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.
麻豆原创 Analysis and Insights: Global Big Data & Machine Learning in Telecom 麻豆原创
The global Big Data & Machine Learning in Telecom market is projected to grow from US$ million in 2023 to US$ million by 2029, at a Compound Annual Growth Rate (CAGR) of % during the forecast period.
The US & Canada market for Big Data & Machine Learning in Telecom is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The China market for Big Data & Machine Learning in Telecom is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The Europe market for Big Data & Machine Learning in Telecom is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The global key companies of Big Data & Machine Learning in Telecom include Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility and Procera networks, etc. in 2022, the global top five players had a share approximately % in terms of revenue.
Report Includes
This report presents an overview of global market for Big Data & Machine Learning in Telecom market size. Analyses of the global market trends, with historic market revenue data for 2018 - 2022, estimates for 2023, and projections of CAGR through 2029.
This report researches the key producers of Big Data & Machine Learning in Telecom, also provides the revenue of main regions and countries. Highlights of the upcoming market potential for Big Data & Machine Learning in Telecom, and key regions/countries of focus to forecast this market into various segments and sub-segments. Country specific data and market value analysis for the U.S., Canada, Mexico, Brazil, China, Japan, South Korea, Southeast Asia, India, Germany, the U.K., Italy, Middle East, Africa, and Other Countries.
This report focuses on the Big Data & Machine Learning in Telecom revenue, market share and industry ranking of main companies, data from 2018 to 2023. Identification of the major stakeholders in the global Big Data & Machine Learning in Telecom market, and analysis of their competitive landscape and market positioning based on recent developments and segmental revenues. This report will help stakeholders to understand the competitive landscape and gain more insights and position their businesses and market strategies in a better way.
This report analyzes the segments data by type and by application, revenue, and growth rate, from 2018 to 2029. Evaluation and forecast the market size for Big Data & Machine Learning in Telecom revenue, projected growth trends, production technology, application and end-user industry.
Descriptive company profiles of the major global players, including Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility and Procera networks, etc.
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, and Latin America
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (product type, 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: Revenue of Big Data & Machine Learning in Telecom in global and regional level. 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. 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鈥� competitive landscape, revenue, market share and industry ranking, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by type, covering the revenue, 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 revenue, and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6: North America by type, by application and by country, revenue for each segment.
Chapter 7: Europe by type, by application and by country, revenue for each segment.
Chapter 8: China by type and by application revenue for each segment.
Chapter 9: Asia (excluding China) by type, by application and by region, revenue for each segment.
Chapter 10: Middle East, Africa, and Latin America by type, by application and by country, revenue for each segment.
Chapter 11: Provides profiles of key companies, introducing the basic situation of the main companies in the market in detail, including product descriptions and specifications, Big Data & Machine Learning in Telecom revenue, gross margin, and recent development, etc.
Chapter 12: Analyst's Viewpoints/Conclusions
Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.
1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Big Data & Machine Learning in Telecom 麻豆原创 Size Growth Rate by Type, 2018 VS 2022 VS 2029
1.2.2 Descriptive Analytics
1.2.3 Predictive Analytics
1.2.4 Machine Learning
1.2.5 Feature Engineering
1.3 麻豆原创 by Application
1.3.1 Global Big Data & Machine Learning in Telecom 麻豆原创 Size Growth Rate by Application, 2018 VS 2022 VS 2029
1.3.2 Processing
1.3.3 Storage
1.3.4 Analyzing
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Global Growth Trends
2.1 Global Big Data & Machine Learning in Telecom 麻豆原创 Perspective (2018-2029)
2.2 Global Big Data & Machine Learning in Telecom Growth Trends by Region
2.2.1 Big Data & Machine Learning in Telecom 麻豆原创 Size by Region: 2018 VS 2022 VS 2029
2.2.2 Big Data & Machine Learning in Telecom Historic 麻豆原创 Size by Region (2018-2023)
2.2.3 Big Data & Machine Learning in Telecom Forecasted 麻豆原创 Size by Region (2024-2029)
2.3 Big Data & Machine Learning in Telecom 麻豆原创 Dynamics
2.3.1 Big Data & Machine Learning in Telecom Industry Trends
2.3.2 Big Data & Machine Learning in Telecom 麻豆原创 Drivers
2.3.3 Big Data & Machine Learning in Telecom 麻豆原创 Challenges
2.3.4 Big Data & Machine Learning in Telecom 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Revenue Big Data & Machine Learning in Telecom by Players
3.1.1 Global Big Data & Machine Learning in Telecom Revenue by Players (2018-2023)
3.1.2 Global Big Data & Machine Learning in Telecom Revenue 麻豆原创 Share by Players (2018-2023)
3.2 Global Big Data & Machine Learning in Telecom 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players of Big Data & Machine Learning in Telecom, Ranking by Revenue, 2021 VS 2022 VS 2023
3.4 Global Big Data & Machine Learning in Telecom 麻豆原创 Concentration Ratio
3.4.1 Global Big Data & Machine Learning in Telecom 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Big Data & Machine Learning in Telecom Revenue in 2022
3.5 Global Key Players of Big Data & Machine Learning in Telecom Head office and Area Served
3.6 Global Key Players of Big Data & Machine Learning in Telecom, Product and Application
3.7 Global Key Players of Big Data & Machine Learning in Telecom, Date of Enter into This Industry
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 麻豆原创 Size by Type (2018-2023)
4.2 Global Big Data & Machine Learning in Telecom Forecasted 麻豆原创 Size by Type (2024-2029)
5 Big Data & Machine Learning in Telecom Breakdown Data by Application
5.1 Global Big Data & Machine Learning in Telecom Historic 麻豆原创 Size by Application (2018-2023)
5.2 Global Big Data & Machine Learning in Telecom Forecasted 麻豆原创 Size by Application (2024-2029)
6 North America
6.1 North America Big Data & Machine Learning in Telecom 麻豆原创 Size (2018-2029)
6.2 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type
6.2.1 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2018-2023)
6.2.2 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2024-2029)
6.2.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Share by Type (2018-2029)
6.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application
6.3.1 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2018-2023)
6.3.2 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2024-2029)
6.3.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Share by Application (2018-2029)
6.4 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
6.4.1 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country: 2018 VS 2022 VS 2029
6.4.2 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2018-2023)
6.4.3 North America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2024-2029)
6.4.4 U.S.
6.4.5 Canada
7 Europe
7.1 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size (2018-2029)
7.2 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Type
7.2.1 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2018-2023)
7.2.2 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2024-2029)
7.2.3 Europe Big Data & Machine Learning in Telecom 麻豆原创 Share by Type (2018-2029)
7.3 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Application
7.3.1 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2018-2023)
7.3.2 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2024-2029)
7.3.3 Europe Big Data & Machine Learning in Telecom 麻豆原创 Share by Application (2018-2029)
7.4 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
7.4.1 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Country: 2018 VS 2022 VS 2029
7.4.2 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2018-2023)
7.4.3 Europe Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2024-2029)
7.4.3 Germany
7.4.4 France
7.4.5 U.K.
7.4.6 Italy
7.4.7 Russia
7.4.8 Nordic Countries
8 China
8.1 China Big Data & Machine Learning in Telecom 麻豆原创 Size (2018-2029)
8.2 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Type
8.2.1 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2018-2023)
8.2.2 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2024-2029)
8.2.3 China Big Data & Machine Learning in Telecom 麻豆原创 Share by Type (2018-2029)
8.3 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Application
8.3.1 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2018-2023)
8.3.2 China Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2024-2029)
8.3.3 China Big Data & Machine Learning in Telecom 麻豆原创 Share by Application (2018-2029)
9 Asia (excluding China)
9.1 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size (2018-2029)
9.2 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Type
9.2.1 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2018-2023)
9.2.2 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2024-2029)
9.2.3 Asia Big Data & Machine Learning in Telecom 麻豆原创 Share by Type (2018-2029)
9.3 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Application
9.3.1 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2018-2023)
9.3.2 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2024-2029)
9.3.3 Asia Big Data & Machine Learning in Telecom 麻豆原创 Share by Application (2018-2029)
9.4 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Region
9.4.1 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Region: 2018 VS 2022 VS 2029
9.4.2 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Region (2018-2023)
9.4.3 Asia Big Data & Machine Learning in Telecom 麻豆原创 Size by Region (2024-2029)
9.4.4 Japan
9.4.5 South Korea
9.4.6 China Taiwan
9.4.7 Southeast Asia
9.4.8 India
9.4.9 Australia
10 Middle East, Africa, and Latin America
10.1 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size (2018-2029)
10.2 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type
10.2.1 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2018-2023)
10.2.2 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Type (2024-2029)
10.2.3 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Share by Type (2018-2029)
10.3 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application
10.3.1 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2018-2023)
10.3.2 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Application (2024-2029)
10.3.3 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Share by Application (2018-2029)
10.4 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country
10.4.1 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country: 2018 VS 2022 VS 2029
10.4.2 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2018-2023)
10.4.3 Middle East, Africa, and Latin America Big Data & Machine Learning in Telecom 麻豆原创 Size by Country (2024-2029)
10.4.4 Brazil
10.4.5 Mexico
10.4.6 Turkey
10.4.7 Saudi Arabia
10.4.8 Israel
10.4.9 GCC Countries
11 Key Players Profiles
11.1 Allot
11.1.1 Allot Company Details
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 (2018-2023)
11.1.5 Allot Recent Developments
11.2 Argyle data
11.2.1 Argyle data Company Details
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 (2018-2023)
11.2.5 Argyle data Recent Developments
11.3 Ericsson
11.3.1 Ericsson Company Details
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 (2018-2023)
11.3.5 Ericsson Recent Developments
11.4 Guavus
11.4.1 Guavus Company Details
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 (2018-2023)
11.4.5 Guavus Recent Developments
11.5 HUAWEI
11.5.1 HUAWEI Company Details
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 (2018-2023)
11.5.5 HUAWEI Recent Developments
11.6 Intel
11.6.1 Intel Company Details
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 (2018-2023)
11.6.5 Intel Recent Developments
11.7 NOKIA
11.7.1 NOKIA Company Details
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 (2018-2023)
11.7.5 NOKIA Recent Developments
11.8 Openwave mobility
11.8.1 Openwave mobility Company Details
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 (2018-2023)
11.8.5 Openwave mobility Recent Developments
11.9 Procera networks
11.9.1 Procera networks Company Details
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 (2018-2023)
11.9.5 Procera networks Recent Developments
11.10 Qualcomm
11.10.1 Qualcomm Company Details
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 (2018-2023)
11.10.5 Qualcomm Recent Developments
11.11 ZTE
11.11.1 ZTE Company Details
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 (2018-2023)
11.11.5 ZTE Recent Developments
11.12 Google
11.12.1 Google Company Details
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 (2018-2023)
11.12.5 Google Recent Developments
11.13 AT&T
11.13.1 AT&T Company Details
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 (2018-2023)
11.13.5 AT&T Recent Developments
11.14 Apple
11.14.1 Apple Company Details
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 (2018-2023)
11.14.5 Apple Recent Developments
11.15 Amazon
11.15.1 Amazon Company Details
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 (2018-2023)
11.15.5 Amazon Recent Developments
11.16 Microsoft
11.16.1 Microsoft Company Details
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 (2018-2023)
11.16.5 Microsoft Recent Developments
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
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
Google
AT&T
Apple
Amazon
Microsoft
听
听
*If Applicable.
