Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014 - 2019
Big Data refers to a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. While the presence of such datasets is not something new, the past few years have witnessed immense commercial investments in solutions that address the processing and analysis of Big Data.
Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.
With access to vast amounts of data sets, telecommunications companies are emerging as major proponents of the Big Data movement. Big Data technologies, and in particular their analytics abilities, offer a multitude of benefits to telecom companies including improved subscriber experience, building and maintaining smarter networks, reducing churn, and generation of new revenue streams.
Mind commerce, thus expects the Big Data driven telecom analytics market to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue.
This report provides an in-depth assessment of the global Big Data and telecom analytics markets, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2013 to 2019.
Topics covered in the report include:
- The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
- Big Data Technology: A review of the underlying technologies that resolve big data complexities
- Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
- The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
- Big Data in Telco Analytics: How telecom can utilize Big Data technology to reduce churn, optimize their networks, reduce risks and create new revenue streams
- Telco Case Studies: Case Studies of two major wireless telecom capitalizing on Big Data to reduce churn and improve revenue
- Vendor Assessment & Key Player Profiles: An assessment of the vendor landscape for leading players within the Big Data market
- Market Analysis and Forecasts: A global and regional assessment of the market size and forecasts for the Big Data market from 2014 to 2019
Companies in Report:
- Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.
- Mind Commerce has determined that IBM leads the Big Data market in terms of current investments (from a vendor perspective), with estimated revenue for $1.3 Billion in 2012 for its Big Data services, software and hardware sale
- Despite challenges such as the lack of clear big data strategies, security concerns and the need for workforce re-skilling, the growth potential of Big Data is unprecedented. Mind Commerce estimates that global spending on Big Data will grow at a CAGR of 48% between 2014 and 2019. Big Data revenues will reach $135 Billion by the end of 2019
- Big Data technologies, and in particular their analytics abilities offer a multitude of benefits to telecom including improving subscriber experience, building & maintaining smarter networks, reducing churn and even the generation of new revenue streams
- The Big Data driven telecom analytics market to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue.
- Apache Software Foundation
- APTEAN (Formerly CDC Software)
- Bristol Myers Squibb
- Brooks Brothers
- Centre for Economics and Business Research
- Cisco Systems
- Cloud Security Alliance (CSA)
- GoodData Corporation
- Hitachi Data Systems
- MongoDB (Formerly 10Gen)
- Morgan Stanley
- MU Sigma
- Opera Solutions
- Revolution Analytics
- SAS Institute
- Software AG/Terracotta
- Tableau Software
- Think Big Analytics
- Tidemark Systems
- US Xpress
- VMware (Part of EMC)
- Investment Firms
- Media Companies
- Utilities Companies
- Financial Institutions
- Application Developers
- Government Organizations
- Retail & Hospitality Companies
- Other Vertical Industry Players
- Analytics and Data Reporting Companies
- Healthcare Service Providers & Institutions
- Fixed and Mobile Telecom service providers
- Big Data Technology/Solution (Infrastructure, Software, Service) Vendors
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.
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.