Big Data in Telecom Analytics Market Outlook and Forecasts 2020 – 2027

Big Data in Telecom Analytics Market Outlook and Forecasts 2020 – 2027

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.

Telecom 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. Mind Commerce 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 (Big) data.

This report provides an in-depth assessment of the global Structured Data, Big Data and Telecom Analytics markets, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2020 to 2027.

Target Audience:
• Network service providers
• Systems integration companies
• Big Data and Analytics companies
• Advertising and media companies
• Enterprise across all industry verticals
• Cloud and IoT product and service providers


1.0 Executive Summary
1.1 Topics Covered
1.2 Key Findings
1.3 Target Audience
1.4 Companies
2.0 Big Data Technology And Business Case
2.1 Structured Vs. Unstructured Data
2.1.1 Structured Database Services In Telecom
2.1.2 Unstructured Data From Apps And Databases In Telecom
2.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
2.2 Defining Big Data
2.3 Key Characteristics Of Big Data
2.3.1 Volume
2.3.2 Variety
2.3.3 Velocity
2.3.4 Variability
2.3.5 Complexity
2.4 Capturing Data Through Detection And Social Systems
2.4.1 Data In Social Systems
2.4.2 Detection And Sensors
2.4.3 Sensors In The Consumer Sector
2.4.4 Sensors In Industry
2.5 Big Data Technology
2.5.1 Hadoop
2.5.1.1 Mapreduce
2.5.1.2 Hdfs
2.5.1.3 Other Apache Projects
2.5.2 Nosql
2.5.2.1 Hbase
2.5.2.2 Cassandra
2.5.2.3 Mongo Db
2.5.2.4 Riak
2.5.2.5 Couchdb
2.5.3 Mpp Databases
2.5.4 Others And Emerging Technologies
2.5.4.1 Storm
2.5.4.2 Drill
2.5.4.3 Dremel
2.5.4.4 Sap Hana
2.5.4.5 Gremlin & Giraph
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
3.1.1 Big Data In M2m
3.1.2 Vertical Opportunities
3.2 Retail And Hospitality
3.2.1 Improving Accuracy Of Forecasts And Stock Management
3.2.2 Determining Buying Patterns
3.2.3 Hospitality Use Cases
3.3 Media
3.3.1 Social Media
3.3.2 Social Gaming Analytics
3.3.3 Usage Of Social Media Analytics By Other Verticals
3.4 Utilities
3.4.1 Analysis Of Operational Data
3.4.2 Application Areas For The Future
3.5 Financial Services
3.5.1 Fraud Analysis & Risk Profiling
3.5.2 Merchant-funded Reward Programs
3.5.3 Customer Segmentation
3.5.4 Insurance Companies
3.6 Healthcare And Pharmaceutical
3.6.1 Drug Development
3.6.2 Medical Data Analytics
3.6.3 Case Study: Identifying Heartbeat Patterns
3.7 Telecom Companies
3.7.1 Telcom Analytics: Customer/Usage Profiling And Service Optimization
3.7.2 Speech Analytics
3.7.3 Other Use Cases
3.8 Government And Homeland Security
3.8.1 Developing New Applications For The Public
3.8.2 Tracking Crime
3.8.3 Intelligence Gathering
3.8.4 Fraud Detection And Revenue Generation
3.9 Other Sectors
3.9.1 Aviation: Air Traffic Control
3.9.2 Transportation And Logistics: Optimizing Fleet Usage
3.9.3 Sports: Real-time Processing Of Statistics
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
5.2.1 Generating A Full Spectrum View Of The Subscriber
5.2.2 Creating Customized Experiences And Targeted Promotions
5.2.3 Central Big Data Repository: Key To Customer Satisfaction
5.2.4 Reduce Costs And Increase Market Share
5.3 Building Smarter Networks
5.3.1 Understanding Network Utilization
5.3.2 Improving Network Quality And Coverage
5.3.3 Combining Telecom Data With Public Data Sets: Real-time Event Management
5.3.4 Leveraging M2m For Telecom Analytics
5.3.5 M2m, Deep Packet Inspection And Big Data: Identifying & Fixing Network Defects
5.4 Churn/Risk Reduction And New Revenue Streams
5.4.1 Predictive Analytics
5.4.2 Identifying Fraud And Bandwidth Theft
5.4.3 Creating New Revenue Streams
5.5 Telecom Analytics Case Studies
5.5.1 T-mobile Usa: Churn Reduction By 50%
5.5.2 Vodafone: Using Telco Analytics To Enable Navigation
5.6 Carriers, Analytics, And Data As A Service (Daas)
5.6.1 Carrier Data Management Operational Strategies
5.6.2 Network Vs. Subscriber Analytics
5.6.3 Data And Analytics Opportunities To Third Parties
5.6.4 Carriers To Offer Data As S Service (Daas) On B2b Basis
5.6.5 Daas Planning And Strategies
5.6.6 Carrier Monetization Of Data With Daas
5.7 Opportunities For Carriers In Cloud Analytics
5.7.1 Carrier Nfv And Cloud Analytics
5.7.2 Carrier Cloud Oss/Bss Analytics
5.7.3 Carrier Cloud Services, Data, And Analytics
5.7.4 Carrier Performance Management And The Cloud Analytics
6.0 Structured Data In Telecom Analytics
6.1 Telecom Data Sources And Repositories
6.1.1 Subscriber Data
6.1.2 Subscriber Presence And Location Data
6.1.3 Business Data: Toll-free And Other Directory Services
6.1.4 Network Data: Deriving Data From Network Operations
6.2 Telecom Data Mining
6.2.1 Data Sources: Rating, Charging, And Billing Examples
6.2.2 Privacy Issues
6.3 Telecom Database Services
6.3.1 Calling Name Identity
6.3.2 Subscriber Data Management (Sdm) Services
6.3.3 Other Data-intensive Service Areas
6.3.4 Emerging Service Area: Identity Verification
6.4 Structured Telecom Data Analytics
6.4.1 Dealing With Telecom Data Fragmentation
6.4.2 Deep Packet Inspection
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 – 2027
8.2 Big Data In Telecom Analytics By Region 2020 – 2027
8.3 Big Data Products And Services In Telecom Analytics 2020 – 2027
8.4 Big Data Management Platform For Telecom 2020 – 2027
8.4.1 Big Data In Telecom Data Analytics By Compute Type 2020 – 2027
8.4.2 Big Data Compute In Telecom Data Analytics By Cloud Deployment Type 2020 – 2027
8.4.3 Big Data In Telecom Data Analytics By Storage Type 2020 – 2027
8.4.4 Big Data Storage In Telecom Data Analytics By Cloud Deployment Type 2020 – 2027
8.4.5 Big Data In Telecom Analytics By Functions 2020 – 2027
8.4.5.1 Big Data In Network Data Analytics By Functions 2020 – 2027
8.4.6 Big Data In Telecom Analytics By Application Type 2020 – 2027
8.4.6.1 Big Data In Business Specific Applications In Telecom Analytics 2020 – 2027
8.4.6.2 Iot In Telecom Analytics By Consumer, Enterprise, Industrial, And Government Sectors 2020 – 2027
8.5 Big Data Services For Telecom Analytics 2020 – 2027
8.5.1 Big Data Professional Services For Telecom Analytics 2020 – 2027
8.5.2 Big Data Managed Services For Telecom Analytics 2020 – 2027
8.6 Big Data Virtualization Platform Deployment In Telecom Analytics 2020 – 2027
Figures
Figure 1: Hybrid Data in Next Generation Applications
Figure 2: Big Data Components
Figure 3: Big Data Sources
Figure 4: Capturing Data from Detection Systems and Sensors
Figure 5: Capturing Data across Sectors
Figure 6: AI Structure
Figure 7: The Big Data Value Chain
Figure 8: Telco Analytics Investments Driven by Big Data
Figure 9: Different Data Types within Telco Environment
Figure 10: Presence-enabled Application
Figure 11: Calling Name (CNAM) Service Operation
Figure 12: Subscriber Data Management (SDM) Ecosystem
Figure 13: Data Fragmented across Telecom Databases
Figure 14: Big Data Product Growth Prospects
Figure 15: Big Data Vendor Ranking Matrix
Figure 16: Global Big Data in Telecom Analytics 2020 – 2027
Figure 17: Big Data in Telecom Analytics by Region 2020 – 2027
Figure 18: Big Data Products and Services in Telecom Analytics 2020 – 2027
Figure 19: Big Data Management Platform for Telecom 2020 – 2027
Figure 20: Big Data in Telecom Data Analytics by Compute Type 2020 – 2027
Figure 21: Big Data Compute in Telecom Data Analytics by Cloud Deployment Type 2020 – 2027
Figure 22: Big Data in Telecom Data Analytics by Storage Type 2020 – 2027
Figure 23: Big Data Storage in Telecom Data Analytics by Cloud Deployment Type 2020 – 2027
Figure 24: Big Data in Telecom Analytics by Functions 2020 – 2027
Figure 25: Big Data in Network Data Analytics by Functions 2020 – 2027
Figure 26: Big Data in Telecom Analytics by Application Type 2020 – 2027
Figure 27: Big Data in Business Specific Applications in Telecom Analytics 2020 – 2027
Figure 28: IoT in Telecom Analytics by Consumer, Enterprise, Industrial, and Government Sectors 2020 – 2027
Figure 29: Big Data Services for Telecom Analytics 2020 – 2027
Figure 30: Big Data Professional Services for Telecom Analytics 2020 – 2027
Figure 31: Big Data Managed Services for Telecom Analytics 2020 – 2027
Figure 32: Big Data Virtualization Platform Deployment in Telecom Analytics 2020 – 2027
Tables
Table 1: Global Big Data in Telecom Analytics 2020 – 2027
Table 2: Big Data in Telecom Analytics by Region 2020 – 2027
Table 3: Big Data Products and Services in Telecom Analytics 2020 – 2027
Table 4: Big Data Management Platform for Telecom 2020 – 2027
Table 5: Big Data in Telecom Data Analytics by Compute Type 2020 – 2027
Table 6: Big Data Compute in Telecom Data Analytics by Cloud Deployment Type 2020 – 2027
Table 7: Big Data in Telecom Data Analytics by Storage Type 2020 – 2027
Table 8: Big Data Storage in Telecom Data Analytics by Cloud Deployment Type 2020 – 2027
Table 9: Big Data in Telecom Analytics by Functions 2020 – 2027
Table 10: Big Data in Network Data Analytics by Functions 2020 – 2027
Table 11: Big Data in Telecom Analytics by Application Type 2020 – 2027
Table 12: Big Data in Business Specific Applications in Telecom Analytics 2020 – 2027
Table 13: IoT in Telecom Analytics by Consumer, Enterprise, Industrial, and Government Sectors 2020 – 2027
Table 14: Big Data Services for Telecom Analytics 2020 – 2027
Table 15: Big Data Professional Services for Telecom Analytics 2020 – 2027
Table 16: Big Data Managed Services for Telecom Analytics 2020 – 2027
Table 17: Big Data Virtualization Platform Deployment in Telecom Analytics 2020 – 2027

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