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Global Big Data in Telecom Analytics Market Report 2020: Over 22% of Carrier App Value Will be Enabled via Big Data by 2025

Research and Markets
·6-min read

Dublin, Nov. 10, 2020 (GLOBE NEWSWIRE) -- The "Big Data in Telecom Analytics by Computing Type, Deployment Type, Applications and Services 2020-2025" report has been added to's offering.

This report provides an in-depth assessment of the global structured data, big data and telecom analytics markets, including a study of the business drivers, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2020 to 2025.

Big data tools help communications service providers gain deeper insights into customer behavior, including usage patterns, preferences, and interests. While hard to derive quick and meaningful insights, big data solutions provide carriers insights into relationships, family, work patterns and location. This is increasingly achieved in real-time using both structured and unstructured data.

The term big data refers to a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. While the presence of such datasets is not something new, the past few years have witnessed immense commercial investments in solutions that address the processing and analysis of big data.

Big data opens a vast array of applications and opportunities in multiple vertical sectors including not limited to retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, government and homeland security and the emerging industrial internet vertical. With access to vast amounts of datasets, telecom companies are also turning out to be major proponents of the big data movement. big data technologies, and in particular their analytics abilities offer a multitude of benefits to network operators which include improving subscriber experience, building and maintaining smarter networks, reducing churn and even the generation of new revenue streams.

Big data and analytics have emerged as a potential source of revenue for telecom operators, at a time when carriers have been feeling the pressure to generate new sources of revenue. One of those sources comes from their ability to mine the huge amount of data they generate or have access to in both their customer base and their networks. The two have emerged as the tools to help analyze and manage this information. There are now many analytical and intelligence tools that enable mobile operators to understand customer and network behavior.

Communications service providers have a rich stream of data, especially those that offer telephony, TV and Internet services, the triple play operators. The many sources of data is an advantage for telecom companies, but if they want to monetize that data and derive meaningful, actionable analytics it could be challenging due to the complexities of correlation, prediction, and the massive volumes of data from different sources.

Big data helps telecom providers to get deeper insights into customer behavior, their service usage patterns, preferences, and interests. While hard to derive quick and meaningful insights, big data gives telecom companies an idea of relationships, family, work patterns and accurate location data among others. The publisher of this report believes that this will optimally be performed in real-time using both structured and unstructured data.

In general, the data coming into a telecom service provider could be categorized as 'data' which is the actual content flowing across the network, and 'meta-data', which is the data describing the properties, sources, costs, etc. relating to the content data. In terms of types of data telco data can be divided into two broad categories as structured and unstructured data.

Key Topics Covered:

1.0 Executive Summary
1.1 Topics Covered
1.2 Key Findings
1.3 Target Audience
1.4 Companies Mentioned

2.0 Big Data Technology and Business Case
2.1 Structured vs. Unstructured Data
2.2 Defining Big Data
2.3 Key Characteristics of Big Data
2.4 Capturing Data through Detection and Social Systems
2.5 Big Data Technology
2.6 Business Drivers for Telecom Big Data and Analytics
2.6.1 Continued Growth of Mobile Broadband
2.6.2 Competition from New Types of Service Providers
2.6.3 New Technology Investment
2.6.4 Need for New KPIs
2.6.5 Artificial Intelligence and Machine Learning
2.7 Market Barriers
2.7.1 Privacy and Security: The 'Big' Barrier
2.7.2 Workforce Re-skilling and Organizational Resistance
2.7.3 Lack of Clear Big Data Strategies
2.7.4 Technical Challenges: Scalability and Maintenance

3.0 Key Big Data Investment Sectors
3.1 Industrial Internet and M2M
3.2 Retail and Hospitality
3.3 Media
3.4 Utilities
3.5 Financial Services
3.6 Healthcare and Pharmaceutical
3.7 Telecom Companies
3.8 Government and Homeland Security
3.9 Other Sectors

4.0 The Big Data Value Chain
4.1 Fragmentation in the Big Data Value Chain
4.2 Data Acquisitioning and Provisioning
4.3 Data Warehousing and Business Intelligence
4.4 Analytics and Virtualization
4.5 Actioning and Business Process Management (BPM)
4.6 Data Governance

5.0 Big Data in Telecom Analytics
5.1 Telecom Analytics Market
5.2 Improving Subscriber Experience
5.3 Building Smarter Networks
5.4 Churn/Risk Reduction and New Revenue Streams
5.5 Telecom Analytics Case Studies
5.6 Carriers, Analytics, and Data as a Service (DaaS)
5.7 Opportunities for Carriers in Cloud Analytics

6.0 Structured Data in Telecom Analytics
6.1 Telecom Data Sources and Repositories
6.2 Telecom Data Mining
6.3 Telecom Database Services
6.4 Structured Telecom Data Analytics

7.0 Analysis of Select Big Data Market Players
7.1 Vendor Assessment Matrix
7.2 Apache Software Foundation
7.3 Accenture
7.4 Amazon
7.5 APTEAN (Formerly CDC Software)
7.6 Cisco Systems
7.7 Cloudera
7.8 Dell EMC
7.9 Facebook
7.10 GoodData Corporation
7.11 Google (Alphabet)
7.12 Guavus (Thales Group)
7.13 Hitachi Data Systems
7.14 Hortonworks
7.15 HPE
7.16 IBM
7.17 Informatica
7.18 Intel
7.19 Jaspersoft (TIBCO)
7.20 Microsoft
7.21 MongoDB (Formerly 10Gen)
7.22 MU Sigma
7.23 Netapp
7.24 ElectrifAI (formerly Opera Solutions)
7.25 Oracle
7.26 Pentaho
7.27 Platfora (Workday)
7.28 Qliktech
7.29 Rackspace Technology
7.30 Revolution Analytics (Microsoft)
7.31 Salesforce
7.32 SAP
7.33 SAS Institute
7.34 Sisense
7.35 Splunk
7.36 Sqrrl Data
7.37 Supermicro
7.38 Tableau Software
7.39 Teradata
7.40 Tidemark (Insight Software)
7.41 VMware

8.0 Big Data in Telecom Analytics Forecast 2020 to 2027
8.1 Global Big Data in Telecom Analytics 2020 - 2025
8.2 Big Data in Telecom Analytics by Region 2020 - 2025
8.3 Big Data Products and Services in Telecom Analytics 2020 - 2025
8.4 Big Data Management Platform for Telecom 2020 - 2025
8.5 Big Data Services for Telecom Analytics 2020 - 2025
8.6 Big Data Virtualization Platform Deployment in Telecom Analytics 2020 - 2025

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