Big Data Markets, 2026 - Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals

Dublin, Feb. 04, 2021 (GLOBE NEWSWIRE) -- The "Big Data Market by Leading Companies, Solutions, Use Cases, Infrastructure, Data Integration, IoT Support, Deployment Model and Services in Industry Verticals 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.

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 2021 to 2026. 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 such as Teradata, IBM, Oracle, SAS, and Datameter. The report evaluates, compares, and contrasts vendors, and provides a vendor ranking matrix. Analysis takes into consideration solutions integrating both structured and unstructured data.

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.

Select Report Findings:

• Big data in SCM will exceed $6.6B globally by 2026

• Data Integration and Quality Tools $9.9B globally by 2026

• Enterprise performance analytics will reach $27.8B globally by 2026

• Big data in business intelligence applications will reach $50.4B by 2026

• 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

Key Topics Covered:

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
2.2 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
3.2 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
4.1.3 MPP Databases
4.1.4 Other Technologies
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 Naive 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 2021 - 2026
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 2021 - 2026
12.1 Big Data Market by Management Utilities 2021 - 2026
12.2 Big Data Market by Functional Segment 2021 - 2026
12.3 Market for Big Data in Emerging Technologies 2021 - 2026
12.4 Big Data Market by Industry Type 2021 - 2026
12.5 Regional Big Data Markets 2021 - 2026

13.0 Appendix: Big Data Support of Streaming IoT Data

For more information about this report visit https://www.researchandmarkets.com/r/fmtcsx

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