opportunities to Third Parties
3.4 Carriers to offer Data as s Service (DaaS) on B2B Basis
3.5 DaaS Planning and Strategies
3.6 Carrier Monetization of Data with DaaS
4 Carrier Strategies
4.1 Leverage Real-time Data
4.2 Focus on Analytics and Business Intelligence
4.3 Provide Data Discovery Services
4.4 Provide Big Data and Analytics to Enterprise Customers
Figures
Figure 1: Telecom Deep Packet Inspection Revenue 2015 - 2020
Figure 2: Different Data Types within Telco Environment

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