Big Data in ICT and Telecom: Transforming Industry Verticals 2012 – 2017

Big Data is much more than its technical definition implies: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool.

From a business perspective, Big Data represents a major inflection point for the ICT and Telecom sectors as it will transform business asset utility and value forever more.

Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. However, value realization and the implications for using this data is often little understood and underappreciated.

This research evaluates Big Data challenges including management, mining, and analytics as well as the impact on telecom and ICT systems. This research also analyzes Big Data in key industry verticals including healthcare, government, manufacturing, energy, and transportation. This report also assesses Big Data within government including homeland security, defense, and law enforcement. The report includes a market value assessment for Big Data in telecommunications and industry verticals.

Target Audience:

  • Any governmental agency
  • Fortune 1000 corporations of all types
  • Database and related infrastructure providers
  • ICT infrastructure and service providers of all types
  • Data aggregators, storage and management providers
  • Telecommunications infrastructure and service providers

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.

1.0 Executive Summary
2.0 Introduction
2.1 What Is Big Data
2.2 Why Is Big Data Important
2.3 General Impacts Of Big Data On Telecom And Ict
2.3.1 Significant Impact Areas
3.0 Big Data Technologies And Platforms
3.1 Big Data Techniques
3.2 Mapreduce
3.2.1 Hadoop
3.2.2 Google’s Caffeine
3.2.3 Memcache
3.2.4 Appengine
3.2.5 Redis
3.2.6 Amazon’s Dynamo Database
3.2.7 Mapr
4.0 Data Mining And Management In Telecommunications
4.1.1 Data Mining Tools And Techniques
4.1.2 The Performance Of Data Mining Techniques When Applied To Churn Prediction
4.1.3 Data Mining For Calls
4.1.4 Data Mining In Data Networks
4.1.5 Data Mining In Customers Data And Its Privacy
5.0 Analytics
5.1 Big Data It Analytics
5.1.1 Benefits Of The Big Data It Analytics
6.0 Big Data And Telecom
6.1 4g Drives Greater Usage And More Data
6.2 Big Data And Subscriber Data Management (Sdm)
6.2.1 Subscriber Operational Data
6.3 Big Data And Mobile Commerce/Marketing
6.3.1 Case Study: Big Data Analytics + M-commerce + Social Media= Commercial Success
6.4 Big Data In Near Field Communication
7.0 Big Data And Ict
7.1 Managing Public And Private Data Sources
7.2 Data Management Technologies And Solutions
7.2.1 Data Aggregation
7.2.2 Data Storage
8.0 Big Data In Industry Verticals
8.1 Healthcare
8.2 Government
8.2.1 Homeland Security
8.2.2 Department Of Defense
8.3 Crime Prevention
8.4 Manufacturing
8.5 Energy And Smartgrid
8.6 Transportation
9.0 Big Data Servers In The Cloud
9.1 Cloud-based Data Management
9.2 Case Study: Amazon Web Service And Cloud Infrastructure
9.3 Big Data In The Cloud Applications
9.3.1 Grid Computing
9.3.2 Case Study: How To Use Grid Computing To Save It Costs
9.3.3 Transaction Systems
9.3.4 Deploying Cloud Computing
9.3.5 Cloud Formation
9.4 Cloud Deployment Scenarios
9.4.1 Scenario 1
9.4.2 Scenario 2
9.4.3 Case Study: Cloud And The Return On Investment (Roi)
9.5 Google Business Model And Cloud Strategy
9.5.1 Market Strategy
9.6 Google Apps Marketplace And Mobile Applications
9.6.1 Google Clouds
9.6.2 Economic Of The Cloud And Its Benefits For Google
9.6.3 Swot Analysis For Google Cloud Services
9.6.4 Google Mobile Clouds Potentials In The Mobile Commerce Industry
9.7 Nfc Payment Vs Cloud-based Payments
9.8 Case Study: Big Data In The Cloud And Its Benefits
9.9 Cloud Architectures
9.10 Clouds Technologies
9.11 Cloud-based Service Implementation
9.11.1 For It Providers
9.11.2 For Developers
9.11.3 For Business
9.11.4 Big Data In The Cloud Case Study: Pixar
9.11.5 For Consumer Use
9.11.6 For Government Use
9.12 Anticipated Trends, Applications, And Patents
10.0 Big Data Market Value
10.1 Big Data In Telecommunication
10.2 Big Data In Industry Verticals

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