Industry Growth Insights published a new data on “Big Data & Machine Learning in Telecom Market”. The research report is titled “Big Data & Machine Learning in Telecom Market research by Types (Descriptive Analytics, Predictive Analytics, Machine Learning, Feature Engineering), By Applications (Processing, Storage, Analyzing), By Players/Companies Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility, Procera networks, Qualcomm, ZTE, Google, AT&T, Apple, Amazon, Microsoft”.
Scope Of The Report
Report Attributes
Report Details
Report Title
Big Data & Machine Learning in Telecom Market Research Report
By Type
Descriptive Analytics, Predictive Analytics, Machine Learning, Feature Engineering
By Application
Processing, Storage, Analyzing
By Companies
Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility, Procera networks, Qualcomm, ZTE, Google, AT&T, Apple, Amazon, Microsoft
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
216
Number of Tables & Figures
152
Customization Available
Yes, the report can be customized as per your need.
Global Big Data & Machine Learning in Telecom Market Report Segments:
The global Big Data & Machine Learning in Telecom market is segmented on the basis of:
Types
Descriptive Analytics, Predictive Analytics, Machine Learning, Feature Engineering
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Processing, Storage, Analyzing
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- Allot
- Argyle data
- Ericsson
- Guavus
- HUAWEI
- Intel
- NOKIA
- Openwave mobility
- Procera networks
- Qualcomm
- ZTE
- Google
- AT&T
- Apple
- Amazon
- Microsoft
Highlights of The Big Data & Machine Learning in Telecom Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Descriptive Analytics
- Predictive Analytics
- Machine Learning
- Feature Engineering
- By Application:
- Processing
- Storage
- Analyzing
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Big Data & Machine Learning in Telecom Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
How you may use our products:
- Correctly Positioning New Products
- Market Entry Strategies
- Business Expansion Strategies
- Consumer Insights
- Understanding Competition Scenario
- Product & Brand Management
- Channel & Customer Management
- Identifying Appropriate Advertising Appeals
8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Big data and machine learning are two buzzwords that have been gaining a lot of traction in the telecom industry. They refer to large sets of data that can be analyzed using sophisticated algorithms to make predictions or recommendations. This can be used for a variety of purposes, such as improving customer service, predicting traffic congestion, or identifying frauds.
Some of the key players operating in the big data & machine learning in telecom market are Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility, Procera networks, Qualcomm, ZTE, Google, AT&T, Apple, Amazon, Microsoft.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Big Data & Machine Learning in Telecom Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Big Data & Machine Learning in Telecom Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Big Data & Machine Learning in Telecom Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Big Data & Machine Learning in Telecom Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Big Data & Machine Learning in Telecom Market Size & Forecast, 2020-2028
4.5.1 Big Data & Machine Learning in Telecom Market Size and Y-o-Y Growth
4.5.2 Big Data & Machine Learning in Telecom Market Absolute $ Opportunity
Chapter 5 Global Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Market Size Forecast by Type
5.2.1 Descriptive Analytics
5.2.2 Predictive Analytics
5.2.3 Machine Learning
5.2.4 Feature Engineering
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Market Size Forecast by Applications
6.2.1 Processing
6.2.2 Storage
6.2.3 Analyzing
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Big Data & Machine Learning in Telecom Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Big Data & Machine Learning in Telecom Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Analysis and Forecast
9.1 Introduction
9.2 North America Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Market Size Forecast by Type
9.6.1 Descriptive Analytics
9.6.2 Predictive Analytics
9.6.3 Machine Learning
9.6.4 Feature Engineering
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Market Size Forecast by Applications
9.10.1 Processing
9.10.2 Storage
9.10.3 Analyzing
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Analysis and Forecast
10.1 Introduction
10.2 Europe Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Market Size Forecast by Type
10.6.1 Descriptive Analytics
10.6.2 Predictive Analytics
10.6.3 Machine Learning
10.6.4 Feature Engineering
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Market Size Forecast by Applications
10.10.1 Processing
10.10.2 Storage
10.10.3 Analyzing
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Market Size Forecast by Type
11.6.1 Descriptive Analytics
11.6.2 Predictive Analytics
11.6.3 Machine Learning
11.6.4 Feature Engineering
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Market Size Forecast by Applications
11.10.1 Processing
11.10.2 Storage
11.10.3 Analyzing
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Analysis and Forecast
12.1 Introduction
12.2 Latin America Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Market Size Forecast by Type
12.6.1 Descriptive Analytics
12.6.2 Predictive Analytics
12.6.3 Machine Learning
12.6.4 Feature Engineering
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Market Size Forecast by Applications
12.10.1 Processing
12.10.2 Storage
12.10.3 Analyzing
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Market Size Forecast by Type
13.6.1 Descriptive Analytics
13.6.2 Predictive Analytics
13.6.3 Machine Learning
13.6.4 Feature Engineering
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Market Size Forecast by Applications
13.10.1 Processing
13.10.2 Storage
13.10.3 Analyzing
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Big Data & Machine Learning in Telecom Market: Competitive Dashboard
14.2 Global Big Data & Machine Learning in Telecom Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 Allot
14.3.2 Argyle data
14.3.3 Ericsson
14.3.4 Guavus
14.3.5 HUAWEI
14.3.6 Intel
14.3.7 NOKIA
14.3.8 Openwave mobility
14.3.9 Procera networks
14.3.10 Qualcomm
14.3.11 ZTE
14.3.12 Google
14.3.13 AT&T
14.3.14 Apple
14.3.15 Amazon
14.3.16 Microsoft
