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Global Big Data & Machine Learning in Telecom Market Research Report 2024
Synopsis
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 Big Data & Machine Learning in Telecom market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
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 ¥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 ¥1.75 trillion, a year-on-year increase of 21.3%. In the same year, the fixed Internet broadband access business revenue was ¥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 aims to provide a comprehensive presentation of the global market for Big Data & Machine Learning in Telecom, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Big Data & Machine Learning in Telecom.
Report Scope
The Big Data & Machine Learning in Telecom market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Big Data & Machine Learning in Telecom market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Big Data & Machine Learning in Telecom companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
Google
AT&T
Apple
Amazon
Microsoft
Segment by Type
Descriptive Analytics
Predictive Analytics
Machine Learning
Feature Engineering
Segment by Application
Processing
Storage
Analyzing
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Big Data & Machine Learning in Telecom companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
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 Big Data & Machine Learning in Telecom market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
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 ¥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 ¥1.75 trillion, a year-on-year increase of 21.3%. In the same year, the fixed Internet broadband access business revenue was ¥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 aims to provide a comprehensive presentation of the global market for Big Data & Machine Learning in Telecom, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Big Data & Machine Learning in Telecom.
Report Scope
The Big Data & Machine Learning in Telecom market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Big Data & Machine Learning in Telecom market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Big Data & Machine Learning in Telecom companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
AT&T
Apple
Amazon
Microsoft
Segment by Type
Descriptive Analytics
Predictive Analytics
Machine Learning
Feature Engineering
Segment by Application
Processing
Storage
Analyzing
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Big Data & Machine Learning in Telecom companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Big Data & Machine Learning in Telecom Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Descriptive Analytics
1.2.3 Predictive Analytics
1.2.4 Machine Learning
1.2.5 Feature Engineering
1.3 Market by Application
1.3.1 Global Big Data & Machine Learning in Telecom Market Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Processing
1.3.3 Storage
1.3.4 Analyzing
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Big Data & Machine Learning in Telecom Market Perspective (2019-2030)
2.2 Big Data & Machine Learning in Telecom Growth Trends by Region
2.2.1 Global Big Data & Machine Learning in Telecom Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Big Data & Machine Learning in Telecom Historic Market Size by Region (2019-2024)
2.2.3 Big Data & Machine Learning in Telecom Forecasted Market Size by Region (2025-2030)
2.3 Big Data & Machine Learning in Telecom Market Dynamics
2.3.1 Big Data & Machine Learning in Telecom Industry Trends
2.3.2 Big Data & Machine Learning in Telecom Market Drivers
2.3.3 Big Data & Machine Learning in Telecom Market Challenges
2.3.4 Big Data & Machine Learning in Telecom Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Big Data & Machine Learning in Telecom Players by Revenue
3.1.1 Global Top Big Data & Machine Learning in Telecom Players by Revenue (2019-2024)
3.1.2 Global Big Data & Machine Learning in Telecom Revenue Market Share by Players (2019-2024)
3.2 Global Big Data & Machine Learning in Telecom Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Big Data & Machine Learning in Telecom Revenue
3.4 Global Big Data & Machine Learning in Telecom Market Concentration Ratio
3.4.1 Global Big Data & Machine Learning in Telecom Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Big Data & Machine Learning in Telecom Revenue in 2023
3.5 Big Data & Machine Learning in Telecom Key Players Head office and Area Served
3.6 Key Players Big Data & Machine Learning in Telecom Product Solution and Service
3.7 Date of Enter into Big Data & Machine Learning in Telecom Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Big Data & Machine Learning in Telecom Breakdown Data by Type
4.1 Global Big Data & Machine Learning in Telecom Historic Market Size by Type (2019-2024)
4.2 Global Big Data & Machine Learning in Telecom Forecasted Market Size by Type (2025-2030)
5 Big Data & Machine Learning in Telecom Breakdown Data by Application
5.1 Global Big Data & Machine Learning in Telecom Historic Market Size by Application (2019-2024)
5.2 Global Big Data & Machine Learning in Telecom Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Big Data & Machine Learning in Telecom Market Size (2019-2030)
6.2 North America Big Data & Machine Learning in Telecom Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Big Data & Machine Learning in Telecom Market Size by Country (2019-2024)
6.4 North America Big Data & Machine Learning in Telecom Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Big Data & Machine Learning in Telecom Market Size (2019-2030)
7.2 Europe Big Data & Machine Learning in Telecom Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Big Data & Machine Learning in Telecom Market Size by Country (2019-2024)
7.4 Europe Big Data & Machine Learning in Telecom Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Big Data & Machine Learning in Telecom Market Size (2019-2030)
8.2 Asia-Pacific Big Data & Machine Learning in Telecom Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Big Data & Machine Learning in Telecom Market Size by Region (2019-2024)
8.4 Asia-Pacific Big Data & Machine Learning in Telecom Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Big Data & Machine Learning in Telecom Market Size (2019-2030)
9.2 Latin America Big Data & Machine Learning in Telecom Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Big Data & Machine Learning in Telecom Market Size by Country (2019-2024)
9.4 Latin America Big Data & Machine Learning in Telecom Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Big Data & Machine Learning in Telecom Market Size (2019-2030)
10.2 Middle East & Africa Big Data & Machine Learning in Telecom Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Big Data & Machine Learning in Telecom Market Size by Country (2019-2024)
10.4 Middle East & Africa Big Data & Machine Learning in Telecom Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Allot
11.1.1 Allot Company Detail
11.1.2 Allot Business Overview
11.1.3 Allot Big Data & Machine Learning in Telecom Introduction
11.1.4 Allot Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.1.5 Allot Recent Development
11.2 Argyle data
11.2.1 Argyle data Company Detail
11.2.2 Argyle data Business Overview
11.2.3 Argyle data Big Data & Machine Learning in Telecom Introduction
11.2.4 Argyle data Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.2.5 Argyle data Recent Development
11.3 Ericsson
11.3.1 Ericsson Company Detail
11.3.2 Ericsson Business Overview
11.3.3 Ericsson Big Data & Machine Learning in Telecom Introduction
11.3.4 Ericsson Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.3.5 Ericsson Recent Development
11.4 Guavus
11.4.1 Guavus Company Detail
11.4.2 Guavus Business Overview
11.4.3 Guavus Big Data & Machine Learning in Telecom Introduction
11.4.4 Guavus Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.4.5 Guavus Recent Development
11.5 HUAWEI
11.5.1 HUAWEI Company Detail
11.5.2 HUAWEI Business Overview
11.5.3 HUAWEI Big Data & Machine Learning in Telecom Introduction
11.5.4 HUAWEI Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.5.5 HUAWEI Recent Development
11.6 Intel
11.6.1 Intel Company Detail
11.6.2 Intel Business Overview
11.6.3 Intel Big Data & Machine Learning in Telecom Introduction
11.6.4 Intel Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.6.5 Intel Recent Development
11.7 NOKIA
11.7.1 NOKIA Company Detail
11.7.2 NOKIA Business Overview
11.7.3 NOKIA Big Data & Machine Learning in Telecom Introduction
11.7.4 NOKIA Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.7.5 NOKIA Recent Development
11.8 Openwave mobility
11.8.1 Openwave mobility Company Detail
11.8.2 Openwave mobility Business Overview
11.8.3 Openwave mobility Big Data & Machine Learning in Telecom Introduction
11.8.4 Openwave mobility Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.8.5 Openwave mobility Recent Development
11.9 Procera networks
11.9.1 Procera networks Company Detail
11.9.2 Procera networks Business Overview
11.9.3 Procera networks Big Data & Machine Learning in Telecom Introduction
11.9.4 Procera networks Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.9.5 Procera networks Recent Development
11.10 Qualcomm
11.10.1 Qualcomm Company Detail
11.10.2 Qualcomm Business Overview
11.10.3 Qualcomm Big Data & Machine Learning in Telecom Introduction
11.10.4 Qualcomm Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.10.5 Qualcomm Recent Development
11.11 ZTE
11.11.1 ZTE Company Detail
11.11.2 ZTE Business Overview
11.11.3 ZTE Big Data & Machine Learning in Telecom Introduction
11.11.4 ZTE Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.11.5 ZTE Recent Development
11.12 Google
11.12.1 Google Company Detail
11.12.2 Google Business Overview
11.12.3 Google Big Data & Machine Learning in Telecom Introduction
11.12.4 Google Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.12.5 Google Recent Development
11.13 AT&T
11.13.1 AT&T Company Detail
11.13.2 AT&T Business Overview
11.13.3 AT&T Big Data & Machine Learning in Telecom Introduction
11.13.4 AT&T Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.13.5 AT&T Recent Development
11.14 Apple
11.14.1 Apple Company Detail
11.14.2 Apple Business Overview
11.14.3 Apple Big Data & Machine Learning in Telecom Introduction
11.14.4 Apple Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.14.5 Apple Recent Development
11.15 Amazon
11.15.1 Amazon Company Detail
11.15.2 Amazon Business Overview
11.15.3 Amazon Big Data & Machine Learning in Telecom Introduction
11.15.4 Amazon Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.15.5 Amazon Recent Development
11.16 Microsoft
11.16.1 Microsoft Company Detail
11.16.2 Microsoft Business Overview
11.16.3 Microsoft Big Data & Machine Learning in Telecom Introduction
11.16.4 Microsoft Revenue in Big Data & Machine Learning in Telecom Business (2019-2024)
11.16.5 Microsoft Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
Published By : QY Research
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