The Worldwide Big Data in IoT Industry Will Reach $50.9 Billion by 2026

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Nov 09, 2021, 08:45 ET

DUBLIN, Nov. 9, 2021 /PRNewswire/ -- The "Big Data in IoT by Technology, Infrastructure, Solutions, and Industry Verticals 2021-2026" report has been added to ResearchAndMarkets.com's offering.

This report evaluates the technologies, companies, and solutions for leveraging Big Data tools and advanced analytics for IoT data processing. Emphasis is placed on leveraging IoT data for process improvement, new and improved products, and ultimately enterprise IoT data syndication. The report includes detailed forecasts for 2021 through 2026.

Select Report Findings:

The overall global market for big data in IoT will reach $50.9 billion by 2026
Data analytics is the largest segment by product and service in the big data IoT market
Big data in IoT as a service will reach $7.3 billion by 2026 with North America leading the market
Storage of big data in IoT will reach $16.2 billion by 2026, driven by low-cost cloud-based solutions
Big data in IoT within the government sector will exceed $6 billion by 2026, fueled by military and public safety initiatives
Financial services, government, telecom, retail, healthcare, manufacturing, building automation, consumer electronics, and transport and cargo are some of the major industry verticals for the big data in the IoT market

Data that is uncorrelated and does not have a pre-defined data model and is not organized in a predefined manner requires special handling and analytics techniques. The common industry term, big data, represents unstructured data sets that are large, complex, and prohibitively difficult to process using traditional management tools. As the Internet of Things (IoT) progresses, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions.

The business has a great potential and it can be seen with the trend of big companies such as Cisco, Bosch, IBM, Intel, Google, Amazon, AT&T entering the business of big data analytics in IoT either through acquisition or partnering with companies and startups developing various tools, platforms and APIs in big data.

Big data in IoT is different from conventional IoT and thus will require more robust, agile and scalable platforms, analytical tools and data storage systems than conventional big data infrastructure. The data generation is many times faster. Generation of data is of high volume and it will come in many forms and thus there is a need of developing new platforms and systems. Companies such as treasure Data, have developed unified logging layer Fluentd, JSON coming up as a Java-based lightweight data interchange platform and DDS helping in real-time data processing and lightweight protocols are some of the great developments happening towards developing dedicated big data infrastructure for IoT.

Looking beyond data management processes, IoT data itself will become extremely valuable as an agent of change for product development as well as identification of supply gaps and realization of unmet demands. Big data and analytics used in IoT will become an enabler for the entire IoT ecosystem and business as a whole as enterprises begin to syndicate their own data.

Artificial Intelligence (AI) further enhances the ability of big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective decision-making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

Companies in Report:

1010Data (Advance Communication Corp.)
Social Cops
Software AG/Terracotta
Sojern
Splice Machine
Splunk
Sumo Logic
Sunquest Information Systems
Supermicro
Tableau Software
Tableau
Tata Consultancy Services
Teradata
ThetaRay
Thoughtworks
Think Big Analytics
TIBCO
Tube Mogul (Adobe)
Verint Systems
VolMetrix (Microsoft)
VMware
Wipro
Workday (Platfora)
WuXi NextCode Genomics (Genuity Science)
Zoomdata (Logi Analytics)
Accenture
Actian Corporation
AdvancedMD
Alation
Allscripts Healthcare Solutions
Alpine Data Labs
Alteryx
Amazon
Apache Software Foundation
Apple Inc.
APTEAN (Formerly CDC Software)
Athena Health Inc.
Attunity
BGI
Big Panda
Booz Allen Hamilton
Bosch
Capgemini
Cerner Corporation
Cisco Systems
Cloudera
Cogito Ltd.
Computer Science Corporation
Compuverde
Crux Informatics
Data Inc.
Data Stax
Databricks
DataDirect Network
Dataiku
Datameer
Dell EMC
Deloitte
Domo
eClinicalWorks
Epic Systems Corporation
Facebook
Fluentd
Flytxt
Fujitsu
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
Informatica
Intel
Inter Systems Corporation
IVD Industry Connectivity Consortium
Jasper (Cisco)
Juniper Networks
Leica Biosystems (Danaher)
MapR
Marklogic
Mayo Medical Laboratories
McKesson Corporation
Medical Information Technology Inc.
Medopad
Microsoft
Microstrategy
MongoDB (Formerly 10Gen)
MU Sigma
Netapp
NTT Data
Open Text (Actuate Corporation)
Oracle
Palantir Technologies Inc.
Pathway Genomics Corporation (OME Care)
Pentaho (Hitachi)
Perkin Elmer
Qlik Tech
Quality Systems Inc. (NextGen Healthcare)
Quantum
Quertle
Quest Diagnostics Inc.
Rackspace
Red Hat
Revolution Analytics
Roche Diagnostics
Rocket Fuel Inc. (Sizmek)
Salesforce
SAP
SAS Institute
Sense Networks
Shanghai Data Exchange
Sisense

Key Topics Covered:

1 Executive Summary

2 Big Data in Internet of Things
2.1 Big Data in IoT Framework
2.2 Need for New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
2.2.1 Big Data in IoT will need Unified Logging Layer
2.2.2 Big Data in IoT Data Formats
2.2.3 Big Data in IoT Protocols
2.2.3.1 Message Queuing Telemetry Transport
2.2.3.2 Extensible Messaging and Presence Protocol
2.2.3.3 Advanced Message Queuing Protocol
2.2.4 Big Data in IoT Protocols for Network Interoperability
2.2.4.1 Data Distribution Service
2.2.4.2 Other IoT Protocols
2.2.5 Big Data in IoT Data Processing Scalability
2.3 Big Data in IoT Challenges
2.3.1.1 Scalable High-volume Data Storage
2.3.1.2 Data Management and Processing Raw Data in Multi-vendor Environment
2.3.2 Data Security and Personal Information Privacy Challenges

3 Big Data in IoT Business Trends and Predictions
3.1 Large Companies Partnerships and M&A
3.2 Big Data as a Service for IoT Becomes Mainstream
3.3 M2M Analytics and Cloud Services will be Early Beneficiaries
3.4 Cybersecurity for Big Data Analytics in IoT
3.5 Flexible and Scalable Revenue Models for Big Data Services
3.6 Big Data Operational Savings and New Business Models

4 Big Data in IoT Vendor Ecosystem
4.1 Cloud-based Analytics Platforms for IoT
4.2 Cloud-based Data Storage Service and Management Toolsets
4.3 Big Data Processing for Massive Data Analysis
4.4 Compute, Store, and Analyze Data at the Edge of Networks
4.5 Predictive Platforms and Solutions
4.6 Cloud-based Analytics Systems for IoT
4.7 Database System Upgrades and Evolution
4.8 Analytics Platform Upgrades and Evolution
4.9 Real-Time DDS and Comprehensive Messaging Platforms

5 Big Data in IoT Market Analysis and Forecasts
5.1 Driving Factors for Big Data in IoT
5.1.1 Consumer IoT
5.1.2 Industrial IoT
5.1.3 Enterprise IoT
5.1.4 Government IoT
5.2 Overall Global Market for Big Data in IoT 2021 - 2026
5.3 Global Big Data Solutions in IoT Market 2021 - 2026
5.4 Global Big Data in IoT Hardware, Software, and Services 2021 - 2026
5.5 Global Big Data in IoT Products and Services 2021 - 2026
5.5.1 Market for Big Data Collection in IoT 2021 - 2026
5.5.2 Market for Big Data Storage in IoT 2021 - 2026
5.5.3 Market for Big Data Analytics and Applications in IoT 2021 - 2026
5.5.4 Markets for Big Data as a Service in IoT 2020 to 2028
5.6 Big Data in IoT by Industry 2021 - 2026
5.6.1 Big Data in IoT for Building Automation 2021 - 2026
5.6.2 Big Data in IoT for Consumer Electronics 2021 - 2026
5.6.3 Big Data in IoT for Financial Services 2021 - 2026
5.6.4 Big Data in IoT for Government 2021 - 2026
5.6.5 Big Data in IoT for Healthcare 2021 - 2026
5.6.6 Big Data in IoT for Manufacturing 2021 - 2026
5.6.7 Big Data in IoT for Oil and Gas 2020 to 2028
5.6.8 Big Data in IoT for Retail Industry 2021 - 2026
5.6.9 Big Data in IoT for ICT Industry 2021 - 2026
5.6.10 Big Data in IoT for Transport and Cargo 2021 - 2026
5.6.11 Big Data in IoT for Utilities Industry 2021 - 2026

6 Big Data Case Studies
6.1 Organizations
6.1.1 Climate Corporation
6.1.2 Uber
6.1.3 ScienceSoft
6.1.4 CARTO
6.1.5 Netflix
6.1.6 Amazon
6.1.7 Unique Identity Project
6.2 Solution Approaches
6.2.1 Security Intelligence
6.2.2 Preventive Maintenance
6.2.3 Retail Optimization

7 Select Company Analysis

8 Summary and Conclusions

For more information about this report visit https://www.researchandmarkets.com/r/lo7iwq

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