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IoT Analytics Tools

IoT Analytics – Use case

Conclusion

There are several IoT analytics tools available that cater to different aspects of data processing, analysis, and visualization in the context of the Internet of Things( IoT). Here are some notable ones:

  1. Microsoft Azure IoT Analytics: Description Part of the Azure IoT Suite, it offers capabilities for processing and analyzing large quantities of IoT data. It includes tools for data storage, transformation, and querying.
  2. AWS IoT Analytics: Description A service provided by Amazon Web Services( AWS), it allows users to clean, process, store, and analyze IoT data. It integrates with other AWS services for comprehensive IoT solutions.
  3. IBM Watson IoT Platform: Description Offers analytics and AI capabilities for IoT data, allowing organizations to conclude actionable insights. It includes features for real- time data analysis and predictive maintenance.
  4. Google Cloud IoT Core and Cloud IoT Analytics: Description Google Cloud offers IoT Core for device management and Cloud IoT Analytics for processing and analyzing IoT data. It integrates with other Google Cloud services for comprehensive data solutions.
  5. Thing Speak: Description An IoT analytics platform by Math Works, it allows users to collect, analyze, and visualize IoT data in real- time. It’s well- suited for applications involving sensor data and monitoring. DescriptionC3.ai provides an IoT analytics platform that enables organizations to build and deploy AI- driven applications for various use cases, including predictive maintenance and energy management.
  6. Predix( by GE Digital): Description Predix is a platform specifically designed for industrial IoT applications. It provides tools for data analytics, machine learning, and application development in the artificial sector.
  7. Ubidots: Description Ubidots is a cloud- based IoT platform that offers analytics and visualization tools. It’s designed to simplify the process of building IoT applications and dashboards.
  8. Particle: Description Particle provides an IoT platform that includes tools for device management, connectivity, and data visualization. It’s suitable for IoT systems ranging from prototypes to product.
  9. Kaa IoT Platform: Description Kaa is an open- source IoT platform that offers features for data analytics, device management, and application development. It provides flexibility for customization based on specific IoT project requirements.

What is IOT analytics?

In our contemporary online era, data and the Internet of Things( IoT) are inherently interconnected, playing vital places in shaping our digital existence.

In this article, we will explore What is IoT analytics and how it works. and why it is important to explore IoT analytics.

Table of Content

  • What is IoT Analytics?
  • The Significance of Data Analytics in IoT
  • How Does Employing a Data Analyst in IoT Benefit Businesses?
  • The 7 Roles of Data Analysts in IoT
  • Why is IoT data analytics important?
  • What can IoT analytics do?
  • IoT Analytics – Use case
  • IoT Analytics Tools
  • Conclusion

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What is IoT Analytics?

As the internet’s usage continues to grow among individuals and businesses, there’s an exponential increase in both data consumption and production. As of the close of 2020, it was anticipated that nearly 30.73 billion IoT devices would be in operation. The Internet of Thing represents a digital ecosystem where various elements — human resources, technologies, networks, and tools interact and connect to achieve shared objectives....

The Significance of Data Analytics in IoT

Data analytics is a process of analyzing unstructured data to give meaningful conclusions. Numerous of the methods and processes of data analytics are automated and algorithms designed to process raw data for humans to understand. IoT devices give large volumes of precious data that are used for multiple applications. The main goal is to use this data in a comprehensive and precise way so that it’s organized, and structured into a further usable format. A Data analytics uses methods to process large data sets of varying sizes and characteristics, it provides meaningful patterns, and extracts useful outputs from raw data. Manually analyzing these large data sets is veritably time consuming, resource intensive, and expensive. Data analytics is used for saving time, energy, resources and gives precious information in the form of statistics, patterns ,and trends. Organizations use this information to improve their decision making processes, apply further effective strategies, and achieve desired outcomes....

How Does Employing a Data Analyst in IoT Benefit Businesses?

Data analysis gives substantial value to their associations by interpreting, analyzing, and presenting findings grounded on specific data sets. In the environment of IoT, data analysis offers precious data entry services that enable businesses to harness the potential of IoT alongside effective data analytics methods, driving growth and competitive advantage....

The 7 Roles of Data Analysts in IoT

The roles of data analysts within organizations are contingent on their knowledge, skills, and expertise. Here are seven prominent roles that data analysts fulfil:...

Why is IoT data analytics important?

IoT( Internet of Things) data analytics is pivotal for several reasons:...

What can IoT analytics do?

IoT analytics can do the following:...

IoT Analytics – Use case

Here are several IoT analytics use cases across various industries...

IoT Analytics Tools

There are several IoT analytics tools available that cater to different aspects of data processing, analysis, and visualization in the context of the Internet of Things( IoT). Here are some notable ones:...

Conclusion

In our digital world, where data plays a very important role in every aspect of our day to day lives, the capability to harness data’s potential and leverage it for organizational benefit which offers huge advantages. These advantages empower ultramodern businesses to enhance their operations and secure sustainable growth....

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IoT Analytics – Use case

Conclusion