Telecom big data spending includes distributed storage and computing Hadoop (and Spark) clusters, HDFS file systems, SQL and NoSQL software database frameworks, and other operational software. Telecom analytics software, such as for revenue assurance, business intelligence, strategic marketing, and network performance, are considered separately. The evolution from non-machine learning based descriptive analytics to machine learning driven predictive analytics is also considered. Telecom data meets the fundamental 3Vs criteria of big data: velocity, variety, and volume, and should be supported with a big data infrastructure (processing, storage, and analytics) for both real-time and offline analysis.
According to this study, over the next five years the Big Data & Machine Learning in Telecom market will register a xx% CAGR in terms of revenue, the global market size will reach US$ xx million by 2023, from US$ xx million in 2017. In particular, this report presents the global revenue market share of key companies in Big Data & Machine Learning in Telecom business, shared in Chapter 3.
This report presents a comprehensive overview, market shares, and growth opportunities of Big Data & Machine Learning in Telecom market by product type, application, key manufacturers and key regions and countries.
This study considers the Big Data & Machine Learning in Telecom value generated from the sales of the following segments:
Segmentation by product type:
Segmentation by application:
This report also splits the market by region:
The report also presents the market competition landscape and a corresponding detailed analysis of the major vendor/manufacturers in the market. The key manufacturers covered in this report:
In addition, this report discusses the key drivers influencing market growth, opportunities, the challenges and the risks faced by key players and the market as a whole. It also analyzes key emerging trends and their impact on present and future development.
To study and analyze the global Big Data & Machine Learning in Telecom market size by key regions/countries, product type and application.
To understand the structure of Big Data & Machine Learning in Telecom market by identifying its various subsegments.
Focuses on the key global Big Data & Machine Learning in Telecom players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in next few years.
To analyze the Big Data & Machine Learning in Telecom with respect to individual growth trends, future prospects, and their contribution to the total market.
To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
To project the size of Big Data & Machine Learning in Telecom submarkets, with respect to key regions (along with their respective key countries).
To analyze competitive developments such as expansions, agreements, new product launches and acquisitions in the market.
To strategically profile the key players and comprehensively analyze their growth strategies.