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大数据市场：主要参与者、解决方案、用例、基础设施、数据集成、支持物联网、部署模型、行业服务 (2023-2028)
市场调查报告书
商品编码
1194927

大数据市场：主要参与者、解决方案、用例、基础设施、数据集成、支持物联网、部署模型、行业服务 (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个工作天内

价格

在这份报告中，我们分析了全球大数据市场，包括业务案例问题和分析、应用用例、供应商格局、价值链分析、行业趋势量化评估（2023-2028）、大数据市场以及研究数据基础设施和安全框架组件，我们编译和提供信息，例如领先供应商的概况、比较分析和领先解决方案的概述。

主要发现

  • 到 2028 年，商业智能 (BI) 大数据的市场规模预计将达到 635 亿美元。
  • 到 2028 年，全球数据集成和质量工具市场预计将达到 12 亿美元。
  • 到 2028 年，全球企业绩效分析市场预计将达到 399 亿美元。
  • 到 2028 年，全球供应链管理大数据市场预计将达到 83 亿美元。
  • 人工智能和物联网 (AIoT) 的结合将依赖于先进的大数据分析软件。
  • 实时数据是每个用例、领域和解决方案的关键价值主张。
  • 市场领先的公司正在迅速将大数据技术集成到他们的 IoT 基础架构中。

内容

第 1 章执行摘要

第二章介绍

  • 大数据概述
    • 大数据的定义
    • 大数据生态系统
    • 大数据的主要特征
  • 分析背景

第 3 章大数据挑战与机遇

  • 保护大数据基础架构
    • 大数据基础架构
    • 基础设施问题
    • 大数据基础架构机会
  • 非结构化数据和 IoT（物联网）
    • 新协议、平台、流媒体/分析、软件/分析工具
    • 物联网大数据和轻量级数据交换格式
    • 物联网大数据和轻量级协议
    • IoT 大数据和网络互操作性协议
    • 物联网数据处理中的大数据可扩展性

第四章大数据技术与商业案例

  • 大数据技术
    • Hadoop
    • NoSQL
    • MPP数据库
    • 其他技术
  • 新技术、工具和技巧
    • 流媒体分析
    • 云技术
    • 搜索技术
    • 自定义分析工具
    • 关键字优化
  • 大数据路线图
  • 市场驱动因素
    • 数据的数量和类型
    • 企业和运营商更多地采用大数据
    • 大数据软件的成熟度
    • 大型网络公司对大数据的持续投资
    • 业务驱动力
  • 市场壁垒
    • 最大的障碍：隐私与安全之间的差距
    • 员工再培训和组织阻力
    • 缺乏明确的大数据战略
    • 可扩展性和维护方面的技术问题
    • 拥有大数据开发方面的专业知识

第 5 章关键大数据行业

  • 工业自动化和物联网
    • M2M（机器对机器）解决方案的大数据
    • 每个行业的市场机会
  • 零售/酒店业
    • 预测和库存管理
    • 客户关係管理 (CRM)
    • 购买模式决策
    • 酒店用例
    • 个性化营销
  • 数字媒体
    • 社交媒体
    • 社交游戏分析
    • 其他行业对社交媒体分析的使用
    • 互联网关键字搜索
  • 实用工具
    • 运营数据分析
    • 未来的应用领域
  • 金融业务
    • 欺诈分析和缓解、风险分析
    • 商家资助的奖励计划
    • 客户细分
    • 保留客户并提供个性化产品
    • 保险公司
  • 医疗
    • 药物开发
    • 医疗数据分析
    • 案例研究：心跳模式识别
  • 信息和通信技术 (ICT)
    • 运营商分析：客户/使用情况分析和服务优化
    • 大数据分析工具
    • 语音分析
    • 新产品和服务
  • 政府：行政管理和国土安全
    • 大数据研究
    • 统计分析
    • 语言翻译
    • 为大众开发新的应用程序
    • 犯罪追踪
    • 信息收集
    • 欺诈检测和创收
  • 其他部门
    • 航空
    • 运输和物流：优化车队利用率
    • 体育统计数据的实时处理
    • 教育
    • 製造业
    • 采掘/自然资源

第六章大数据价值链

  • 大数据价值链的碎片化
  • 数据采集和配置
  • 数据仓库和商业智能 (BI)
  • 分析和可视化
  • 行动和业务流程管理
  • 数据治理

第 7 章大数据分析

  • 大数据分析的作用和重要性
  • 大数据分析流程
  • 反应式和主动式分析
  • 技术和实施方法
    • 网格计算
    • 数据库内处理
    • 内存分析
    • 数据挖掘
    • 预测分析
    • 自然语言处理 (NLP)
    • 文本分析
    • 视觉分析
    • 学习关联规则
    • 分类树分析
    • 机器学习
    • 神经网络
    • 多层感知器
    • 径向基函数
    • 地理空间预测建模
    • 回归分析
    • 社交网络分析

第 8 章标准化和监管问题

  • CSCC（云标准客户委员会）
  • 美国国家标准与技术研究院 (NIST)
  • 绿洲
  • ODF（开放数据基金会）
  • ODCA（开放数据中心联盟）
  • CSA（云安全联盟）
  • 国际电信联盟 (ITU)
  • 国际标准化组织 (ISO)

第9章大数据在各行业的应用领域

  • 大数据在製造业中的应用
  • 零售用途
  • 使用大数据：检测保险欺诈
  • 大数据的使用：媒体和娱乐行业
  • 大数据的使用：天气模式
  • 大数据的使用：运输行业
  • 大数据的使用：教育行业
  • 大数据的使用：电子商务的个性化
  • 大数据的使用：石油和天然气行业
  • 大数据的利用：电信行业

第10章大数据主要公司及解决方案

  • 供应商评估矩阵
  • 主要大数据供应商的竞争格局
    • 新产品开发
    • 商业联盟/合作、企业合併/收购 (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章大数据市场总体分析及预测（2023-2028）

  • 全球大数据市场
  • 大数据市场：按解决方案类型分类
  • 大数据市场：按地区

第 12 章大数据市场分析和预测：按细分市场 (2023-2028)

  • 大数据市场：按管理职能分类 (2023-2028)
    • 通用分析服务器及相关硬件市场 (2023-2028)
    • 大数据应用基础架构和中间件市场（2023 年至 2028 年）
    • 数据集成工具和数据质量工具市场（2023 年至 2028 年）
    • 数据库管理系统的大数据市场（2023 年至 2028 年）
    • 存储管理大数据市场（2023-2028 年）
  • 大数据市场：按功能细分 (2023-2028)
    • 供应链管理大数据（2023-2028 年）
    • 用于劳动力分析的大数据 (2023-2028)
    • 用于企业绩效分析的大数据（2023-2028 年）
    • 专业服务大数据 (2023-2028)
    • 用于商业智能 (BI) 的大数据 (2023-2028)
    • 用于社交媒体和内容分析的大数据 (2023-2028)
  • 新兴技术中的大数据市场（2023 年至 2028 年）
    • 物联网大数据 (2023-2028)
    • 智慧城市的大数据 (2023-2028)
    • 区块炼和加密货币的大数据 (2023-2028)
    • 用于增强现实/虚拟现实 (AR/VR) 的大数据 (2023-2028)
    • 网络安全大数据 (2023-2028)
    • 智能助手的大数据 (2023-2028)
    • 用于认知计算的大数据（2023 年至 2028 年）
    • 客户关係管理 (CRM) 大数据（2023-2028 年）
    • 空间信息大数据 (2023-2028)
  • 大数据市场：按行业分类 (2023-2028)
  • 大数据市场：按地区划分 (2023-2028)
    • 北美大数据市场（2023 年至 2028 年）
    • 南美洲的大数据市场（2023 年至 2028 年）
    • 西欧大数据市场 (2023-2028)
    • 2023-2028 年中东欧大数据市场
    • 亚太大数据市场（2023-2028 年）
    • 中东和非洲大数据市场（2023 年至 2028 年）

第 13 章附录：用于流式传输 IoT 数据的大数据

  • 流式 IoT 数据的大数据技术市场前景
  • 全球流媒体物联网数据分析收入（2023 年至 2028 年）
  • 按地区划分的流式 IoT 数据分析收入（2023 年至 2028 年）
  • 按国家/地区分列的流式 IoT 数据分析收入（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