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Carrier Data Revenue: Big Data, Analytics, and Data as a Service (DaaS) 2014 - 2019

Carrier Data Revenue: Big Data, Analytics, and Data as a Service (DaaS) 2014 - 2019

This product is a collection of several individual publications.

Telecommunications service providers acquire and maintain substantial structured and unstructured (Big) data. Leading carriers have centralized Subscriber Data Management (SDM) systems, which consolidate and organize data from various sources such as HLR, HSS, and other data repositories. In addition, carriers have access to a plethora of data from various “Big Data” sources such as OSS/BSS, system monitoring and performance management systems including Self Organizing Networks (SON).

Big Data and related Analytics solutions opens a vast array of applications and opportunities for telecom carriers to offer services in multiple industry verticals. Network operators may sell data in a “Data as a Service” (DaaS) model to various market sectors including retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.

DaaS is defined as any service offered wherein users can access vendor provided databases or host their own databases on vendor managed systems. DaaS is expected to grow significantly in the near future due to a few dominant themes including cloud-based infrastructure/services, enterprise data syndication, and the consumer services trend towards Everything as a Service (XaaS).

Carriers have an excellent opportunity to offer Business-to-Business (B2B) services on a DaaS basis, representing a fast growing secondary and revenue stream. The Big Data driven telecom analytics market alone is expect to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue. This research provides a quantitative and qualitative and assessment of carrier prospects for B2B revenue as a DaaS provider including forecast data and key insights respectively. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Telecom service providers
Wireless device manufacturers
Big Data and Analytics companies
Wireless infrastructure companies
Telecom managed service companies
Cloud infrastructure and XaaS providers
Intermediaries and mediation companies

Report Benefits:

Forecast data for Big Data, Analytics and DaaS to 2019
Understand DaaS infrastructure challenges for operations
Identify carrier Big Data solutions and XaaS service packages
Recognize the role and importance of DaaS as service offering
Understand the importance of managed systems and best practices
Understand Big Data vendor landscape, value chain analysis, case studies

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014 – 2019
1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
1.5 Companies Mentioned
2 Chapter 2: Big Data Technology & Business Case
2.1 Defining Big Data
2.2 Key Characteristics of Big Data
2.2.1 Volume
2.2.2 Variety
2.2.3 Velocity
2.2.4 Variability
2.2.5 Complexity
2.3 Big Data Technology
2.3.1 Hadoop
2.3.1.1 MapReduce
2.3.1.2 HDFS
2.3.1.3 Other Apache Projects
2.3.2 NoSQL
2.3.2.1 Hbase
2.3.2.2 Cassandra
2.3.2.3 Mongo DB
2.3.2.4 Riak
2.3.2.5 CouchDB
2.3.3 MPP Databases
2.3.4 Others and Emerging Technologies
2.3.4.1 Storm
2.3.4.2 Drill
2.3.4.3 Dremel
2.3.4.4 SAP HANA
2.3.4.5 Gremlin & Giraph
2.4 Market Drivers
2.4.1 Data Volume & Variety
2.4.2 Increasing Adoption of Big Data by Enterprises & Telcos
2.4.3 Maturation of Big Data Software
2.4.4 Continued Investments in Big Data by Web Giants
2.5 Market Barriers
2.5.1 Privacy & Security: The ‘Big’ Barrier
2.5.2 Workforce Re-skilling & Organizational Resistance
2.5.3 Lack of Clear Big Data Strategies
2.5.4 Technical Challenges: Scalability & Maintenance
3 Chapter 3: Key Investment Sectors for Big Data
3.1 Industrial Internet & M2M
3.1.1 Big Data in M2M
3.1.2 Vertical Opportunities
3.2 Retail & Hospitality
3.2.1 Improving Accuracy of Forecasts & 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 & Pharmaceutical
3.6.1 Drug Development
3.6.2 Medical Data Analytics
3.6.3 Case Study: Identifying Heartbeat Patterns
3.7 Telcos
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
3.7.2 Speech Analytics
3.7.3 Other Use Cases
3.8 Government & 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 & Revenue Generation
3.9 Other Sectors
3.9.1 Aviation: Air Traffic Control
3.9.2 Transportation & Logistics: Optimizing Fleet Usage
3.9.3 Sports: Real-Time Processing of Statistics
4 Chapter 4: The Big Data Value Chain
4.1 How Fragmented is the Big Data Value Chain?
4.2 Data Acquisitioning & Provisioning
4.3 Data Warehousing & Business Intelligence
4.4 Analytics & Virtualization
4.5 Actioning & Business Process Management (BPM)
4.6 Data Governance
5 Chapter 5: Big Data in Telco Analytics
5.1 How Big is the Market for Telco Analytics?
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 the Usage of the Network
5.3.2 The Magic of Analytics: Improving Network Quality and Coverage
5.3.3 Combining Telco Data with Public Data Sets: Real-Time Event Management
5.3.4 Leveraging M2M for Telco Analytics
5.3.5 M2M, Deep Packet Inspection & 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 & Bandwidth Theft
5.4.3 Creating New Revenue Streams
5.5 Telco Analytics Case Studies
5.5.1 T-Mobile USA: Churn Reduction by 50%
5.5.2 Vodafone: Using Telco Analytics to Enable Navigation
6 Chapter 6: Key Players in the Big Data Market
6.1 Vendor Assessment Matrix
6.2 Apache Software Foundation
6.3 Accenture
6.4 Amazon
6.5 APTEAN (Formerly CDC Software)
6.6 Cisco Systems
6.7 Cloudera
6.8 Dell
6.9 EMC
6.10 Facebook
6.11 GoodData Corporation
6.12 Google
6.13 Guavus
6.14 Hitachi Data Systems
6.15 Hortonworks
6.16 HP
6.17 IBM
6.18 Informatica
6.19 Intel
6.20 Jaspersoft
6.21 Microsoft
6.22 MongoDB (Formerly 10Gen)
6.23 MU Sigma
6.24 Netapp
6.25 Opera Solutions
6.26 Oracle
6.27 Pentaho
6.28 Platfora
6.29 Qliktech
6.30 Quantum
6.31 Rackspace
6.32 Revolution Analytics
6.33 Salesforce
6.34 SAP
6.35 SAS Institute
6.36 Sisense
6.37 Software AG/Terracotta
6.38 Splunk
6.39 Sqrrl
6.40 Supermicro
6.41 Tableau Software
6.42 Teradata
6.43 Think Big Analytics
6.44 Tidemark Systems
6.45 VMware (Part of EMC)
7 Chapter 7: Market Analysis
7.1 Big Data Revenue: 2014 - 2019
7.2 Big Data Revenue by Functional Area: 2014 - 2019
7.2.1 Supply Chain Management
7.2.2 Business Intelligence
7.2.3 Application Infrastructure & Middleware
7.2.4 Data Integration Tools & Data Quality Tools
7.2.5 Database Management Systems
7.2.6 Big Data Social & Content Analytics
7.2.7 Big Data Storage Management
7.2.8 Big Data Professional Services
7.3 Big Data Revenue by Region 2014 - 2019
7.3.1 Asia Pacific
7.3.2 Eastern Europe
7.3.3 Latin & Central America
7.3.4 Middle East & Africa
7.3.5 North America
7.3.6 Western Europe
Figures
Figure 1: The Big Data Value Chain
Figure 2: Telco Analytics Investments Driven by Big Data: 2013 - 2019 ($ Million)
Figure 3: Big Data Vendor Ranking Matrix 2013
Figure 4: Big Data Revenue: 2013 – 2019 ($ Million)
Figure 5: Big Data Revenue by Functional Area: 2013 – 2019 ($ Million)
Figure 6: Big Data Supply Chain Management Revenue: 2013 – 2019 ($ Million)
Figure 7: Big Data Supply Business Intelligence Revenue: 2013 – 2019 ($ Million)
Figure 8: Big Data Application Infrastructure & Middleware Revenue: 2013 – 2019 ($ Million)
Figure 9: Big Data Integration Tools & Data Quality Tools Revenue: 2013 – 2019 ($ Million)
Figure 10: Big Data Database Management Systems Revenue: 2013 – 2019 ($ Million)
Figure 11: Big Data Social & Content Analytics Revenue: 2013 – 2019 ($ Million)
Figure 12: Big Data Storage Management Revenue: 2013 – 2019 ($ Million)
Figure 13: Big Data Professional Services Revenue: 2013 – 2019 ($ Million)
Figure 14: Big Data Revenue by Region: 2013 – 2019 ($ Million)
Figure 15: Asia Pacific Big Data Revenue: 2013 – 2019 ($ Million)
Figure 16: Eastern Europe Big Data Revenue: 2013 – 2019 ($ Million)
Figure 17: Latin & Central America Big Data Revenue: 2013 – 2019 ($ Million)
Figure 18: Middle East & Africa Big Data Revenue: 2013 – 2019 ($ Million)
Figure 19: North America Big Data Revenue: 2013 – 2019 ($ Million)
Figure 20: Western Europe Big Data Revenue: 2013 – 2019 ($ Million)
Data as a Service (DaaS) Market and Forecasts 2014 – 2019
1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
2 DaaS Technologies
2.1 Cloud
2.2 Database Approaches and Solutions
2.2.1 Relational Database Management System (RDBS)
2.2.2 NoSQL
2.2.3 Hadoop
2.2.4 High Performance Computing Cluster (HPCC)
2.2.5 OpenStack
2.3 DaaS and the XaaS Ecosystem
2.4 Open Data Center Alliance
2.5 Market Sizing by Horizontal
3 DaaS Market
3.1 Market Overview
3.1.1 Data Structure
3.1.2 Specialization
3.1.3 Vendors
3.2 Vendor Analysis and Prospects
3.2.1 Large Vendors: BDaaS
3.2.2 Mid-sized Vendors
3.2.3 Small Vendors: DaaS and SaaS
3.2.4 Market Size: BDaaS vs. RDBMS
3.3 Market Drivers and Constraints
3.3.1 Drivers
3.3.2 Constraints
3.4 Market Share and Geographic Influence
3.5 Vendors
3.5.1 1010data
3.5.2 Amazon
3.5.3 Clickfox
3.5.4 Datameer
3.5.5 Google
3.5.6 Hewlett-Packard
3.5.7 IBM
3.5.8 Infosys
3.5.9 Microsoft
3.5.10 Oracle
3.5.11 Rackspace
3.5.12 Salesforce
3.5.13 Splunk
3.5.14 Teradata
3.5.15 Tresata
4 DaaS Strategies
4.1 General Strategies
4.1.1 Tiered Data Focus
4.1.2 Value-based Pricing
4.1.3 Open Development Environment
4.2 Specific Strategies
4.2.1 Service Ecosystem and Platforms
4.2.2 Bringing to Together Multiple Sources for Mash-ups
4.2.3 Developing Value-added Services (VAS) as Proof Points
4.2.4 Open Access to all Entities including Competitors
4.2.5 Prepare for Big Opportunities with the Internet of Things (IoT)
4.3 Service Provider Strategies
4.3.1 Telecom Network Operators
4.3.2 Data Center Providers
4.3.3 Managed Service Providers
4.4 Infrastructure Provider Strategies
4.4.1 Enable New Business Models
4.5 Application Developer Strategies
5 DaaS based Applications
5.1 Business Intelligence
5.2 Development Environments
5.3 Verification and Authorization
5.4 Reporting and Analytics
5.5 Development Environments
6 Market Outlook and Future of DaaS
6.1 Recent Security Concerns
6.2 Cloud Trends
6.2.1 Hybrid Computing
6.2.2 Multi-Cloud
6.2.3 Cloud Bursting
6.3 General Data Trends
6.4 Enterprise Leverages own Data and Telecom
6.4.1 Web APIs
6.4.2 SOA and Enterprise APIs
6.4.3 Cloud APIs
6.4.4 Telecom APIs
6.5 Data Federation Emerges for DaaS
7 Conclusions
8 Appendix
8.1 Structured vs. Unstructured Data
8.1.1 Structured Database Services in Telecom
8.1.2 Unstructured Database Services in Telecom and Enterprise
8.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
8.2 Data Architecture and Functionality
8.2.1 Data Architecture
8.2.1.1 Data Models and Modelling
8.2.1.2 DaaS Architecture
8.2.2 Data Mart vs. Data Warehouse
8.2.3 Data Gateway
8.2.4 Data Mediation
8.3 Master Data Management (MDM)
8.3.1 Understanding MDM
8.3.1.1 Transactional vs. Non-transactional Data
8.3.1.2 Reference vs. Analytics Data
8.3.2 MDM and DaaS
8.3.2.1 Data Acquisition and Provisioning
8.3.2.2 Data Warehousing and Business Intelligence
8.3.2.3 Analytics and Virtualization
8.3.2.4 Data Governance
8.4 Data Mining
8.4.1 Data Capture
8.4.1.1 Event Detection
8.4.1.2 Capture Methods
8.4.2 Data Mining Tools
Figures
Figure 1: Total DaaS Revenue, Through 2019
Figure 2: Cloud Computing Service Model Stack and Principle Consumers
Figure 3: DaaS across Horizontal and Vertical Segments
Figure 4: Revenue by XaaS Horizontal 2014 - 2019
Figure 5: BDaaS Revenue by Vertical 2014 - 2019
Figure 6: BDaaS Revenue by Vertical 2014 - 2019
Figure 7: DaaS Revenue by Region 2014 - 2019
Figure 8: Different Data Types and Functions in DaaS
Figure 9: Ecosystem and Platform Model
Figure 10: Ecosystem and Platform Model
Figure 11: DaaS and IoT Mediation for Smartgrid
Figure 12: Internet of Things (IoT) and DaaS
Figure 13: Telecom API Value Chain for DaaS
Figure 14: DaaS, Verification and Authorization
Figure 15: Web APIs
Figure 16: Services Oriented Architecture
Figure 17: Cloud Services, DaaS, and APIs
Figure 18: Telecom APIs
Figure 19: Federated Data vs. Non-Federated Models
Figure 20: Federated Data at Functional Level
Figure 21: Federated Data at City Level
Figure 22: Federated Data at Global Level
Figure 23: Federation Requires Mediation Data
Figure 24: Mediation Data Synchronization
Figure 25: Hybrid Data in Next Generation Applications
Figure 26: Traditional Data Architecture
Figure 27: Data Architecture Modeling
Figure 28: DaaS Data Architecture
Figure 29: Location Data Mediation
Figure 30: Data Mediation in IoT
Figure 31: Data Mediation for Smartgrids
Figure 32: Enterprise Data Types
Figure 33: Data Governance
Figure 34: Data Flow
Figure 35: Processing Streaming Data
Everything as a Service (XaaS)
1 Introduction
1.1 Executive Summary
1.2 XaaS: Market Driver for DaaS
2 The SPI Model (SaaS, Paas and Iaas)
2.1 Software as a Service (SaaS)
2.2 Infrastructure as a Service (IaaS)
2.3 Platform as a Service (PaaS)
3 Benefits for the Enterprise
3.1 Market Forecasts 2014 - 2018
3.2 Transforming Enterprise Operations into the Cloud: Benefits and Challenges
4 Everything as a Service (Xaas)
4.1 Storage as a Service (STORaaS)
4.2 Communication as a Service (CaaS)
4.3 Network as a Service (NaaS)
4.4 Monitoring as a Service (MaaS)
4.5 Back-up as a Service (BaaS)
4.6 Desktop as a Service (DTaaS)
4.7 Database as a Service (DBaaS)
4.8 Big Data as a Service (BDaaS)
4.9 Identity as a Service (IDaaS)
4.10 Management as a Service (MGTaaS)
4.11 Business Process as a Service (BPaaS)
4.12 Proximity as a Service (PROXaaS)
4.13 XaaS Future Direction
5 XaaS Vendors Landscape
5.1 M5 Networks
5.1.1 M5 UCaaS
5.2 Microsoft Lync UCaaS
5.2.1 Solution Analysis
5.3 Thinking Phone Networks
5.3.1 Solutions
5.4 Boundary
5.4.1 Solution
5.5 enStratus Networks
5.5.1 Solutions
5.6 RightScale
5.6.1 Solutions
5.7 Radius Networks
5.7.1 Solutions Analysis

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