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大數據市場：主要參與者、解決方案、用例、基礎設施、數據集成、支持物聯網、部署模型、行業服務 (2023-2028)
市場調查報告書
商品編碼
1194927

大數據市場：主要參與者、解決方案、用例、基礎設施、數據集成、支持物聯網、部署模型、行業服務 (2023-2028)

Big Data Market by Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals 2023 - 2028
出版日期: | 出版商: Mind Commerce | 英文 322 Pages | 商品交期: 最快1-2個工作天內

價格

在這份報告中，我們分析了全球大數據市場，包括業務案例問題和分析、應用用例、供應商格局、價值鏈分析、行業趨勢量化評估（2023-2028）、大數據市場以及研究數據基礎設施和安全框架組件，我們編譯和提供信息，例如領先供應商的概況、比較分析和領先解決方案的概述。

主要發現

  • 到 2028 年，商業智能 (BI) 大數據的市場規模預計將達到 635 億美元。
  • 到 2028 年，全球數據集成和質量工具市場預計將達到 12 億美元。
  • 到 2028 年，全球企業績效分析市場預計將達到 399 億美元。
  • 到 2028 年，全球供應鏈管理大數據市場預計將達到 83 億美元。
  • 人工智能和物聯網 (AIoT) 的結合將依賴於先進的大數據分析軟件。
  • 實時數據是每個用例、領域和解決方案的關鍵價值主張。
  • 市場領先的公司正在迅速將大數據技術集成到他們的 IoT 基礎架構中。

內容

第 1 章執行摘要

第二章介紹

  • 大數據概述
    • 大數據的定義
    • 大數據生態系統
    • 大數據的主要特徵
  • 分析背景

第 3 章大數據挑戰與機遇

  • 保護大數據基礎架構
    • 大數據基礎架構
    • 基礎設施問題
    • 大數據基礎架構機會
  • 非結構化數據和 IoT（物聯網）
    • 新協議、平台、流媒體/分析、軟件/分析工具
    • 物聯網大數據和輕量級數據交換格式
    • 物聯網大數據和輕量級協議
    • IoT 大數據和網絡互操作性協議
    • 物聯網數據處理中的大數據可擴展性

第四章大數據技術與商業案例

  • 大數據技術
    • Hadoop
    • NoSQL
    • MPP數據庫
    • 其他技術
  • 新技術、工具和技巧
    • 流媒體分析
    • 雲技術
    • 搜索技術
    • 自定義分析工具
    • 關鍵字優化
  • 大數據路線圖
  • 市場驅動因素
    • 數據的數量和類型
    • 企業和運營商更多地採用大數據
    • 大數據軟件的成熟度
    • 大型網絡公司對大數據的持續投資
    • 業務驅動力
  • 市場壁壘
    • 最大的障礙：隱私與安全之間的差距
    • 員工再培訓和組織阻力
    • 缺乏明確的大數據戰略
    • 可擴展性和維護方面的技術問題
    • 擁有大數據開發方面的專業知識

第 5 章關鍵大數據行業

  • 工業自動化和物聯網
    • M2M（機器對機器）解決方案的大數據
    • 每個行業的市場機會
  • 零售/酒店業
    • 預測和庫存管理
    • 客戶關係管理 (CRM)
    • 購買模式決策
    • 酒店用例
    • 個性化營銷
  • 數字媒體
    • 社交媒體
    • 社交遊戲分析
    • 其他行業對社交媒體分析的使用
    • 互聯網關鍵字搜索
  • 實用工具
    • 運營數據分析
    • 未來的應用領域
  • 金融業務
    • 欺詐分析和緩解、風險分析
    • 商家資助的獎勵計劃
    • 客戶細分
    • 保留客戶並提供個性化產品
    • 保險公司
  • 醫療
    • 藥物開發
    • 醫療數據分析
    • 案例研究：心跳模式識別
  • 信息和通信技術 (ICT)
    • 運營商分析：客戶/使用情況分析和服務優化
    • 大數據分析工具
    • 語音分析
    • 新產品和服務
  • 政府：行政管理和國土安全
    • 大數據研究
    • 統計分析
    • 語言翻譯
    • 為大眾開發新的應用程序
    • 犯罪追蹤
    • 信息收集
    • 欺詐檢測和創收
  • 其他部門
    • 航空
    • 運輸和物流：優化車隊利用率
    • 體育統計數據的實時處理
    • 教育
    • 製造業
    • 採掘/自然資源

第六章大數據價值鏈

  • 大數據價值鏈的碎片化
  • 數據採集和配置
  • 數據倉庫和商業智能 (BI)
  • 分析和可視化
  • 行動和業務流程管理
  • 數據治理

第 7 章大數據分析

  • 大數據分析的作用和重要性
  • 大數據分析流程
  • 反應式和主動式分析
  • 技術和實施方法
    • 網格計算
    • 數據庫內處理
    • 內存分析
    • 數據挖掘
    • 預測分析
    • 自然語言處理 (NLP)
    • 文本分析
    • 視覺分析
    • 學習關聯規則
    • 分類樹分析
    • 機器學習
    • 神經網絡
    • 多層感知器
    • 徑向基函數
    • 地理空間預測建模
    • 回歸分析
    • 社交網絡分析

第 8 章標準化和監管問題

  • CSCC（雲標準客戶委員會）
  • 美國國家標準與技術研究院 (NIST)
  • 綠洲
  • ODF（開放數據基金會）
  • ODCA（開放數據中心聯盟）
  • CSA（雲安全聯盟）
  • 國際電信聯盟 (ITU)
  • 國際標準化組織 (ISO)

第9章大數據在各行業的應用領域

  • 大數據在製造業中的應用
  • 零售用途
  • 使用大數據：檢測保險欺詐
  • 大數據的使用：媒體和娛樂行業
  • 大數據的使用：天氣模式
  • 大數據的使用：運輸行業
  • 大數據的使用：教育行業
  • 大數據的使用：電子商務的個性化
  • 大數據的使用：石油和天然氣行業
  • 大數據的利用：電信行業

第10章大數據主要公司及解決方案

  • 供應商評估矩陣
  • 主要大數據供應商的競爭格局
    • 新產品開發
    • 商業聯盟/合作、企業合併/收購 (M&A)
  • 1010Data (ACC)
  • Accenture
  • Actian Corporation
  • AdvancedMD
  • Alation
  • Allscripts Healthcare Solutions
  • Alpine Data Labs
  • Alteryx
  • Amazon
  • Anova Data
  • Apache Software Foundation
  • Apple Inc.
  • APTEAN
  • Athena Health Inc.
  • Attunity
  • Booz Allen Hamilton
  • Bosch
  • BGI
  • Big Panda
  • Bina Technologies Inc.
  • Capgemini
  • Cerner Corporation
  • Cisco Systems
  • CLC Bio
  • Cloudera
  • Cogito Ltd.
  • Compuverde
  • CRAY Inc.
  • Computer Science Corporation
  • Crux Informatics
  • Ctrl Shift
  • Cvidya
  • Cybatar
  • DataDirect Network
  • Data Inc.
  • Databricks
  • Dataiku
  • Datameer
  • Data Stax
  • Definiens
  • Dell EMC
  • Deloitte
  • Domo
  • eClinicalWorks
  • Epic Systems Corporation
  • Facebook
  • Fluentd
  • Flytxt
  • Fujitsu
  • Genalice
  • General Electric
  • GenomOncology
  • GoodData Corporation
  • Google
  • Greenplum
  • Grid Gain Systems
  • Groundhog Technologies
  • Guavus
  • Hack/reduce
  • HPCC Systems
  • HP Enterprise
  • Hitachi Data Systems
  • Hortonworks
  • IBM
  • Illumina Inc
  • Imply Corporation
  • Informatica
  • Inter Systems Corporation
  • Intel
  • IVD Industry Connectivity Consortium-IICC
  • Jasper (Cisco)
  • Juniper Networks
  • Knome, Inc.
  • Leica Biosystems (Danaher)
  • Longview
  • MapR
  • Marklogic
  • Mayo Medical Laboratories
  • McKesson Corporation
  • Medical Information Technology Inc.
  • Medio
  • Medopad
  • Microsoft
  • Microstrategy
  • MongoDB
  • MU Sigma
  • N-of-One
  • Netapp
  • NTT Data
  • Open Text (Actuate Corporation)
  • Opera Solutions
  • Oracle
  • Palantir Technologies Inc.
  • Pathway Genomics Corporation
  • Perkin Elmer
  • Pentaho (Hitachi)
  • Platfora
  • Qlik Tech
  • Quality Systems Inc.
  • Quantum
  • Quertle
  • Quest Diagnostics Inc.
  • Rackspace
  • Red Hat
  • Revolution Analytics
  • Roche Diagnostics
  • Rocket Fuel Inc.
  • Salesforce
  • SAP
  • SAS Institute
  • Selventa Inc.
  • Sense Networks
  • Shanghai Data Exchange
  • Sisense
  • Social Cops
  • Software AG/Terracotta
  • Sojern
  • Splice Machine
  • Splunk
  • Sqrrl
  • Sumo Logic
  • Sunquest Information Systems
  • Supermicro
  • Tableau Software
  • Tableau
  • Tata Consultancy Services
  • Teradata
  • ThetaRay
  • Thoughtworks
  • Think Big Analytics
  • TIBCO
  • Tube Mogul
  • Verint Systems
  • VolMetrix
  • VMware
  • Wipro
  • Workday (Platfora)
  • WuXi NextCode Genomics
  • Zoomdata

第11章大數據市場總體分析及預測（2023-2028）

  • 全球大數據市場
  • 大數據市場：按解決方案類型分類
  • 大數據市場：按地區

第 12 章大數據市場分析和預測：按細分市場 (2023-2028)

  • 大數據市場：按管理職能分類 (2023-2028)
    • 通用分析服務器及相關硬件市場 (2023-2028)
    • 大數據應用基礎架構和中間件市場（2023 年至 2028 年）
    • 數據集成工具和數據質量工具市場（2023 年至 2028 年）
    • 數據庫管理系統的大數據市場（2023 年至 2028 年）
    • 存儲管理大數據市場（2023-2028 年）
  • 大數據市場：按功能細分 (2023-2028)
    • 供應鏈管理大數據（2023-2028 年）
    • 用於勞動力分析的大數據 (2023-2028)
    • 用於企業績效分析的大數據（2023-2028 年）
    • 專業服務大數據 (2023-2028)
    • 用於商業智能 (BI) 的大數據 (2023-2028)
    • 用於社交媒體和內容分析的大數據 (2023-2028)
  • 新興技術中的大數據市場（2023 年至 2028 年）
    • 物聯網大數據 (2023-2028)
    • 智慧城市的大數據 (2023-2028)
    • 區塊鍊和加密貨幣的大數據 (2023-2028)
    • 用於增強現實/虛擬現實 (AR/VR) 的大數據 (2023-2028)
    • 網絡安全大數據 (2023-2028)
    • 智能助手的大數據 (2023-2028)
    • 用於認知計算的大數據（2023 年至 2028 年）
    • 客戶關係管理 (CRM) 大數據（2023-2028 年）
    • 空間信息大數據 (2023-2028)
  • 大數據市場：按行業分類 (2023-2028)
  • 大數據市場：按地區劃分 (2023-2028)
    • 北美大數據市場（2023 年至 2028 年）
    • 南美洲的大數據市場（2023 年至 2028 年）
    • 西歐大數據市場 (2023-2028)
    • 2023-2028 年中東歐大數據市場
    • 亞太大數據市場（2023-2028 年）
    • 中東和非洲大數據市場（2023 年至 2028 年）

第 13 章附錄：用於流式傳輸 IoT 數據的大數據

  • 流式 IoT 數據的大數據技術市場前景
  • 全球流媒體物聯網數據分析收入（2023 年至 2028 年）
  • 按地區劃分的流式 IoT 數據分析收入（2023 年至 2028 年）
  • 按國家/地區分列的流式 IoT 數據分析收入（2023 年至 2028 年）

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:

  • 1010Data
  • Accenture
  • Actian Corporation
  • AdvancedMD
  • Alation
  • Allscripts Healthcare Solutions
  • Alpine Data Labs
  • Alteryx
  • Amazon
  • Anova Data
  • Apache Software Foundation
  • Apple Inc.
  • APTEAN
  • AthenaHealth Inc.
  • Attunity
  • BGI
  • Big Panda
  • Bina Technologies Inc.
  • Booz Allen Hamilton
  • Bosch
  • Capgemini
  • Cerner Corporation
  • Cisco Systems
  • CLC Bio
  • Cloudera
  • Cogito Ltd.
  • Computer Science Corporation
  • Compuverde
  • CRAY Inc.
  • Crux Informatics
  • Ctrl Shift
  • Cvidya
  • Cybatar
  • Data Inc.
  • Data Stax
  • Databricks
  • DataDirect Network
  • Dataiku
  • Datameer
  • Definiens
  • Dell EMC
  • Deloitte
  • Domo
  • eClinicalWorks
  • Epic Systems Corporation
  • Facebook
  • Fluentd
  • Flytxt
  • Fujitsu
  • Genalice
  • General Electric
  • GenomOncology
  • GoodData Corporation
  • Google
  • Greenplum
  • Grid Gain Systems
  • Groundhog Technologies
  • Guavus
  • Hack/reduce
  • Hitachi Data Systems
  • Hortonworks
  • HP Enterprise
  • HPCC Systems
  • IBM
  • Illumina Inc
  • Imply Corporation
  • Industry Connectivity Consortium
  • Informatica
  • Intel
  • Inter Systems Corporation
  • Jasper (Cisco Jasper)
  • Juniper Networks
  • Knome,Inc.
  • Leica Biosystems (Danaher)
  • Longview
  • MapR
  • Marklogic
  • Mayo Medical Laboratories
  • McKesson Corporation
  • Medical Information Technology Inc.
  • Medio
  • Medopad
  • Microsoft
  • Microstrategy
  • MongoDB (Formerly 10Gen)
  • MU Sigma
  • N-of-One
  • Netapp
  • NTT Data
  • Open Text (Actuate Corporation)
  • Opera Solutions
  • Oracle
  • Palantir Technologies Inc.
  • Pathway Genomics Corporation
  • Pentaho (Hitachi)
  • Perkin Elmer
  • Platfora
  • Qlik Tech
  • Quality Systems Inc.
  • Quantum
  • Quertle
  • Quest Diagnostics Inc.
  • Rackspace
  • Red Hat
  • Revolution Analytics
  • Roche Diagnostics
  • Rocket Fuel Inc.
  • Salesforce
  • SAP
  • SAS Institute
  • Selventa Inc.
  • Sense Networks
  • Shanghai Data Exchange
  • Sisense
  • Social Cops
  • Software AG/Terracotta
  • Sojern
  • Splice Machine
  • Splunk
  • Sqrrl
  • Sumo Logic
  • Sunquest Information Systems
  • Supermicro
  • Tableau
  • Tableau Software
  • Tata Consultancy Services
  • Teradata
  • ThetaRay
  • Think Big Analytics
  • Thoughtworks
  • TIBCO
  • Tube Mogul
  • Verint Systems
  • VMware
  • VolMetrix
  • Wipro
  • Workday (Platfora)
  • WuXi NextCode Genomics
  • Zoomdata

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

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 6: NoSQL vs Legacy DB Performance Comparisons
  • Figure 7: Big Data Technology Roadmap
  • Figure 8: The Big Data Value Chain
  • Figure 9: Big Data Value Flow
  • Figure 10: Big Data Analytics
  • Figure 11: Big Data Vendor Ranking Matrix
  • Figure 12: Global Big Data Market
  • Figure 13: Big Data Market by Solution Type
  • Figure 14: Regional Market for Big Data
  • Figure 15: Big Data Market by Management Utilities
  • Figure 16: Market for Servers and Other Hardware
  • Figure 17: Market for Application Infrastructure and Middleware
  • Figure 18: Big Data Market for Data Integration Tools and Data Quality Tools
  • Figure 19: Market for Database Management Systems
  • Figure 20: Market for Storage Management
  • Figure 21: Big Data Market by Functional Segment
  • Figure 22: Big Data in Supply Chain Management
  • Figure 23: Big Data in Workforce Analytics
  • Figure 24: Big Data in Enterprise Performance Analytics
  • Figure 25: Big Data in Professional Services
  • Figure 26: Big Data in Business Intelligence
  • Figure 27: Big Data in Social Media and Content Analytics
  • Figure 28: Market for Big Data in Emerging Technologies
  • Figure 29: Big Data in Internet of Things
  • Figure 30: Big Data in Smart Cities
  • Figure 31: Big Data in Blockchain and Cryptocurrency
  • Figure 32: Big Data in Augmented and Virtual Reality
  • Figure 33: Big Data in Cybersecurity
  • Figure 34: Big Data in Smart Assistants
  • Figure 35: Big Data in Cognitive Computing
  • Figure 36: Big Data in CRM
  • Figure 37: Big Data in Spatial Information
  • Figure 38: Big Data Market by Industry Type
  • Figure 39: Big Data Applications in Transportation
  • Figure 40: Big Data Applications in Automobiles
  • Figure 41: North America Big Data Market by Solution Type
  • Figure 42: North America Big Data Market by Management Utilities
  • Figure 43: North America Big Data Market by Functional Segments
  • Figure 44: North America Market for Big Data in Emerging Technologies
  • Figure 45: North America Big Data Market by Industry Type
  • Figure 46: South America Big Data Market by Solution Type
  • Figure 47: South America Big Data Market by Management Utilities
  • Figure 48: South America Big Data Market by Functional Segments
  • Figure 49: South America Market for Big Data in Emerging Technologies
  • Figure 50: South America Big Data Market by Industry Type
  • Figure 51: Western Europe Big Data Market by Solution Type
  • Figure 52: Western Europe Big Data Market by Management Utilities
  • Figure 53: Western Europe Big Data Market by Functional Segments
  • Figure 54: Western Europe Market for Big Data in Emerging Technologies
  • Figure 55: Western Europe Big Data Market by Industry Type
  • Figure 56: Central and Eastern Europe Big Data Market by Solution Type
  • Figure 57: Central and Eastern Europe Big Data Market by Management Utilities
  • Figure 58: Central and Eastern Europe Big Data Market by Functional Segments
  • Figure 59: Central and Eastern Europe Market for Big Data in Emerging Tech
  • Figure 60: Central and Eastern Europe Big Data Market by Industry Type
  • Figure 61: APAC Big Data Market by Solution Type
  • Figure 62: APAC Big Data Market by Management Utilities
  • Figure 63: APAC Big Data Market by Functional Segment
  • Figure 64: APAC Market for Big Data in Emerging Technologies
  • Figure 65: APAC Big Data Market by Industry Type
  • Figure 66: MEA Big Data Market by Solution Type
  • Figure 67: MEA Big Data Market by Management Utilities
  • Figure 68: MEA Big Data Market by Functional Segments
  • Figure 69: MEA Market for Big Data in Emerging Technologies
  • Figure 70: MEA Big Data Market by Industry Type
  • Figure 71: Streaming IoT Data Sources Compared
  • Figure 72: Overall Streaming IoT Data Analytics

Tables

  • Table 1: Global Markets for Big Data
  • Table 2: Big Data Markets by the Type of Plan
  • Table 3: Regional Markets for Big Data
  • Table 4: Big Data Market by Management Utilities
  • Table 5: Market for Servers and Other Hardware
  • Table 6: Big Data Market for Application Infrastructure and Middleware
  • Table 7: Market for Data Integration Tools and Data Quality Tools
  • Table 8: Market for Database Management Systems
  • Table 9: Market for Storage Management
  • Table 10: Big Data Market by Functional Segment
  • Table 11: Big Data in Supply Chain Management
  • Table 12: Big Data in Workforce Analytics
  • Table 13: Big Data in Enterprise Performance Analytics
  • Table 14: Big Data in Professional Services
  • Table 15: Big Data in Business Intelligence
  • Table 16: Big Data in Social Media and Content Analytics
  • Table 17: Market for Big Data in Emerging Technologies
  • Table 18: Big Data in Internet of Things
  • Table 19: Big Data in Smart Cities
  • Table 20: Big Data in Blockchain Technology and Cryptocurrency
  • Table 21: Big Data in Augmented and Virtual Reality
  • Table 22: Big Data in Cybersecurity
  • Table 23: Big Data in Smart Assistants
  • Table 24: Big Data in Cognitive Computing
  • Table 25: Big Data in CRM
  • Table 26: Big Data in Spatial Information
  • Table 27: Big Data Market by Industry Type
  • Table 28: Big Data Applications in Transportation Market
  • Table 29: Big Data Applications in Automobiles
  • Table 30: North America Big Data Market by Solution Type
  • Table 31: North America Big Data Market by Management Utilities
  • Table 32: North America Big Data Market by Functional Segments
  • Table 33: North America Market for Big Data in Emerging Technologies
  • Table 34: North America Big Data Market by Industry Type
  • Table 35: South America Big Data Market by Solution Type
  • Table 36: South America Big Data Market by Management Utilities
  • Table 37: South America Big Data Market by Functional Segments
  • Table 38: South America Market for Big Data in Emerging Technologies
  • Table 39: South America Big Data Market by Industry Type
  • Table 40: Western Europe Big Data Market by Solution Type
  • Table 41: Western Europe Big Data Market by Management Utilities
  • Table 42: Western Europe Big Data Market by Functional Segments
  • Table 43: Western Europe Market for Big Data in Emerging Technologies
  • Table 44: Western Europe Big Data Market by Industry Type
  • Table 45: Central and Eastern Europe Big Data Market by Solution Type
  • Table 46: Central and Eastern Europe Big Data Market by Management Utilities
  • Table 47: Central and Eastern Europe Big Data Market by Functional Segments
  • Table 48: Central and Eastern Europe Market for Big Data in Emerging Tech
  • Table 49: Central and Eastern Europe Big Data Market by Industry Type
  • Table 50: APAC Big Data Market by Solution Type
  • Table 51: APAC Big Data Market by Management Utilities
  • Table 52: APAC Big Data Market by Functional Segments
  • Table 53: APAC Market for Big Data in Emerging Technologies
  • Table 54: APAC Big Data Market by Industry Type
  • Table 55: MEA Big Data Market by Solution Type
  • Table 56: MEA Big Data Market by Management Utilities
  • Table 57: MEA Big Data Market by Functional Segments
  • Table 58: MEA Market for Big Data in Emerging Technologies
  • Table 59: MEA Big Data Market by Industry Type
  • Table 60: Global Streaming IoT Data Analytics Revenue by App, Software, and Service
  • Table 61: Global Streaming IoT Data Analytics Revenue in Industry Vertical
  • Table 62: Retail Streaming IoT Data Analytics Revenue by Retail Segment
  • Table 63: Retail Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 64: Telecom & IT Streaming IoT Data Analytics Rev by Segment
  • Table 65: Telecom & IT Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 66: Energy & Utilities Streaming IoT Data Analytics Rev by Segment
  • Table 67: Energy & Utilities Streaming IoT Data Analytics Rev by App, Software, and Services
  • Table 68: Government Streaming IoT Data Analytics Revenue by Segment
  • Table 69: Government Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 70: Healthcare & Life Science Streaming IoT Data Analytics Revenue by Segment
  • Table 71: Healthcare & Life Science Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 72: Manufacturing Streaming IoT Data Analytics Revenue by Segment
  • Table 73: Manufacturing Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 74: Transportation and Logistics Streaming IoT Data Analytics Revenue by Segment
  • Table 75: Transportation and Logistics Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 76: Banking and Finance Streaming IoT Data Analytics Revenue by Segment
  • Table 77: Banking & Finance Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 78: Smart Cities Streaming IoT Data Analytics Revenue by Segment
  • Table 79: Smart Cities Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 80: Automotive Streaming IoT Data Analytics Revenue by Segment
  • Table 81: Automotive Streaming IoT Data Analytics Revenue by Apps, Software, and Services
  • Table 82: Education Streaming IoT Data Analytics Revenue by Segment
  • Table 83: Education Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 84: Outsourcing Service Streaming IoT Data Analytics Revenue by Segment
  • Table 85: Outsourcing Service Streaming IoT Data Analytics Revenue by App, Software, and Services
  • Table 86: Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 87: Streaming IoT Data Analytics Revenue in Region
  • Table 88: APAC Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 89: APAC Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 90: APAC Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 91: Europe Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 92: Europe Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 93: Europe Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 94: North America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 95: North America Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 96: North America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 97: Latin America Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 98: Latin America Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 99: Latin America Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 100: ME&A Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 101: ME&A Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 102: ME&A Streaming IoT Data Analytics Revenue by Leading Vendor Platforms
  • Table 103: Streaming IoT Data Analytics Revenue by APAC Countries
  • Table 104: Japan Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 105: Japan Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 106: China Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 107: China Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 108: India Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 109: India Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 110: Australia Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 111: Australia Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 112: Streaming IoT Data Analytics Revenue by Europe Countries
  • Table 113: Germany Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 114: Germany Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 115: UK Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 116: UK Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 117: France Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 118: France Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 119: Streaming IoT Data Analytics Revenue by North America Countries
  • Table 120: US Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 121: US Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 122: Canada Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 123: Canada Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 124: Streaming IoT Data Analytics Revenue by Latin America Countries
  • Table 125: Brazil Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 126: Brazil Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 127: Mexico Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 128: Mexico Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 129: Streaming IoT Data Analytics Revenue by ME&A Countries
  • Table 130: South Africa Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 131: South Africa Streaming IoT Data Analytics Revenue in Industry Verticals
  • Table 132: UAE Streaming IoT Data Analytics Revenue by Solution and Services
  • Table 133: UAE Streaming IoT Data Analytics Revenue in Industry Verticals