Big Data in IoT by Technology, Infrastructure, Solutions, and Industry Verticals 2022-2027

DUBLIN–(BUSINESS WIRE)–The “Big Data in IoT by Technology, Infrastructure, Solutions, and Industry Verticals 2022 – 2027” report has been added to’s offering.

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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 2022 through 2027.

Select Report Findings:

  • The overall global market for big data in IoT will reach $63.8 billion by 2027
  • 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 $8.1 billion by 2027 with North America leading the market
  • Storage of big data in IoT will reach $19.2 billion by 2027, driven by low-cost cloud-based solutions
  • Big data in IoT within the government sector will exceed $7.23 billion by 2027, 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) continues to evolve, 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. Generation of data is often at very high volumes for many applications and it manifests in many forms. This facilitates the need to develop new platforms and systems.

Companies such as Treasure Data (Softbank), 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.

Key Topics Covered:

1 Executive Summary

2 Big Data in Internet of Things

3 Big Data in IoT Business Trends and Predictions

4 Big Data in IoT Vendor Ecosystem

5 Big Data in IoT Market Analysis and Forecasts

6 Big Data Case Studies

7 Select Company Analysis

8 Summary and Conclusions

Companies Mentioned

  • 1010Data (Advance Communication Corp.)
  • Accenture
  • Actian Corporation
  • AdvancedMD
  • Alation
  • Allscripts Healthcare Solutions
  • Alpine Data Labs
  • Alteryx
  • Amazon
  • Apache Software Foundation
  • Apple Inc.
  • APTEAN (Formerly CDC Software)
  • AthenaHealth 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
  • Gridgain Systems
  • Groundhog Technologies
  • Guavus
  • Hack/reduce
  • Hitachi Data Systems
  • Hortonworks
  • HP Enterprise
  • HPCC Systems
  • IBM
  • Illumina Inc
  • Imply Corporation
  • Red Hat
  • Revolution Analytics
  • Roche Diagnostics
  • VolMetrix (Microsoft)
  • Wipro
  • Workday (Platfora)
  • WuXi NextCode Genomics (Genuity Science)
  • Zoomdata (Logi Analytics)

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