Big Data Market Outlook 2017 – 2022

Table of Contents

1 Background
1.1 Introduction
1.2 Scope of the Report
1.3 Target Audience
1.4 Companies in Report
2 Executive Summary
3 Big Data Technology and Business Case
3.1 Defining Big Data
3.2 Key Characteristics of Big Data
3.2.1 Volume
3.2.2 Variety
3.2.3 Velocity
3.2.4 Variability
3.2.5 Complexity
3.3 Big Data Technology
3.3.1 Hadoop
3.3.1.1 Other Apache Projects
3.3.2 NoSQL
3.3.2.1 Hbase
3.3.2.2 Cassandra
3.3.2.3 Mongo DB
3.3.2.4 Riak
3.3.2.5 CouchDB
3.3.3 MPP Databases
3.3.4 Others and Emerging Technologies
3.3.4.1 Storm
3.3.4.2 Drill
3.3.4.3 Dremel
3.3.4.4 SAP HANA
3.3.4.5 Gremlin & Giraph
3.4 New Paradigms and Techniques
3.4.1 Streaming Analytics
3.4.2 Cloud Technology
3.4.3 Google Search
3.4.4 Customize Analytical Tools
3.4.5 Internet Keywords
3.4.6 Gamification
3.5 Big Data Roadmap
3.6 Market Drivers
3.6.1 Data Volume & Variety
3.6.2 Increasing Adoption of Big Data by Enterprises and Telecom
3.6.3 Maturation of Big Data Software
3.6.4 Continued Investments in Big Data by Web Giants
3.6.5 Business Drivers
3.7 Market Barriers
3.7.1 Privacy and Security: The ‘Big’ Barrier
3.7.2 Workforce Re-skilling and Organizational Resistance
3.7.3 Lack of Clear Big Data Strategies
3.7.4 Technical Challenges: Scalability & Maintenance
3.7.5 Big Data Development Expertise
4 Key Sectors for Big Data
4.1 Industrial Internet and Machine-to-Machine
4.1.1 Big Data in M2M
4.1.2 Vertical Opportunities
4.2 Retail and Hospitality
4.2.1 Improving Accuracy of Forecasts & Stock Management
4.2.2 Determining Buying Patterns
4.2.3 Hospitality Use Cases
4.2.4 Personalized Marketing
4.3 Media
4.3.1 Social Media
4.3.2 Social Gaming Analytics
4.3.3 Usage of Social Media Analytics by Other Verticals
4.3.4 Internet Keyword Search
4.4 Utilities
4.4.1 Analysis of Operational Data
4.4.2 Application Areas for the Future
4.5 Financial Services
4.5.1 Fraud Analysis, Mitigation & Risk Profiling
4.5.2 Merchant-Funded Reward Programs
4.5.3 Customer Segmentation
4.5.4 Customer Retention & Personalized Product Offering
4.5.5 Insurance Companies
4.6 Healthcare and Pharmaceutical
4.6.1 Drug Development
4.6.2 Medical Data Analytics
4.6.3 Case Study: Identifying Heartbeat Patterns
4.7 Telecommunications
4.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
4.7.2 Big Data Analytic Tools
4.7.3 Speech Analytics
4.7.4 New Products and Services
4.8 Government and Homeland Security
4.8.1 Big Data Research
4.8.2 Statistical Analysis
4.8.3 Language Translation
4.8.4 Developing New Applications for the Public
4.8.5 Tracking Crime
4.8.6 Intelligence Gathering
4.8.7 Fraud Detection & Revenue Generation
4.9 Other Sectors
4.9.1 Aviation
4.9.2 Transportation & Logistics: Optimizing Fleet Usage
4.9.3 Sports: Real-Time Processing of Statistics
4.9.4 Education
4.9.5 Manufacturing
5 The Big Data Value Chain
5.1 Fragmentation in the Big Data Value
5.2 Data Acquisitioning & Provisioning
5.3 Data Warehousing & Business Intelligence
5.4 Analytics & Visualization
5.5 Actioning and Business Process Management
5.6 Data Governance
6 Big Data Analytics
6.1 What is Big Data Analytics?
6.2 The Importance of Big Data Analytics
6.3 Reactive vs. Proactive Analytics
6.4 Technology and Implementation Approaches
6.4.1 Grid Computing
6.4.2 In-Database processing
6.4.3 In-Memory Analytics
6.4.4 Data Mining
6.4.5 Predictive Analytics
6.4.6 Natural Language Processing
6.4.7 Text Analytics
6.4.8 Visual Analytics
6.4.9 Association Rule Learning
6.4.10 Classification Tree Analysis
6.4.11 Machine Learning
6.4.12 Neural Networks
6.4.13 Multilayer Perceptron (MLP)
6.4.14 Radial Basis Functions
6.4.14.1 Support Vector Machines
6.4.14.2 Naïve Bayes
6.4.14.3 K-nearest Neighbors
6.4.15 Geospatial Predictive Modelling
6.4.16 Regression Analysis
6.4.17 Social Network Analysis
7 Standardization and Regulatory Initiatives
7.1 Cloud Standards Customer Council
7.2 National Institute of Standards and Technology
7.3 OASIS
7.4 Open Data Foundation
7.5 Open Data Center Alliance
7.6 Cloud Security Alliance
7.7 International Telecommunications Union
7.8 International Organization for Standardization
8 Global Markets and Forecasts for Big Data
8.1 Global Big Data Markets 2017 – 2022
8.2 Regional Markets for Big Data 2017 – 2022
8.3 Big Data Revenue by Product Segment 2017 – 2022
8.3.1 Database Management Systems
8.3.2 Big Data Integration Tools
8.3.3 Application Infrastructure and Middleware
8.3.4 Business Intelligence Tools and Analytics Platforms
8.3.5 Big Data in Professional Services
9 Key Players in the Big Data Market
9.1 Vendor Assessment Matrix
9.2 1010Data
9.3 Accenture
9.4 Actian Corporation
9.5 Amazon
9.6 Apache Software Foundation
9.7 APTEAN (Formerly CDC Software)
9.8 Booz Allen Hamilton
9.9 Bosch Software Innovations: Bosch IoT Suite
9.10 Capgemini
9.11 Cisco Systems
9.12 Cloudera
9.13 CRAY Inc.
9.14 Computer Science Corporation (CSC)
9.15 DataDirect Network
9.16 Dell
9.17 Deloitte
9.18 EMC
9.19 Facebook
9.20 Fujitsu
9.21 General Electric (GE)
9.22 GoodData Corporation
9.23 Google
9.24 Guavus
9.25 HP
9.26 Hitachi Data Systems
9.27 Hortonworks
9.28 IBM
9.29 Informatica
9.30 Intel
9.31 Jasper (Cisco Jasper)
9.32 Juniper Networks
9.33 Marklogic
9.34 Microsoft
9.35 MongoDB (Formerly 10Gen)
9.36 MU Sigma
9.37 Netapp
9.38 NTT Data
9.39 Open Text (Actuate Corporation)
9.40 Opera Solutions
9.41 Oracle
9.42 Pentaho
9.43 Qlik Tech
9.44 Quantum
9.45 Rackspace
9.46 Revolution Analytics
9.47 Salesforce
9.48 SAP
9.49 SAS Institute
9.50 Sisense
9.51 Software AG/Terracotta
9.52 Splunk
9.53 Sqrrl
9.54 Supermicro
9.55 Tableau Software
9.56 Tata Consultancy Services
9.57 Teradata
9.58 Think Big Analytics
9.59 TIBCO
9.60 Tidemark Systems
9.61 VMware (Part of EMC)
9.62 Wipro
9.63 Workday (Platfora)
9.64 Zettics
10 Appendix: Big Data Support of Streaming IoT Data
10.1 Big Data Technology Market Outlook for Streaming IoT Data
10.1.1 IoT Data Management is a Ubiquitous Opportunity across Enterprise
10.1.2 IoT Data becomes a Big Data Revenue Opportunity
10.1.3 Real-time Streaming IoT Data Analytics becoming a Substantial Business Opportunity
10.2 Global Streaming IoT Data Analytics Revenue
10.2.1 Overall Streaming Data Analytics Revenue for IoT
10.2.2 Global Streaming IoT Data Analytics Revenue by App, Software, and Services
10.2.3 Global Streaming IoT Data Analytics Revenue in Industry Verticals
10.2.3.1 Streaming IoT Data Analytics Revenue in Retail
10.2.3.1.1 Streaming IoT Data Analytics Revenue by Retail Segment
10.2.3.1.2 Streaming IoT Data Analytics Retail Revenue by App, Software, and Service
10.2.3.2 Streaming IoT Data Analytics Revenue in Telecom and IT
10.2.3.2.1 Streaming IoT Data Analytics Revenue by Telecom and IT Segment
10.2.3.2.2 Streaming IoT Data Analytics Revenue by Telecom & IT App, Software, and Service
10.2.3.3 Streaming IoT Data Analytics Revenue in Energy and Utility
10.2.3.3.1 Streaming IoT Data Analytics Revenue by Energy and Utility Segment
10.2.3.3.2 Streaming IoT Data Analytics Energy and Utilities Revenue by App, Software, and Service
10.2.3.4 Streaming IoT Data Analytics Revenue in Government
10.2.3.4.1 Streaming IoT Data Analytics Revenue by Government Segment
10.2.3.4.2 Streaming IoT Data Analytics Government Revenue by App, Software, and Service
10.2.3.5 Streaming IoT Data Analytics Revenue in Healthcare and Life Science
10.2.3.5.1 Streaming IoT Data Analytics Revenue by Healthcare Segment
10.2.3.6 Streaming IoT Data Analytics Revenue in Manufacturing
10.2.3.6.1 Streaming IoT Data Analytics Revenue by Manufacturing Segment
10.2.3.6.2 Streaming IoT Data Analytics Manufacturing Revenue by App, Software, and Service
10.2.3.7 Streaming IoT Data Analytics Revenue in Transportation & Logistics
10.2.3.7.1 Streaming IoT Data Analytics Revenue by Transportation & Logistics Segment
10.2.3.7.2 Streaming IoT Data Analytics Transportation & Logistics Revenue by App, Software, and Service
10.2.3.8 Streaming IoT Data Analytics Revenue in Banking and Finance
10.2.3.8.1 Streaming IoT Data Analytics Revenue by Banking and Finance Segment
10.2.3.8.2 Streaming IoT Data Analytics Revenue by Banking & Finance App, Software, and Service
10.2.3.9 Streaming IoT Data Analytics Revenue in Smart Cities
10.2.3.9.1 Streaming IoT Data Analytics Revenue by Smart City Segment
10.2.3.10 Streaming IoT Data Analytics Revenue in Automotive
10.2.3.10.1 Streaming IoT Data Analytics Revenue by Automobile Industry Segment
10.2.3.10.2 Streaming IoT Data Analytics Revenue by Automotive Industry App, Software, and Service
10.2.3.11 Streaming IoT Data Analytics Revenue in Education
10.2.3.11.1 Streaming IoT Data Analytics Revenue by Education Industry Segment
10.2.3.11.2 Streaming IoT Data Analytics Revenue by Education Industry App, Software, and Service
10.2.3.12 Streaming IoT Data Analytics Revenue in Outsourcing Services
10.2.3.12.1 Streaming IoT Data Analytics Revenue by Outsourcing Segment
10.2.3.12.2 Streaming IoT Data Analytics Revenue by Outsourcing Industry App, Software, and Service
10.2.3.13 Streaming IoT Data Analytics Revenue by Leading Vendor Platform
10.3 Regional Streaming IoT Data Analytics Revenue
10.3.1 Revenue in Region
10.3.2 APAC Market Revenue
10.3.3 Europe Market Revenue
10.3.4 North America Market Revenue
10.3.5 Latin America Market Revenue
10.3.6 ME&A Market Revenue
10.4 Streaming IoT Data Analytics Revenue by Country 2016 – 2021
10.4.1 Revenue by APAC Countries
10.4.1.1 Leading Countries
10.4.1.2 Japan Market Revenue
10.4.1.3 China Market Revenue
10.4.1.4 India Market Revenue
10.4.1.5 Australia Market Revenue
10.4.2 Revenue by Europe Countries
10.4.2.1 Leading Countries
10.4.2.2 Germany Market Revenue
10.4.2.3 UK Market Revenue
10.4.2.4 France Market Revenue
10.4.3 Revenue by North America Countries
10.4.3.1 Leading Countries
10.4.3.2 US Market Revenue
10.4.3.3 Canada Market Revenue
10.4.4 Revenue by Latin America Countries
10.4.4.1 Leading Countries
10.4.4.2 Brazil Market Revenue
10.4.4.3 Mexico Market Revenue
10.4.5 Revenue by ME&A Countries
10.4.5.1 Leading Countries
10.4.5.2 South Africa Market Revenue
10.4.5.3 UAE Market Revenue

2.1 Understanding Multi-access Edge Computing 
2.1.1 Edge Computing 
2.1.2 Edge Computing vs. Cluster Computing 
2.1.3 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 
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 MEC Value Chain for Edge Cloud Computing 
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 Akamai Cloudlet Solution 
3.4.7 OPENi Cloudlet Storage Framework 
3.5 MEC Deployment 
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
5 MEC Ecosystem 
5.1 Network Ecosystem 
5.2 MEC Ecosystem Players 
5.2.1 Software and ASPs 
5.2.2 OTT Service and Content Providers 
5.2.3 Network Infrastructure and Equipment Providers 
5.2.4 Mobile Network Operators 
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 
7 MEC Market Forecasts 2017 – 2022 
7.1 Global Market 2017 – 2022 
7.1.1 Combined MEC Market 
7.1.2 MEC Market by Segment 
7.1.2.1 MEC Cloud Server Market 
7.1.2.2 MEC Equipment Market 
7.1.2.3 MEC Platform Market 
7.1.2.4 MEC Software and API Market 
7.1.2.5 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 2017 – 2022 
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 2017 – 2022 
7.3.1 Global MEC Network Users/Devices 
7.3.2 MEC Network User by Supporting Network 
7.3.3 Regional MEC Network User 
7.3.3.1 North America User 
7.3.3.2 APAC User 
7.3.3.3 Europe User 
8 Conclusions and Recommendations