The global Big Data & Machine Learning in Telecom market size was valued at USD million in 2023 and is forecast to a readjusted size of USD million by 2030 with a CAGR of % during review period.
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.
The Global Mobile Economy Development Report 2023 released by GSMA Intelligence pointed out that by the end of 2022, the number of global mobile users would exceed 5.4 billion. The mobile ecosystem supports 16 million jobs directly and 12 million jobs indirectly.
According to our Communications Research Centre, in 2022, the global communication equipment was valued at US$ 100 billion. The U.S. and China are powerhouses in the manufacture of communications equipment. According to data from the Ministry of Industry and Information Technology of China, the cumulative revenue of telecommunications services in 2022 was 楼1.58 trillion, an increase of 8% over the previous year. The total amount of telecommunications business calculated at the price of the previous year reached 楼1.75 trillion, a year-on-year increase of 21.3%. In the same year, the fixed Internet broadband access business revenue was 楼240.2 billion, an increase of 7.1% over the previous year, and its proportion in the telecommunications business revenue decreased from 15.3% in the previous year to 15.2%, driving the telecommunications business revenue to increase by 1.1 percentage points.
This report includes an overview of the development of the Big Data & Machine Learning in Telecom industry chain, the market status of Processing (Descriptive Analytics, Predictive Analytics), Storage (Descriptive Analytics, Predictive Analytics), and key enterprises in developed and developing market, and analysed the cutting-edge technology, patent, hot applications and market trends of Big Data & Machine Learning in Telecom.
Regionally, the report analyzes the Big Data & Machine Learning in Telecom markets in key regions. North America and Europe are experiencing steady growth, driven by government initiatives and increasing consumer awareness. Asia-Pacific, particularly China, leads the global Big Data & Machine Learning in Telecom market, with robust domestic demand, supportive policies, and a strong manufacturing base.
Key Features:
The report presents comprehensive understanding of the Big Data & Machine Learning in Telecom market. It provides a holistic view of the industry, as well as detailed insights into individual components and stakeholders. The report analysis market dynamics, trends, challenges, and opportunities within the Big Data & Machine Learning in Telecom industry.
The report involves analyzing the market at a macro level:
麻豆原创 Sizing and Segmentation: Report collect data on the overall market size, including the revenue generated, and market share of different by Type (e.g., Descriptive Analytics, Predictive Analytics).
Industry Analysis: Report analyse the broader industry trends, such as government policies and regulations, technological advancements, consumer preferences, and market dynamics. This analysis helps in understanding the key drivers and challenges influencing the Big Data & Machine Learning in Telecom market.
Regional Analysis: The report involves examining the Big Data & Machine Learning in Telecom market at a regional or national level. Report analyses regional factors such as government incentives, infrastructure development, economic conditions, and consumer behaviour to identify variations and opportunities within different markets.
麻豆原创 Projections: Report covers the gathered data and analysis to make future projections and forecasts for the Big Data & Machine Learning in Telecom market. This may include estimating market growth rates, predicting market demand, and identifying emerging trends.
The report also involves a more granular approach to Big Data & Machine Learning in Telecom:
Company Analysis: Report covers individual Big Data & Machine Learning in Telecom players, suppliers, and other relevant industry players. This analysis includes studying their financial performance, market positioning, product portfolios, partnerships, and strategies.
Consumer Analysis: Report covers data on consumer behaviour, preferences, and attitudes towards Big Data & Machine Learning in Telecom This may involve surveys, interviews, and analysis of consumer reviews and feedback from different by Application (Processing, Storage).
Technology Analysis: Report covers specific technologies relevant to Big Data & Machine Learning in Telecom. It assesses the current state, advancements, and potential future developments in Big Data & Machine Learning in Telecom areas.
Competitive Landscape: By analyzing individual companies, suppliers, and consumers, the report present insights into the competitive landscape of the Big Data & Machine Learning in Telecom market. This analysis helps understand market share, competitive advantages, and potential areas for differentiation among industry players.
麻豆原创 Validation: The report involves validating findings and projections through primary research, such as surveys, interviews, and focus groups.
麻豆原创 Segmentation
Big Data & Machine Learning in Telecom market is split by Type and by Application. For the period 2019-2030, the growth among segments provides accurate calculations and forecasts for consumption value by Type, and by Application in terms of value.
麻豆原创 segment by Type
Descriptive Analytics
Predictive Analytics
Machine Learning
Feature Engineering
麻豆原创 segment by Application
Processing
Storage
Analyzing
麻豆原创 segment by players, this report covers
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
Google
AT&T
Apple
Amazon
Microsoft
麻豆原创 segment by regions, regional analysis covers
North America (United States, Canada, and Mexico)
Europe (Germany, France, UK, Russia, Italy, and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Australia and Rest of Asia-Pacific)
South America (Brazil, Argentina and Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Big Data & Machine Learning in Telecom product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Big Data & Machine Learning in Telecom, with revenue, gross margin and global market share of Big Data & Machine Learning in Telecom from 2019 to 2024.
Chapter 3, the Big Data & Machine Learning in Telecom competitive situation, revenue and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and application, with consumption value and growth rate by Type, application, from 2019 to 2030.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2019 to 2024.and Big Data & Machine Learning in Telecom market forecast, by regions, type and application, with consumption value, from 2025 to 2030.
Chapter 11, market dynamics, drivers, restraints, trends and Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Big Data & Machine Learning in Telecom.
Chapter 13, to describe Big Data & Machine Learning in Telecom research findings and conclusion.
Please Note - This is an on demand report and will be delivered in 2 business days (48 Hours) post payment.
1 麻豆原创 Overview
1.1 Product Overview and Scope of Big Data & Machine Learning in Telecom
1.2 麻豆原创 Estimation Caveats and Base Year
1.3 Classification of Big Data & Machine Learning in Telecom by Type
1.3.1 Overview: Global Big Data & Machine Learning in Telecom 麻豆原创 Size by Type: 2019 Versus 2023 Versus 2030
1.3.2 Global Big Data & Machine Learning in Telecom Consumption Value 麻豆原创 Share by Type in 2023
1.3.3 Descriptive Analytics
1.3.4 Predictive Analytics
1.3.5 Machine Learning
1.3.6 Feature Engineering
1.4 Global Big Data & Machine Learning in Telecom 麻豆原创 by Application
1.4.1 Overview: Global Big Data & Machine Learning in Telecom 麻豆原创 Size by Application: 2019 Versus 2023 Versus 2030
1.4.2 Processing
1.4.3 Storage
1.4.4 Analyzing
1.5 Global Big Data & Machine Learning in Telecom 麻豆原创 Size & Forecast
1.6 Global Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast by Region
1.6.1 Global Big Data & Machine Learning in Telecom 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
1.6.2 Global Big Data & Machine Learning in Telecom 麻豆原创 Size by Region, (2019-2030)
1.6.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Size and Prospect (2019-2030)
1.6.4 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size and Prospect (2019-2030)
1.6.5 Asia-Pacific Big Data & Machine Learning in Telecom 麻豆原创 Size and Prospect (2019-2030)
1.6.6 South America Big Data & Machine Learning in Telecom 麻豆原创 Size and Prospect (2019-2030)
1.6.7 Middle East and Africa Big Data & Machine Learning in Telecom 麻豆原创 Size and Prospect (2019-2030)
2 Company Profiles
2.1 Allot
2.1.1 Allot Details
2.1.2 Allot Major Business
2.1.3 Allot Big Data & Machine Learning in Telecom Product and Solutions
2.1.4 Allot Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.1.5 Allot Recent Developments and Future Plans
2.2 Argyle data
2.2.1 Argyle data Details
2.2.2 Argyle data Major Business
2.2.3 Argyle data Big Data & Machine Learning in Telecom Product and Solutions
2.2.4 Argyle data Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.2.5 Argyle data Recent Developments and Future Plans
2.3 Ericsson
2.3.1 Ericsson Details
2.3.2 Ericsson Major Business
2.3.3 Ericsson Big Data & Machine Learning in Telecom Product and Solutions
2.3.4 Ericsson Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.3.5 Ericsson Recent Developments and Future Plans
2.4 Guavus
2.4.1 Guavus Details
2.4.2 Guavus Major Business
2.4.3 Guavus Big Data & Machine Learning in Telecom Product and Solutions
2.4.4 Guavus Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.4.5 Guavus Recent Developments and Future Plans
2.5 HUAWEI
2.5.1 HUAWEI Details
2.5.2 HUAWEI Major Business
2.5.3 HUAWEI Big Data & Machine Learning in Telecom Product and Solutions
2.5.4 HUAWEI Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.5.5 HUAWEI Recent Developments and Future Plans
2.6 Intel
2.6.1 Intel Details
2.6.2 Intel Major Business
2.6.3 Intel Big Data & Machine Learning in Telecom Product and Solutions
2.6.4 Intel Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.6.5 Intel Recent Developments and Future Plans
2.7 NOKIA
2.7.1 NOKIA Details
2.7.2 NOKIA Major Business
2.7.3 NOKIA Big Data & Machine Learning in Telecom Product and Solutions
2.7.4 NOKIA Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.7.5 NOKIA Recent Developments and Future Plans
2.8 Openwave mobility
2.8.1 Openwave mobility Details
2.8.2 Openwave mobility Major Business
2.8.3 Openwave mobility Big Data & Machine Learning in Telecom Product and Solutions
2.8.4 Openwave mobility Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.8.5 Openwave mobility Recent Developments and Future Plans
2.9 Procera networks
2.9.1 Procera networks Details
2.9.2 Procera networks Major Business
2.9.3 Procera networks Big Data & Machine Learning in Telecom Product and Solutions
2.9.4 Procera networks Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.9.5 Procera networks Recent Developments and Future Plans
2.10 Qualcomm
2.10.1 Qualcomm Details
2.10.2 Qualcomm Major Business
2.10.3 Qualcomm Big Data & Machine Learning in Telecom Product and Solutions
2.10.4 Qualcomm Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.10.5 Qualcomm Recent Developments and Future Plans
2.11 ZTE
2.11.1 ZTE Details
2.11.2 ZTE Major Business
2.11.3 ZTE Big Data & Machine Learning in Telecom Product and Solutions
2.11.4 ZTE Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.11.5 ZTE Recent Developments and Future Plans
2.12 Google
2.12.1 Google Details
2.12.2 Google Major Business
2.12.3 Google Big Data & Machine Learning in Telecom Product and Solutions
2.12.4 Google Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.12.5 Google Recent Developments and Future Plans
2.13 AT&T
2.13.1 AT&T Details
2.13.2 AT&T Major Business
2.13.3 AT&T Big Data & Machine Learning in Telecom Product and Solutions
2.13.4 AT&T Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.13.5 AT&T Recent Developments and Future Plans
2.14 Apple
2.14.1 Apple Details
2.14.2 Apple Major Business
2.14.3 Apple Big Data & Machine Learning in Telecom Product and Solutions
2.14.4 Apple Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.14.5 Apple Recent Developments and Future Plans
2.15 Amazon
2.15.1 Amazon Details
2.15.2 Amazon Major Business
2.15.3 Amazon Big Data & Machine Learning in Telecom Product and Solutions
2.15.4 Amazon Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.15.5 Amazon Recent Developments and Future Plans
2.16 Microsoft
2.16.1 Microsoft Details
2.16.2 Microsoft Major Business
2.16.3 Microsoft Big Data & Machine Learning in Telecom Product and Solutions
2.16.4 Microsoft Big Data & Machine Learning in Telecom Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.16.5 Microsoft Recent Developments and Future Plans
3 麻豆原创 Competition, by Players
3.1 Global Big Data & Machine Learning in Telecom Revenue and Share by Players (2019-2024)
3.2 麻豆原创 Share Analysis (2023)
3.2.1 麻豆原创 Share of Big Data & Machine Learning in Telecom by Company Revenue
3.2.2 Top 3 Big Data & Machine Learning in Telecom Players 麻豆原创 Share in 2023
3.2.3 Top 6 Big Data & Machine Learning in Telecom Players 麻豆原创 Share in 2023
3.3 Big Data & Machine Learning in Telecom 麻豆原创: Overall Company Footprint Analysis
3.3.1 Big Data & Machine Learning in Telecom 麻豆原创: Region Footprint
3.3.2 Big Data & Machine Learning in Telecom 麻豆原创: Company Product Type Footprint
3.3.3 Big Data & Machine Learning in Telecom 麻豆原创: Company Product Application Footprint
3.4 New 麻豆原创 Entrants and Barriers to 麻豆原创 Entry
3.5 Mergers, Acquisition, Agreements, and Collaborations
4 麻豆原创 Size Segment by Type
4.1 Global Big Data & Machine Learning in Telecom Consumption Value and 麻豆原创 Share by Type (2019-2024)
4.2 Global Big Data & Machine Learning in Telecom 麻豆原创 Forecast by Type (2025-2030)
5 麻豆原创 Size Segment by Application
5.1 Global Big Data & Machine Learning in Telecom Consumption Value 麻豆原创 Share by Application (2019-2024)
5.2 Global Big Data & Machine Learning in Telecom 麻豆原创 Forecast by Application (2025-2030)
6 North America
6.1 North America Big Data & Machine Learning in Telecom Consumption Value by Type (2019-2030)
6.2 North America Big Data & Machine Learning in Telecom Consumption Value by Application (2019-2030)
6.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
6.3.1 North America Big Data & Machine Learning in Telecom Consumption Value by Country (2019-2030)
6.3.2 United States Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
6.3.3 Canada Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
6.3.4 Mexico Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
7 Europe
7.1 Europe Big Data & Machine Learning in Telecom Consumption Value by Type (2019-2030)
7.2 Europe Big Data & Machine Learning in Telecom Consumption Value by Application (2019-2030)
7.3 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
7.3.1 Europe Big Data & Machine Learning in Telecom Consumption Value by Country (2019-2030)
7.3.2 Germany Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
7.3.3 France Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
7.3.4 United Kingdom Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
7.3.5 Russia Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
7.3.6 Italy Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8 Asia-Pacific
8.1 Asia-Pacific Big Data & Machine Learning in Telecom Consumption Value by Type (2019-2030)
8.2 Asia-Pacific Big Data & Machine Learning in Telecom Consumption Value by Application (2019-2030)
8.3 Asia-Pacific Big Data & Machine Learning in Telecom 麻豆原创 Size by Region
8.3.1 Asia-Pacific Big Data & Machine Learning in Telecom Consumption Value by Region (2019-2030)
8.3.2 China Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8.3.3 Japan Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8.3.4 South Korea Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8.3.5 India Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8.3.6 Southeast Asia Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
8.3.7 Australia Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
9 South America
9.1 South America Big Data & Machine Learning in Telecom Consumption Value by Type (2019-2030)
9.2 South America Big Data & Machine Learning in Telecom Consumption Value by Application (2019-2030)
9.3 South America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
9.3.1 South America Big Data & Machine Learning in Telecom Consumption Value by Country (2019-2030)
9.3.2 Brazil Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
9.3.3 Argentina Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
10 Middle East & Africa
10.1 Middle East & Africa Big Data & Machine Learning in Telecom Consumption Value by Type (2019-2030)
10.2 Middle East & Africa Big Data & Machine Learning in Telecom Consumption Value by Application (2019-2030)
10.3 Middle East & Africa Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
10.3.1 Middle East & Africa Big Data & Machine Learning in Telecom Consumption Value by Country (2019-2030)
10.3.2 Turkey Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
10.3.3 Saudi Arabia Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
10.3.4 UAE Big Data & Machine Learning in Telecom 麻豆原创 Size and Forecast (2019-2030)
11 麻豆原创 Dynamics
11.1 Big Data & Machine Learning in Telecom 麻豆原创 Drivers
11.2 Big Data & Machine Learning in Telecom 麻豆原创 Restraints
11.3 Big Data & Machine Learning in Telecom Trends Analysis
11.4 Porters Five Forces Analysis
11.4.1 Threat of New Entrants
11.4.2 Bargaining Power of Suppliers
11.4.3 Bargaining Power of Buyers
11.4.4 Threat of Substitutes
11.4.5 Competitive Rivalry
12 Industry Chain Analysis
12.1 Big Data & Machine Learning in Telecom Industry Chain
12.2 Big Data & Machine Learning in Telecom Upstream Analysis
12.3 Big Data & Machine Learning in Telecom Midstream Analysis
12.4 Big Data & Machine Learning in Telecom Downstream Analysis
13 Research Findings and Conclusion
14 Appendix
14.1 Methodology
14.2 Research Process and Data Source
14.3 Disclaimer
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
Google
AT&T
Apple
Amazon
Microsoft
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*If Applicable.