Big Data & Machine Learning in Telecom Market, Global Outlook and Forecast 2023-2030
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.
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. This report contains market size and forecasts of Big Data & Machine Learning in Telecom in global, including the following market information:
Global Big Data & Machine Learning in Telecom Market Revenue, 2018-2023, 2024-2030, ($ millions)
Global top five companies in 2022 (%)
The global Big Data & Machine Learning in Telecom market was valued at US$ million in 2022 and is projected to reach US$ million by 2029, at a CAGR of % during the forecast period. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.
The U.S. Market is Estimated at $ Million in 2022, While China is to reach $ Million.
Descriptive Analytics Segment to Reach $ Million by 2029, with a % CAGR in next six years.
The global key manufacturers 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 have a share approximately % in terms of revenue.
We surveyed the Big Data & Machine Learning in Telecom companies, and industry experts on this industry, involving the revenue, demand, product type, recent developments and plans, industry trends, drivers, challenges, obstacles, and potential risks.
Total Market by Segment:
Global Big Data & Machine Learning in Telecom Market, by Type, 2018-2023, 2024-2030 ($ millions)
Global Big Data & Machine Learning in Telecom Market Segment Percentages, by Type, 2022 (%)
Descriptive Analytics
Predictive Analytics
Machine Learning
Feature Engineering
Global Big Data & Machine Learning in Telecom Market, by Application, 2018-2023, 2024-2030 ($ millions)
Global Big Data & Machine Learning in Telecom Market Segment Percentages, by Application, 2022 (%)
Processing
Storage
Analyzing
Global Big Data & Machine Learning in Telecom Market, By Region and Country, 2018-2023, 2024-2030 ($ Millions)
Global Big Data & Machine Learning in Telecom Market Segment Percentages, By Region and Country, 2022 (%)
North America
US
Canada
Mexico
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Benelux
Rest of Europe
Asia
China
Japan
South Korea
Southeast Asia
India
Rest of Asia
South America
Brazil
Argentina
Rest of South America
Middle East & Africa
Turkey
Israel
Saudi Arabia
UAE
Rest of Middle East & Africa
Competitor Analysis
The report also provides analysis of leading market participants including:
Key companies Big Data & Machine Learning in Telecom revenues in global market, 2018-2023 (estimated), ($ millions)
Key companies Big Data & Machine Learning in Telecom revenues share in global market, 2022 (%)
Further, the report presents profiles of competitors in the market, key players include:
Allot
Argyle data
Ericsson
Guavus
HUAWEI
Intel
NOKIA
Openwave mobility
Procera networks
Qualcomm
ZTE
Google
AT&T
Apple
Amazon
Microsoft
Outline of Major Chapters:
Chapter 1: Introduces the definition of Big Data & Machine Learning in Telecom, market overview.
Chapter 2: Global Big Data & Machine Learning in Telecom market size in revenue.
Chapter 3: Detailed analysis of Big Data & Machine Learning in Telecom company competitive landscape, revenue and 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: Sales of Big Data & Machine Learning in Telecom in regional level and country 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 of each country in the world.
Chapter 7: 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 8: The main points and conclusions of the report.
