Global Big Data Analytics, Mobile Edge Computing, and Real-time Data Market 2018-2023: Focus on Education, Financial, Government, Healthcare, Manufacturing, Retail, Telecom and IT & Transportation

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This research provides an assessment of the global Big Data market, including business case issues/analysis, application use cases, vendor landscape, value chain analysis, and a quantitative assessment of the industry with forecasting from 2018 to 2023. This research indicates that no major industry vertical will be without real-time, edge computing driven data strategy by 2023 with up to 64% of all segments implementing at least one IoT related real-time data service offering by 2025.

While big data analytics solutions are in a rapid adoption phase within most major enterprise companies, edge computing remains in its infancy across most industry verticals. This will soon change with the introduction of mobile edge computing (also known as multi-access edge computing or MEC), driven by major global cellular service providers and supported by a few large IT services firms as well as some key systems integrators. Implemented as a complement to 5G and to optimize capacity allocation, MEC will also enable highly targeted apps and services including zone-based solutions for many segments including smart buildings, self-driving vehicles, robotics, UAVs, and more.

Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or Internet of Things (IoT) related apps and services.

This research provides market projections through 2023 including the following verticals:

  • Education
  • Financial Services
  • Government
  • Healthcare
  • Manufacturing
  • Retail Services
  • Telecom and IT
  • Transportation

This research also evaluates MEC technology, architecture and building blocks, ecosystem, market drivers, applications, solutions, and deployment challenges. The report also analyzes MEC industry initiatives, leading companies, and solutions. The report includes a market assessment and forecast for MEC users and MEC revenue globally, regionally, and within the enterprise market. It also includes market analysis for real-time data acquired via edge computing and big data analytics.

Target Audience:

  • Data analytics providers
  • Wireless service providers
  • ICT managed service providers
  • Software, App, and Content Providers
  • Wireless/mobile infrastructure providers
  • Cloud and IoT product and service providers

Key Topics Covered:

Big Data Market: Business Case, Market Analysis and Forecasts 2018 – 2023

1 Executive Summary

2 Introduction
2.1 Big Data Overview
2.1.1 Defining Big Data
2.1.2 Big Data Ecosystem
2.1.3 Key Characteristics of Big Data
2.2 Research Background
2.2.1 Scope
2.2.2 Coverage
2.2.3 Company Focus

3 Big Data Challenges and Opportunities
3.1 Securing Big Data Infrastructure
3.1.1 Big Data Infrastructure
3.1.2 Infrastructure Challenges
3.1.3 Big Data Infrastructure Opportunities
3.2 Unstructured Data and the Internet of Things
3.2.1 New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
3.2.2 Big Data in IoT will require Lightweight Data Interchange Format
3.2.3 Big Data in IoT will use Lightweight Protocols
3.2.4 Big Data in IoT will need Protocol for Network Interoperability
3.2.5 Big Data in IoT Demands Data Processing on Appropriate Scale

4 Big Data Technology and Business Case
4.1 Big Data Technology
4.1.1 Hadoop
4.1.2 NoSQL
4.1.3 MPP Databases
4.1.4 Others and Emerging Technologies
4.2 Emerging Technologies, Tools, and Techniques
4.2.1 Streaming Analytics
4.2.2 Cloud Technology
4.2.3 Google Search
4.2.4 Customize Analytical Tools
4.2.5 Internet Keywords
4.2.6 Gamification
4.3 Big Data Roadmap
4.4 Market Drivers
4.4.1 Data Volume & Variety
4.4.2 Increasing Adoption of Big Data by Enterprises and Telecom
4.4.3 Maturation of Big Data Software
4.4.4 Continued Investments in Big Data by Web Giants
4.4.5 Business Drivers
4.5 Market Barriers
4.5.1 Privacy and Security: The Big’ Barrier
4.5.2 Workforce Re-skilling and Organizational Resistance
4.5.3 Lack of Clear Big Data Strategies
4.5.4 Technical Challenges: Scalability & Maintenance
4.5.5 Big Data Development Expertise

5 Key Sectors for Big Data
5.1 Industrial Internet and Machine-to-Machine
5.1.1 Big Data in M2M
5.1.2 Vertical Opportunities
5.2 Retail and Hospitality
5.2.1 Improving Accuracy of Forecasts and Stock Management
5.2.2 Determining Buying Patterns
5.2.3 Hospitality Use Cases
5.2.4 Personalized Marketing
5.3 Media
5.3.1 Social Media
5.3.2 Social Gaming Analytics
5.3.3 Usage of Social Media Analytics by Other Verticals
5.3.4 Internet Keyword Search
5.4 Utilities
5.4.1 Analysis of Operational Data
5.4.2 Application Areas for the Future
5.5 Financial Services
5.5.1 Fraud Analysis, Mitigation & Risk Profiling
5.5.2 Merchant-Funded Reward Programs
5.5.3 Customer Segmentation
5.5.4 Customer Retention & Personalized Product Offering
5.5.5 Insurance Companies
5.6 Healthcare and Pharmaceutical
5.6.1 Drug Development
5.6.2 Medical Data Analytics
5.6.3 Case Study: Identifying Heartbeat Patterns
5.7 Telecommunications
5.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
5.7.2 Big Data Analytic Tools
5.7.3 Speech Analytics
5.7.4 New Products and Services
5.8 Government and Homeland Security
5.8.1 Big Data Research
5.8.2 Statistical Analysis
5.8.3 Language Translation
5.8.4 Developing New Applications for the Public
5.8.5 Tracking Crime
5.8.6 Intelligence Gathering
5.8.7 Fraud Detection and Revenue Generation
5.9 Other Sectors
5.9.1 Aviation
5.9.2 Transportation and Logistics: Optimizing Fleet Usage
5.9.3 Real-Time Processing of Sports Statistics
5.9.4 Education
5.9.5 Manufacturing

6 The Big Data Value Chain
6.1 Fragmentation in the Big Data Value
6.2 Data Acquisitioning and Provisioning
6.3 Data Warehousing and Business Intelligence
6.4 Analytics and Visualization
6.5 Actioning and Business Process Management
6.6 Data Governance

7 Big Data Analytics
7.1 The Role and Importance of Big Data Analytics
7.2 Big Data Analytics Processes
7.3 Reactive vs. Proactive Analytics
7.4 Technology and Implementation Approaches
7.4.1 Grid Computing
7.4.2 In-Database processing
7.4.3 In-Memory Analytics
7.4.4 Data Mining
7.4.5 Predictive Analytics
7.4.6 Natural Language Processing
7.4.7 Text Analytics
7.4.8 Visual Analytics
7.4.9 Association Rule Learning
7.4.10 Classification Tree Analysis
7.4.11 Machine Learning
7.4.12 Neural Networks
7.4.13 Multilayer Perceptron (MLP)
7.4.14 Radial Basis Functions
7.4.15 Geospatial Predictive Modelling
7.4.16 Regression Analysis
7.4.17 Social Network Analysis

8 Standardization and Regulatory Initiatives
8.1 Cloud Standards Customer Council
8.2 National Institute of Standards and Technology
8.4 Open Data Foundation
8.5 Open Data Center Alliance
8.6 Cloud Security Alliance
8.7 International Telecommunications Union
8.8 International Organization for Standardization

9 Global Markets and Forecasts for Big Data
9.1 Global Big Data Markets 2018 – 2023
9.2 Regional Markets for Big Data 2018 – 2023
9.3 Leading Countries in Big Data
9.3.1 United States
9.3.2 China
9.4 Big Data Revenue by Product Segment 2018 – 2023
9.4.1 Database Management Systems
9.4.2 Big Data Integration Tools
9.4.3 Application Infrastructure and Middleware
9.4.4 Business Intelligence Tools and Analytics Platforms
9.4.5 Big Data in Professional Services

10 Key Big Data Players
10.1 Vendor Assessment Matrix
10.2 1010Data (Advance Communication Corp.)
10.3 Accenture
10.4 Actian Corporation
10.5 Alteryx
10.6 Amazon
10.7 Anova Data
10.8 Apache Software Foundation
10.9 APTEAN (Formerly CDC Software)
10.10 Booz Allen Hamilton
10.11 Bosch Software Innovations: Bosch IoT Suite
10.12 Capgemini
10.13 Cisco Systems
10.14 Cloudera
10.15 CRAY Inc.
10.16 Computer Science Corporation (CSC)
10.17 DataDirect Network
10.18 Dell EMC
10.19 Deloitte
10.20 Facebook
10.21 Fujitsu
10.22 General Electric (GE)
10.23 GoodData Corporation
10.24 Google
10.25 Guavus
10.26 HP Enterprise
10.27 Hitachi Data Systems
10.28 Hortonworks
10.29 IBM
10.30 Informatica
10.31 Intel
10.32 Jasper (Cisco Jasper)
10.33 Juniper Networks
10.34 Longview
10.35 Marklogic
10.36 Microsoft
10.37 Microstrategy
10.38 MongoDB (Formerly 10Gen)
10.39 MU Sigma
10.40 Netapp
10.41 NTT Data
10.42 Open Text (Actuate Corporation)
10.43 Opera Solutions
10.44 Oracle
10.45 Pentaho (Hitachi)
10.46 Qlik Tech
10.47 Quantum
10.48 Rackspace
10.49 Revolution Analytics
10.50 Salesforce
10.51 SAP
10.52 SAS Institute
10.53 Sisense
10.54 Software AG/Terracotta
10.55 Splunk
10.56 Sqrrl
10.57 Supermicro
10.58 Tableau Software
10.59 Tata Consultancy Services
10.60 Teradata
10.61 Think Big Analytics
10.62 TIBCO
10.63 Verint Systems
10.64 VMware (Part of EMC)
10.65 Wipro
10.66 Workday (Platfora)

11 Appendix: Big Data Support of Streaming IoT Data
11.1 Big Data Technology Market Outlook for Streaming IoT Data
11.1.1 IoT Data Management is a Ubiquitous Opportunity across Enterprise
11.1.2 IoT Data becomes a Big Data Revenue Opportunity
11.1.3 Real-time Streaming IoT Data Analytics becoming a Substantial Business Opportunity
11.2 Global Streaming IoT Data Analytics Revenue
11.2.1 Overall Streaming Data Analytics Revenue for IoT
11.2.2 Global Streaming IoT Data Analytics Revenue by App, Software, and Services
11.2.3 Global Streaming IoT Data Analytics Revenue in Industry Verticals
11.3 Regional Streaming IoT Data Analytics Revenue
11.4 Streaming IoT Data Analytics Revenue by Country

Multi-access Edge Computing (MEC): Market Outlook and Forecasts 2018 – 2023

1 Executive Summary

2 Introduction
2.1 Understanding Multi-access Edge Computing
2.1.1 Edge Computing in an ICT Context
2.1.2 Proximity Computing: The Edge in Physical and Logical Context
2.1.3 Edge Computing vs. Other Computational Approaches
2.1.4 Multi-access Edge Computing
2.2 Important Characteristics of MEC
2.2.1 Processing at the Edge
2.2.2 Low Latency
2.2.3 Context Based
2.2.4 Location and Analytics
2.3 MEC Benefits
2.3.1 Business Benefits
2.3.2 Technical Benefits
2.3.3 Mobile Network Operator Benefits
2.3.4 Key Element of Carrier Heterogeneous Network Strategy

3 MEC Technology, Platforms, and Architecture
3.1 MEC Platform Architecture Building Blocks
3.1.1 MEC Infrastructure
3.1.2 MEC Application Platforms
3.1.3 MEC Management Framework
3.2 The Edge Cloud Computing Value Chain
3.3 MEC Technology Building Blocks
3.3.1 Radio Network Information Service
3.3.2 Traffic Offload Function
3.3.3 MEC Interfaces
3.3.4 Configuration Management
3.3.5 Application Lifecycle Management
3.3.6 VM Operations and Management
3.3.7 Hardware Virtualization and Infrastructure Management
3.3.8 Core Network Elements
3.3.9 Open Standards
3.4 MEC Technology Enablers
3.4.1 Mobile Computing to Mobile Cloud Computing
3.4.2 Cloudlet based Mobile Cloud Computing
3.4.3 Cloudlet to Cloud
3.4.4 PacketCloud Open Platform for Cloudlets
3.4.5 Enterprise Cloud Architecture
3.4.6 Cloudlet Solutions
3.4.7 Cloudlet Storage Frameworks
3.5 MEC Deployment Considerations
3.5.1 MEC Implementation Challenges
3.5.2 MEC Operational Challenges

4 MEC Market Drivers and Opportunities
4.1 Limitations of Cloud Convergence
4.2 IT and Telecom Network Convergence
4.3 Base Station Evolution
4.4 Cell Aggregation
4.5 Virtualization in the Cloud
4.6 Continually Improving Server Capacity
4.7 Data Center to Network Interactions
4.8 Open and Flexible App and Service Ecosystem
4.9 Fifth Generation (5G) Wireless
4.10 Edge Cloud and Data Transferability
4.11 Proximate Cloud Computing
4.12 Increasingly Faster Content Delivery
4.13 Advantages of MEC Small Cell Deployment
4.14 Overall Mobile Data Demand
4.15 Low Latency Applications
4.16 Integration of MEC with Cloud RAN
4.17 MEC Enhances Real-time Data and Analytics
4.17.1 Why Data at the Edge?
4.17.2 Convergence of Distributed Cloud and Big Data

5 MEC Ecosystem
5.1 The Overall Edge Computing Ecosystem
5.2 MEC Ecosystem Players
5.2.2 Software and ASPs
5.2.3 OTT Service and Content Providers
5.2.4 Network Infrastructure and Equipment Providers
5.2.5 Mobile Network Operators
5.3 Individual Company Analysis
5.3.1 ADLINK Technology Inc.
5.3.2 Advantech
5.3.3 Akamai Technologies
5.3.4 Allot Communications
5.3.5 Advanced Micro Devices
5.3.6 Brocade Communications Systems
5.3.7 Cavium Networks
5.3.8 Ceragon Networks
5.3.9 Cisco Systems
5.3.10 Fujitsu Technology Solutions
5.3.11 Hewlett Packard Enterprise
5.3.12 Huawei Technologies Co. Ltd
5.3.13 IBM Corporation
5.3.14 Integrated Device Technology
5.3.15 Intel Corporation
5.3.16 InterDigital Inc.
5.3.17 Juniper Networks
5.3.18 NEC Corporation
5.3.19 Nokia Corporation
5.3.20 PeerApp Ltd.
5.3.21 Quortus
5.3.22 Redhat, Inc.
5.3.23 Saguna Networks
5.3.24 Samsung Electronics Co., Ltd
5.3.25 Sony Corporation
5.3.26 SpiderCloud Wireless
5.3.27 Vasona Networks
5.3.28 Xilinx, Inc.
5.3.29 Yaana Ltd.
5.3.30 ZTE Corporation

6 MEC Application and Service Strategies
6.1 Optimizing the Mobile Cloud
6.1.1 Mobile Network Operator Strategies
6.1.2 Service Strategies and End-user Demand
6.2 Context Aware Services
6.2.1 Commerce
6.2.2 Education
6.2.3 Gaming
6.2.4 Healthcare
6.2.5 Location-based Services
6.2.6 Public Safety
6.2.7 Connected Vehicles
6.2.8 Wearables
6.1 Data Services and Analytics
6.1.1 Localized Real-time Data Becomes King
6.1.2 Anonymizing Local and Real-time Data for Third-party Usage
6.1.3 Increasing Demand for Data as a Service (DaaS) in MEC Environment

7 MEC Market Forecasts 2018 – 2023
7.1 Global Market 2018 – 2023
7.1.1 Combined MEC Market
7.1.2 MEC Market by Segment MEC Cloud Server Market MEC Equipment Market MEC Platform Market MEC Software and API Market MEC Service Market
7.1.3 MEC Enterprise CAPEX and OPEX Spend
7.1.4 MEC Network Migration
7.1.5 MEC Enterprise Adoption
7.2 MEC Regional Market 2018 – 2023
7.2.1 North America Market Forecast
7.2.2 APAC Market Forecasts
7.2.3 Europe Market Forecast
7.3 MEC Network Users/Devices 2018 – 2023
7.3.1 Global MEC Network Users/Devices
7.3.2 MEC Network User by Supporting Network
7.3.3 Regional MEC Network User North America User APAC User Europe User

8 Conclusions and Recommendations
8.1 Anticipated Market Needs and Opportunities
8.1.1 The need for MEC Integration with Public Cloud Platforms
8.1.2 Enterprise (Dedicated and Shared Resources) MEC Integration
8.1.3 Dedicated MEC Public Safety and Homeland Security Infrastructure
8.2 Insights into Future Market Dynamics
8.2.1 MEC will Facilitate Downward Price Pressure on Non-real-time Data
8.2.2 MEC will Drive Demand for Virtual Network Operators
8.2.3 MEC will Drive the Need for New Players as well as M&A

9 Appendix: Real-time Data Analytics Revenue 2018 – 2023
9.1 Global Streaming Data Analytics Revenue
9.2 Global Real-time Data Analytics Revenue by App, Software, and Services
9.3 Global Real-time Data Analytics Revenue in Industry Verticals
9.3.1 Real-time Data Analytics Revenue in Retail Real-time Data Analytics Revenue by Retail Segment Real-time Data Analytics Retail Revenue by App, Software, and Service
9.3.2 Real-time Data Analytics Revenue in Telecom and IT Real-time Data Analytics Revenue by Telecom and IT Segment Real-time Data Analytics Revenue by Telecom and IT App, Software, and Service
9.3.3 Real-time Data Analytics Revenue in Energy and Utility Real-time Data Analytics Revenue by Energy and Utility Segment Real-time Data Analytics Energy and Utilities Revenue by App, Software, and Service
9.3.4 Real-time Data Analytics Revenue in Government Real-time Data Analytics Revenue by Government Segment Real-time Data Analytics Government Revenue by App, Software, and Service
9.3.5 Real-time Data Analytics Revenue in Healthcare and Life Science Real-time Data Analytics Revenue by Healthcare Segment
9.3.6 Real-time Data Analytics Revenue in Manufacturing Real-time Data Analytics Revenue by Manufacturing Segment Real-time Data Analytics Manufacturing Revenue by App, Software, and Service
9.3.7 Real-time Data Analytics Revenue in Transportation and Logistics Real-time Data Analytics Revenue by Transportation and Logistics Segment Real-time Data Analytics Transportation and Logistics Revenue by App, Software, and Service
9.3.8 Real-time Data Analytics Revenue in Banking and Finance Real-time Data Analytics Revenue by Banking and Finance Segment Real-time Data Analytics Revenue by Banking and Finance App, Software, and Service
9.3.9 Real-time Data Analytics Revenue in Smart Cities Real-time Data Analytics Revenue by Smart City Segment Real-time Data Analytics Revenue by Smart City App, Software, and Service
9.3.10 Real-time Data Analytics Revenue in Automotive Real-time Data Analytics Revenue by Automobile Industry Segment Real-time Data Analytics Revenue by Automotive Industry App, Software, and Service
9.3.11 Real-time Data Analytics Revenue in Education Real-time Data Analytics Revenue by Education Industry Segment Real-time Data Analytics Revenue by Education Industry App, Software, and Service
9.3.12 Real-time Data Analytics Revenue in Outsourcing Services Real-time Data Analytics Revenue by Outsourcing Segment Real-time Data Analytics Revenue by Outsourcing Industry App, Software, and Service
9.4 Real-time Data Analytics Revenue by Leading Vendor Platform
9.4.1 Global Investment in Data by Industry Sector 2018 – 2023

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