Overview:
This report 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 2023 to 2028. This report also evaluates the components of big data infrastructure and security framework.
This report also provides an analysis of leading big data solutions with key metrics such as streaming IoT data analytics revenue for leading providers. The report evaluates, compares, and contrasts vendors, and provides a vendor ranking matrix. Analysis takes into consideration solutions integrating both structured and unstructured data.
Select Report Findings:
- Big data in business intelligence apps will reach $63.5 billion by 2028
- Data Integration & Quality Tools to reach $1.2 billion globally by 2028
- Enterprise performance analytics will reach $39.9 billion globally by 2028
- Big data in supply chain management will reach $8.3 billion globally by 2028
- Combination of AI and IoT (AIoT) will rely upon advanced big data analytics software
- Real-time data will be a key value proposition for all use cases, segments, and solutions
- Market leading companies are rapidly integrated big data technologies with IoT infrastructure
Big data solutions are relied upon to gain insights from data files/sets so large and complex that it becomes difficult to process using traditional database management tools and data processing applications. The publisher sees key solution areas for big data as commerce, geospatial, finance, healthcare, transportation, and smart grids. Key technology integration includes AI, IoT, cloud and high-performance computing.
AI facilitates the efficient and effective supply of information to enterprises for optimized business decision-making. Some of the biggest opportunity areas are commercial applications, search in the big data environment, and mobility control for the generation of actionable business intelligence.
In terms of big data integration with cloud-based infrastructure, cloud solutions allow companies that previously required large investments into hardware to store data to do the same through the cloud at a lower cost. Companies save not only money but physical space where this hardware was previously stored. The trend to migrate to big data technologies is driven by the need for additional information derivable from analysis of all of the electronic data available to a business.
To realize the true potential to transform intelligence information from the huge amount of unstructured data, government agencies cannot leverage traditional data management technologies and DB techniques in terms of processing data. To understand patterns that exist in unstructured data, government agencies apply statistical models to large quantities of unstructured data.
Industry verticals of various types have challenges in capturing, organizing, storing, searching, sharing, transferring, analyzing, and using data to improve business. Big data is making a big impact in certain industries such as the healthcare, industrial, and retail sectors. Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage.
Big data technology will help financial institutions maximize the value of data and gain a competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time. As an example, in the transportation industry, real-time applications can match loads to a vehicle's capacity using data analytics. Big data provides shipping and delivery companies with real-time notifications and updates to increase efficiency and accuracy.
Big data technologies provide financial services firms with the capability to capture and analyze data, build predictive models, back-test, and simulate scenarios. Through iteration, firms will determine the most important variables and also key predictive models. Financial firms are increasingly migrating their data and analytics to the cloud, leading to reduced cost, better data management, and better customer service. Data and insights can also be transferred far quicker than before, allowing representatives to provide customers with real-time data backed insights.
Healthcare services can be applied more accurately with big data. Decisions based on real-time data and assistance from AI/ML solutions. Private health insurance providers can gain access to previously inaccessible information and databases through big data. Healthcare customer service processes can also be streamlined while providing personalized more personalized medical care to individuals.
Big data analytics allows retail companies to examine and interact with their audience online in new ways. Predictive analytics can analyze a consumer's activity and recommend suggested items to them. Once a consumer has purchased from a company, big data can help retain that customer by better understanding what a person wants. For example, online retailers collect all its customers' data to provide a personalized experience, earning up to 40% of its revenue from its customers' data.
Customer Relationship Management benefits greatly from use of technology for organizing, automating, and synchronizing all customer-related information like sales, marketing, services, support and more. Big data represents a big business opportunity and it is poised to do more than just improve CRM.
Data analytics is useful for Supply Chain Management because it can analyze a variety of variables across a business' operations. SCM service providers use advanced analytics to analyze materials, products in inventory and imports/exports to better understand needs. This helps a business to manage its assets better, saving time and money. Data analytics can predict future risks based on history and a large set of data.
Select Companies in Report:
Table of Contents
1.0. Executive Summary
2.0. 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
- 2.1.3.1. Volume
- 2.1.3.2. Variety
- 2.1.3.3. Velocity
- 2.1.3.4. Variability
- 2.1.3.5. Complexity
- 2.2. Research Background
- 2.2.1. Scope
- 2.2.2. Coverage
- 2.2.3. Company Focus
3.0. 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
- 3.1.3.1. Securing State Data
- 3.1.3.2. Securing APIs
- 3.1.3.3. Securing Applications
- 3.1.3.4. Securing Data for Analysis
- 3.1.3.5. Securing User Privileges
- 3.1.3.6. Securing Enterprise Data
- 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 and Lightweight Data Interchange Format
- 3.2.3. Big Data in IoT and Lightweight Protocols
- 3.2.4. Big Data in IoT and Network Interoperability Protocols
- 3.2.5. Big Data in IoT Data Processing Scalability
4.0. Big Data Technologies and Business Cases
- 4.1. Big Data Technology
- 4.1.1. Hadoop
- 4.1.1.1. Other Apache Projects
- 4.1.2. NoSQL
- 4.1.2.1. Hbase
- 4.1.2.2. Cassandra
- 4.1.2.3. Mongo DB
- 4.1.2.4. Riak
- 4.1.2.5. CouchDB
- 4.1.3. MPP Databases
- 4.1.4. Other Technologies
- 4.1.4.1. Storm
- 4.1.4.2. Drill
- 4.1.4.3. Dremel
- 4.1.4.4. SAP HANA
- 4.1.4.5. Gremlin & Giraph
- 4.2. Emerging Technologies, Tools, and Techniques
- 4.2.1. Streaming Analytics
- 4.2.2. Cloud Technology
- 4.2.3. Search Technologies
- 4.2.4. Customizes Analytics Tools
- 4.2.5. Keywords Optimization
- 4.3. Big Data Roadmap
- 4.4. Market Drivers
- 4.4.1. Data Volume and 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. The Big Barrier: Privacy and Security Gaps
- 4.5.2. Workforce Reskilling and Organizational Resistance
- 4.5.3. Lack of Clear Big Data Strategies
- 4.5.4. Scalability and Maintenance Technical Challenges
- 4.5.5. Big Data Development Expertise
5.0. Key Big Data Sectors
- 5.1. Industrial Automation and Internet of Things
- 5.1.1. Big Data in Machine to Machine Solutions
- 5.1.2. Vertical Opportunities
- 5.2. Retail and Hospitality
- 5.2.1. Forecasting and Inventory Management
- 5.2.2. Customer Relationship Management
- 5.2.3. Determining Buying Patterns
- 5.2.4. Hospitality Use Cases
- 5.2.5. Personalized Marketing
- 5.3. Digital 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
- 5.6.1. Drug Development
- 5.6.2. Medical Data Analytics
- 5.6.3. Case Study: Identifying Heartbeat Patterns
- 5.7. Information and Communications Technologies
- 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: Administration 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
- 5.9.6. Extraction and Natural Resources
6.0. Big Data Value Chain
- 6.1. Fragmentation in the Big Data Value Chain
- 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
7.0. 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
- 7.4.14. Radial Basis Functions
- 7.4.14.1. Support Vector Machines
- 7.4.14.2. Naïve Bayes
- 7.4.14.3. K-nearest Neighbors
- 7.4.15. Geospatial Predictive Modelling
- 7.4.16. Regression Analysis
- 7.4.17. Social Network Analysis
8.0. Standardization and Regulatory Issues
- 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
9.0. Big Data in Industry Vertical Applications
- 9.1. Big Data Application in Manufacturing
- 9.2. Retail Applications
- 9.3. Big Data Application: Insurance Fraud Detection
- 9.4. Big Data Application: Media and Entertainment Industry
- 9.5. Big Data Application: Weather Patterns
- 9.6. Big Data Application: Transportation Industry
- 9.7. Big Data Application: Education Industry
- 9.8. Big Data Application: E-Commerce Personalization
- 9.9. Big Data Application: Oil and Gas Industry
- 9.10. Big Data Application: Telecommunication Industry
10.0. Key Big Data Companies and Solutions
- 10.1. Vendor Assessment Matrix
- 10.2. Competitive Landscape of Major Big Data Vendors
- 10.2.1. New Products Developments
- 10.2.2. Partnership, Merger, Acquisition, and Collaboration
- 10.3. 1010Data (ACC)
- 10.4. Accenture
- 10.5. Actian Corporation
- 10.6. AdvancedMD
- 10.7. Alation
- 10.8. Allscripts Healthcare Solutions
- 10.9. Alpine Data Labs
- 10.10. Alteryx
- 10.11. Amazon
- 10.12. Anova Data
- 10.13. Apache Software Foundation
- 10.14. Apple Inc.
- 10.15. APTEAN
- 10.16. Athena Health Inc.
- 10.17. Attunity
- 10.18. Booz Allen Hamilton
- 10.19. Bosch
- 10.20. BGI
- 10.21. Big Panda
- 10.22. Bina Technologies Inc.
- 10.23. Capgemini
- 10.24. Cerner Corporation
- 10.25. Cisco Systems
- 10.26. CLC Bio
- 10.27. Cloudera
- 10.28. Cogito Ltd.
- 10.29. Compuverde
- 10.30. CRAY Inc.
- 10.31. Computer Science Corporation
- 10.32. Crux Informatics
- 10.33. Ctrl Shift
- 10.34. Cvidya
- 10.35. Cybatar
- 10.36. DataDirect Network
- 10.37. Data Inc.
- 10.38. Databricks
- 10.39. Dataiku
- 10.40. Datameer
- 10.41. Data Stax
- 10.42. Definiens
- 10.43. Dell EMC
- 10.44. Deloitte
- 10.45. Domo
- 10.46. eClinicalWorks
- 10.47. Epic Systems Corporation
- 10.48. Facebook
- 10.49. Fluentd
- 10.50. Flytxt
- 10.51. Fujitsu
- 10.52. Genalice
- 10.53. General Electric
- 10.54. GenomOncology
- 10.55. GoodData Corporation
- 10.56. Google
- 10.57. Greenplum
- 10.58. Grid Gain Systems
- 10.59. Groundhog Technologies
- 10.60. Guavus
- 10.61. Hack/reduce
- 10.62. HPCC Systems
- 10.63. HP Enterprise
- 10.64. Hitachi Data Systems
- 10.65. Hortonworks
- 10.66. IBM
- 10.67. Illumina Inc
- 10.68. Imply Corporation
- 10.69. Informatica
- 10.70. Inter Systems Corporation
- 10.71. Intel
- 10.72. IVD Industry Connectivity Consortium-IICC
- 10.73. Jasper (Cisco)
- 10.74. Juniper Networks
- 10.75. Knome, Inc.
- 10.76. Leica Biosystems (Danaher)
- 10.77. Longview
- 10.78. MapR
- 10.79. Marklogic
- 10.80. Mayo Medical Laboratories
- 10.81. McKesson Corporation
- 10.82. Medical Information Technology Inc.
- 10.83. Medio
- 10.84. Medopad
- 10.85. Microsoft
- 10.86. Microstrategy
- 10.87. MongoDB
- 10.88. MU Sigma
- 10.89. N-of-One
- 10.90. Netapp
- 10.91. NTT Data
- 10.92. Open Text (Actuate Corporation)
- 10.93. Opera Solutions
- 10.94. Oracle
- 10.95. Palantir Technologies Inc.
- 10.96. Pathway Genomics Corporation
- 10.97. Perkin Elmer
- 10.98. Pentaho (Hitachi)
- 10.99. Platfora
- 10.100. Qlik Tech
- 10.101. Quality Systems Inc.
- 10.102. Quantum
- 10.103. Quertle
- 10.104. Quest Diagnostics Inc.
- 10.105. Rackspace
- 10.106. Red Hat
- 10.107. Revolution Analytics
- 10.108. Roche Diagnostics
- 10.109. Rocket Fuel Inc.
- 10.110. Salesforce
- 10.111. SAP
- 10.112. SAS Institute
- 10.113. Selventa Inc.
- 10.114. Sense Networks
- 10.115. Shanghai Data Exchange
- 10.116. Sisense
- 10.117. Social Cops
- 10.118. Software AG/Terracotta
- 10.119. Sojern
- 10.120. Splice Machine
- 10.121. Splunk
- 10.122. Sqrrl
- 10.123. Sumo Logic
- 10.124. Sunquest Information Systems
- 10.125. Supermicro
- 10.126. Tableau Software
- 10.127. Tableau
- 10.128. Tata Consultancy Services
- 10.129. Teradata
- 10.130. ThetaRay
- 10.131. Thoughtworks
- 10.132. Think Big Analytics
- 10.133. TIBCO
- 10.134. Tube Mogul
- 10.135. Verint Systems
- 10.136. VolMetrix
- 10.137. VMware
- 10.138. Wipro
- 10.139. Workday (Platfora)
- 10.140. WuXi NextCode Genomics
- 10.141. Zoomdata
11.0. Overall Big Data Market Analysis and Forecasts 2023-2028
- 11.1. Global Big Data Marketplace
- 11.2. Big Data Market by Solution Type
- 11.3. Regional Big Data Market
12.0. Big Data Market Segment Analysis and Forecasts 2023-2028
- 12.1. Big Data Market by Management Utilities 2023-2028
- 12.1.1. Market for General Use Analytics Servers and related Hardware 2023-2028
- 12.1.2. Market for Big Data Application Infrastructure and Middleware 2023-2028
- 12.1.3. Market for Data Integration Tools and Data Quality Tools 2023-2028
- 12.1.4. Big Data Market for Database Management Systems 2023-2028
- 12.1.5. Big Data Market for Storage Management 2023-2028
- 12.2. Big Data Market by Functional Segment 2023-2028
- 12.2.1. Big Data in Supply Chain Management 2023-2028
- 12.2.2. Big Data in Workforce Analytics 2023-2028
- 12.2.3. Big Data in Enterprise Performance Analytics 2023-2028
- 12.2.4. Big Data in Professional Services 2023-2028
- 12.2.5. Big Data in Business Intelligence 2023-2028
- 12.2.6. Big Data in Social Media and Content Analytics 2023-2028
- 12.3. Market for Big Data in Emerging Technologies 2023-2028
- 12.3.1. Big Data in Internet of Things 2023-2028
- 12.3.2. Big Data in Smart Cities 2023-2028
- 12.3.3. Big Data in Blockchain and Cryptocurrency 2023-2028
- 12.3.4. Big Data in Augmented and Virtual Reality 2023-2028
- 12.3.5. Big Data in Cybersecurity 2023-2028
- 12.3.6. Big Data in Smart Assistants 2023-2028
- 12.3.7. Big Data in Cognitive Computing 2023-2028
- 12.3.8. Big Data in Customer Relationship Management 2023-2028
- 12.3.9. Big Data in Spatial Information 2023-2028
- 12.4. Big Data Market by Industry Type 2023-2028
- 12.5. Regional Big Data Markets 2023-2028
- 12.5.1. North America Market for Big Data 2023-2028
- 12.5.2. South American Market for Big Data 2023-2028
- 12.5.3. Western European Market for Big Data 2023-2028
- 12.5.4. Central and Eastern European Market for Big Data 2023-2028
- 12.5.5. Asia Pacific Market for Big Data 2023-2028
- 12.5.6. Middle East and Africa Market for Big Data 2023-2028
13.0. Appendix: Big Data Support of Streaming IoT Data
- 13.1. Big Data Technology Market Outlook for Streaming IoT Data
- 13.1.1. IoT Data Management is a Ubiquitous Opportunity across Enterprise
- 13.1.2. IoT Data becomes a Big Data Revenue Opportunity
- 13.1.3. Real-time Streaming IoT Data Analytics is a Substantial Opportunity
- 13.2. Global Streaming IoT Data Analytics Revenue 2023-2028
- 13.2.1. Overall Streaming Data Analytics Revenue for IoT 2023-2028
- 13.2.2. Global Streaming IoT Data Analytics Revenue by App, Software, and Services 2023-2028
- 13.2.3. Global Streaming IoT Data Analytics Revenue in Industry Verticals 2023-2028
- 13.2.3.1. Streaming IoT Data Analytics Revenue in Retail
- 13.2.3.1.1. Streaming IoT Data Analytics Revenue by Retail Segment
- 13.2.3.1.2. Streaming IoT Data Analytics Retail Revenue by App, Software, and Service
- 13.2.3.2. Streaming IoT Data Analytics Revenue in Telecom and IT
- 13.2.3.2.1. Streaming IoT Data Analytics Revenue by Telecom and IT Segment
- 13.2.3.2.2. Streaming IoT Data Analytics Revenue by Telecom & IT App, Software, and Service
- 13.2.3.3. Streaming IoT Data Analytics Revenue in Energy and Utility
- 13.2.3.3.1. Streaming IoT Data Analytics Revenue by Energy and Utility Segment
- 13.2.3.3.2. Streaming IoT Data Analytics Energy and Utilities Revenue by App, Software, and Service
- 13.2.3.4. Streaming IoT Data Analytics Revenue in Government
- 13.2.3.4.1. Streaming IoT Data Analytics Revenue by Government Segment
- 13.2.3.4.2. Streaming IoT Data Analytics Government Revenue by App, Software, and Service
- 13.2.3.5. Streaming IoT Data Analytics Revenue in Healthcare and Life Science
- 13.2.3.5.1. Streaming IoT Data Analytics Revenue by Healthcare Segment
- 13.2.3.6. Streaming IoT Data Analytics Revenue in Manufacturing
- 13.2.3.6.1. Streaming IoT Data Analytics Revenue by Manufacturing Segment
- 13.2.3.6.2. Streaming IoT Data Analytics Manufacturing Revenue by App, Software, and Service
- 13.2.3.7. Streaming IoT Data Analytics Revenue in Transportation & Logistics
- 13.2.3.7.1. Streaming IoT Data Analytics Revenue by Transportation & Logistics Segment
- 13.2.3.7.2. Streaming IoT Data Analytics Transportation & Logistics Revenue by App, Software, and Service
- 13.2.3.8. Streaming IoT Data Analytics Revenue in Banking and Finance
- 13.2.3.8.1. Streaming IoT Data Analytics Revenue by Banking and Finance Segment
- 13.2.3.8.2. Streaming IoT Data Analytics Revenue by Banking and Finance App, Software, and Service
- 13.2.3.9. Streaming IoT Data Analytics Revenue in Smart Cities
- 13.2.3.9.1. Streaming IoT Data Analytics Revenue by Smart City Segment
- 13.2.3.9.2. Streaming IoT Data Analytics Revenue by Smart Cities App, Software, and Service
- 13.2.3.10. Streaming IoT Data Analytics Revenue in Automotive
- 13.2.3.10.1. Streaming IoT Data Analytics Revenue by Automobile Industry Segment
- 13.2.3.10.2. Streaming IoT Data Analytics Revenue by Automotive Industry App, Software, and Service
- 13.2.3.11. Streaming IoT Data Analytics Revenue in Education
- 13.2.3.11.1. Streaming IoT Data Analytics Revenue by Education Industry Segment
- 13.2.3.11.2. Streaming IoT Data Analytics Revenue by Education Industry App, Software, and Service
- 13.2.3.12. Streaming IoT Data Analytics Revenue in Outsourcing Services
- 13.2.3.12.1. Streaming IoT Data Analytics Revenue by Outsourcing Segment
- 13.2.3.12.2. Streaming IoT Data Analytics Revenue by Outsourcing Industry App, Software, and Service
- 13.2.3.13. Streaming IoT Data Analytics Revenue by Leading Vendor Platform
- 13.3. Regional Streaming IoT Data Analytics Revenue 2023-2028
- 13.3.1. Streaming IoT Data Analytics Revenue by Region 2023-2028
- 13.3.2. Streaming IoT Data Analytics in Asia Pac Market Revenue 2023-2028
- 13.3.3. Streaming IoT Data Analytics in Europe Market Revenue 2023-2028
- 13.3.4. Streaming IoT Data Analytics in North America Market Revenue 2023-2028
- 13.3.5. Streaming IoT Data Analytics in Latin America Market Revenue 2023-2028
- 13.3.6. Streaming IoT Data Analytics in MEA Market Revenue 2023-2028
- 13.4. Streaming IoT Data Analytics Revenue by Country 2023-2028
- 13.4.1. Streaming IoT Data Analytics Revenue by APAC Countries 2023-2028
- 13.4.1.1. Leading Countries
- 13.4.1.2. Japan Market Revenue
- 13.4.1.3. China Market Revenue
- 13.4.1.4. India Market Revenue
- 13.4.1.5. Australia Market Revenue
- 13.4.2. Streaming IoT Data Analytics Revenue by Europe Countries 2023-2028
- 13.4.2.1. Leading Countries
- 13.4.2.2. Germany Market Revenue
- 13.4.2.3. UK Market Revenue
- 13.4.2.4. France Market Revenue
- 13.4.3. Streaming IoT Data Analytics Revenue by North America Countries 2023-2028
- 13.4.3.1. Leading Countries
- 13.4.3.2. US Market Revenue
- 13.4.3.3. Canada Market Revenue
- 13.4.4. Streaming IoT Data Analytics Revenue by Latin America Countries 2023-2028
- 13.4.4.1. Leading Countries
- 13.4.4.2. Brazil Market Revenue
- 13.4.4.3. Mexico Market Revenue
- 13.4.5. Streaming IoT Data Analytics Revenue by ME&A Countries 2023-2028
- 13.4.5.1. Leading Countries
- 13.4.5.2. South Africa Market Revenue
- 13.4.5.3. UAE Market Revenue