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市場調查報告書

巨量資料市場:商務案例,市場分析,預測

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

出版商 Mind Commerce 商品編碼 434554
出版日期 內容資訊 英文 281 Pages
商品交期: 最快1-2個工作天內
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巨量資料市場:商務案例,市場分析,預測 The Big Data Market: Business Case, Market Analysis and Forecasts 2018 - 2023
出版日期: 2018年02月15日 內容資訊: 英文 281 Pages
簡介

本報告提供全球巨量資料市場相關調查分析,商務案例,應用的使用案例,業者情勢,價值鏈分析,案例研究等系統性資訊。

第1章 摘要整理

第2章 簡介

第3章 巨量資料的課題與機會

  • 巨量資料基礎設施的確保
  • 非結構化資料和IoT

第4章 巨量資料技術和商務案例

  • 巨量資料技術
  • 新興技術、工具、技巧
  • 巨量資料發展藍圖
  • 推動市場要素
  • 市場障礙

第5章 巨量資料的主要部門

  • 產業用網際網路和M2M
  • 零售和飯店
  • 媒體
  • 公共事業
  • 金融服務
  • 醫療保健、醫藥品
  • 政府、國防安全保障
  • 其他

第6章 巨量資料的價值鏈

  • 巨量資料價值的片斷化
  • 資料收集、供應
  • 資料保管、商業智慧
  • 分析和視覺化
  • 動作、商務流程管理
  • 資料管治

第7章 巨量資料分析

  • 巨量資料分析所扮演的角色和重要性
  • 反應式 vs. 主動式分析
  • 技術、實行方法

第8章 標準化、規定的舉措

第9章 巨量資料的全球市場與預測

  • 全球巨量資料市場
  • 巨量資料的各地區市場
  • 巨量資料的主要國家
  • 巨量資料的收益:各產品區隔

第10章 巨量資料的主要企業

  • 供應商評估矩陣
  • 1010Data (Advance Communication Corp.)
  • Accenture
  • Actian Corporation
  • Alteryx
  • Amazon
  • Anova Data
  • Apache Software Foundation
  • APTEAN (Formerly CDC Software)
  • Booz Allen Hamilton
  • Bosch Software Innovations: Bosch IoT Suite
  • Capgemini
  • Cisco Systems
  • Cloudera
  • CRAY Inc.
  • Computer Science Corporation (CSC)
  • DataDirect Network
  • Dell EMC
  • Deloitte
  • Facebook
  • 富士通
  • General Electric (GE)
  • GoodData Corporation
  • Google
  • Guavus
  • HP Enterprise
  • Hitachi Data Systems
  • Hortonworks
  • IBM
  • Informatica
  • Intel
  • Jasper (Cisco Jasper)
  • Juniper Networks
  • Longview
  • Marklogic
  • Microsoft
  • Microstrategy
  • MongoDB (Formerly 10Gen)
  • MU Sigma
  • Netapp
  • NTT Data
  • Open Text (Actuate Corporation)
  • Opera Solutions
  • Oracle
  • Pentaho (Hitachi)
  • Qlik Tech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Tata Consultancy Services
  • Teradata
  • Think Big Analytics
  • TIBCO
  • Verint Systems
  • VMware (EMC的一部分)
  • Wipro
  • Workday (Platfora)

第11章 附錄

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目錄

Overview:

Solutions for managing unstructured data are evolving beyond systems aligned towards primarily human-generated data (such as social networking, messaging, and browsing habits) towards increasingly greater emphasis upon machine-generated data found across many industry verticals. For example, manufacturing and healthcare are anticipated to create massive amounts of data that may be rendered useful only through advanced analytics and various Artificial Intelligence (AI) technologies. Emerging networks and systems such as IoT and edge computing will generate substantial amounts of unstructured data, which will present both technical challenges and market opportunities for operating companies and their vendors.

Big Data solution provider dynamics are evolving almost as much as the data management technologies themselves. While some companies rely upon proprietary solutions, many leading companies such as Hortonworks and Cloudera offer products and services primarily based on open source Apache Hadoop technology. One important distinction between market leaders is collaboration vs. competition. For example, Cloudera competes with IBM, Microsoft and others in data science and AI whereas Hortonworks partners with these companies.

In terms of data management and analytics technologies, the Big Data industry is experiencing profound changes across the entire stack including infrastructure, security, analytics, and the application layer. The data services industry as whole is shifting from host-based network topologies to cloud-based, data-centric architectures, thereby creating enormous challenges and opportunities for transitioning and securing data systems. In concert with this shift, Big Data infrastructure will require strategic governance and framework for optimized security.

The Big Data Market: Business Case, Market Analysis and Forecasts 2018 - 2023 provides an in-depth 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 report also evaluates the components of Big Data infrastructure and security framework. Additional topics covered in this report include:
  • Big Data Technology: Analysis of infrastructure and important issues such as security and privacy
  • Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
  • The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
  • The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
  • Big Data Vendor Assessment: Assessment of the vendor landscape of leading players within the Big Data market
  • Market Analysis and Forecasts: Global and regional assessment of the market size and forecasts for 2018 to 2023

Report Benefits:

  • Detailed forecasts 2018 - 2023
  • Identify leading market segments
  • Learn about Big Data technologies
  • Identify key players and strategies
  • Understand market drivers and barriers
  • Identify opportunities in IoT data analytics
  • Understand regulatory issues and initiatives
  • Understand business case for enterprise Big Data

Target Audience:

  • IoT companies
  • 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

Table of Contents

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
  • 8.3. OASIS
  • 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
    • 11.3.1. Revenue in Region
    • 11.3.2. APAC Market Revenue
    • 11.3.3. Europe Market Revenue
    • 11.3.4. North America Market Revenue
    • 11.3.5. Latin America Market Revenue
    • 11.3.6. ME&A Market Revenue
  • 11.4. Streaming IoT Data Analytics Revenue by Country
    • 11.4.1. Revenue by APAC Countries
    • 11.4.2. Revenue by Europe Countries
    • 11.4.3. Revenue by North America Countries
    • 11.4.4. Revenue by Latin America Countries
    • 11.4.5. Revenue by ME&A Countries

Figures

  • Figure 1: Big Data Ecosystem
  • Figure 2: Key Characteristics of Big Data
  • Figure 3: Big Data Use Cases in Industry Verticals
  • Figure 4: Big Data Stack
  • Figure 5: Framework for Big Data in IoT
  • Figure 2: NoSQL vs Legacy DB Performance Comparisons
  • Figure 7: Roadmap Big Data Technologies 2018 - 2030
  • Figure 8: The Big Data Value Chain
  • Figure 9: Big Data Value Flow
  • Figure 10: Big Data Analytics
  • Figure 11: Global Big Data Markets 2018-2023
  • Figure 12: Regional Big Data Markets 2018-2023
  • Figure 13: Database Management Systems 2018-2023
  • Figure 14: Data Integration and Quality Tools 2018-2023
  • Figure 15: Application Infrastructure and Middleware 2018-2023
  • Figure 16: Business Intelligence Tools and Analytics Platforms 2018-2023
  • Figure 17: Big Data in Professional Services 2018-2023
  • Figure 18: Big Data Vendor Ranking Matrix
  • Figure 19: Streaming IoT Data Sources Compared
  • Figure 20: Overall Streaming IoT Data Analytics

Tables

  • Table 1: Global Big Data Markets 2018-2023
  • Table 2: Regional Big Data Markets 2018-2023
  • Table 3: Big Data Markets by Product Segments 2018-2023
  • Table 4: Database Management Systems 2018-2023
  • Table 5: Data Integration Tools 2018-2023
  • Table 6: Application Infrastructure and Middleware 2018-2023
  • Table 7: Business Intelligence Tools and Analytics Platforms 2018-2023
  • Table 8: Big Data in Professional Services 2018-2023
  • Table 9: Big Data Analytics Platforms by Company
  • Table 10: Global Streaming IoT Data Analytics Revenue by App, Software, and Service
  • Table 11: Global Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 12: Retail Streaming IoT Data Analytics Revenue by Retail Segment
  • Table 13: Retail Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 14: Telecom & IT Streaming IoT Data Analytics Rev by Segment
  • Table 15: Telecom & IT Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 16: Energy & Utilities Streaming IoT Data Analytics Rev by Segment
  • Table 17: Energy & Utilities Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 18: Government Streaming IoT Data Analytics Revenue by Segment
  • Table 19: Government Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 20: Healthcare & Life Science Streaming IoT Data Analytics Revenue by Segment
  • Table 21: Healthcare & Life Science Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 22: Manufacturing Streaming IoT Data Analytics Revenue by Segment
  • Table 23: Manufacturing Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 24: Transportation & Logistics Streaming IoT Data Analytics Revenue by Segment
  • Table 25: Transportation & Logistics Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 26: Banking and Finance Streaming IoT Data Analytics Revenue by Segment
  • Table 27: Banking & Finance Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 28: Smart Cities Streaming IoT Data Analytics Revenue by Segment
  • Table 29: Smart Cities Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 30: Automotive Streaming IoT Data Analytics Revenue by Segment
  • Table 31: Automotive Streaming IoT Data Analytics Revenue by Apps, Software, and Services
  • Table 32: Education Streaming IoT Data Analytics Revenue by Segment
  • Table 33: Education Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 34: Outsourcing Service Streaming IoT Data Analytics Revenue by Segment
  • Table 35: Outsourcing Service Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 36: Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 37: Streaming IoT Data Analytics Revenue in Region
  • Table 38: APAC Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 39: APAC Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 40: APAC Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 41: Europe Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 42: Europe Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 43: Europe Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 44: North America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 45: North America Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 46: North America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 47: Latin America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 48: Latin America Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 49: Latin America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 50: ME&A Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 51: ME&A Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 52: ME&A Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 53: Streaming IoT Data Analytics Revenue by APAC Countries
  • Table 54: Japan Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 55: Japan Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 56: China Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 57: China Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 58: India Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 59: India Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 60: Australia Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 61: Australia Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 62: Streaming IoT Data Analytics Revenue by Europe Countries
  • Table 63: Germany Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 64: Germany Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 65: UK Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 66: UK Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 67: France Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 68: France Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 69: Streaming IoT Data Analytics Revenue by North America Countries
  • Table 70: US Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 71: US Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 72: Canada Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 73: Canada Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 74: Streaming IoT Data Analytics Revenue by Latin America Countries
  • Table 75: Brazil Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 76: Brazil Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 77: Mexico Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 78: Mexico Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 79: Streaming IoT Data Analytics Revenue by ME&A Countries
  • Table 80: South Africa Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 81: South Africa Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 82: UAE Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 83: UAE Streaming IoT Data Analytics Revenue in Industry Vertical
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