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What is IoT Data Analytics?

What is IoT Analytics?

IoT data analytics, often known as IoT analytics, is the act of evaluating data generated and gathered by IoT devices using a specific set of data analytics tools and techniques.

The actual goal of IoT data analytics is to transform massive amounts of unstructured data from diverse devices and sensors across the heterogeneous Internet of Things ecosystem into meaningful and actionable insights for driving smart business decisions and further data analysis.

Furthermore, IoT analytics allows for the identification of patterns in data sets, including both current and historical data, which may then be used to make predictions and changes concerning future events.

Here is a video on IoT by Intellipaat:

Devices powered by IoT Analytics

There are numerous IoT devices that collect data, including:

Devices powered by IoT AnalyticsWearables

Fitbit and other smartwatches have progressed beyond simply tracking steps. By connecting your devices to the Internet, you can track your friends’ fitness habits, compete with them, message them, and even answer the phone.

Fitness firms track this data, allowing them to design personalized packages if you sign up. This can involve workout habits, food, and objectives, among other things. The most recent smartwatches can even monitor heart rates and rhythms and correctly diagnose cardiac disorders in their users.

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Smart Home

Smart houses include security systems that can be accessed and controlled while you are away from home, as well as appliances that can be turned on and off with digital assistance.

There are numerous devices that may be integrated into your house, as well as numerous data sets that can be collected to examine consumption patterns, system efficacy, and other factors.

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IoT devices are widely used in healthcare. Bluetooth technology is used to produce hearing aids, monitor pulse-based alarm systems, and call for help.

This has significantly improved healthcare. The data gathered is invaluable in terms of developing newer and better technology.

Voice-Activated Everything

IoT gadgets include digital assistants. Alexa, Siri, and Google can take notes, look up information, play music, order cabs, check the weather, set alarms, and do a variety of other things.

The internet is constantly updating these digital helpers to increase their usefulness. Based on your regular interactions with digital assistants, their data enables businesses to customize their services for you.

IoT Analytics use cases

IoT Analytics use cases

Smart agriculture:

Using IoT analytics, connected field machinery operates on the basis of data produced through IoT analysis. Time, geographical location, weather, altitude, and local environmental conditions are all elements in the study. Irrigation systems, for example, can be improved to give the exact amount of water predicted by rainfall.

Regular restocking of supplies:

Real-time inventory monitoring A food vending company with connected machines can have its machines request refilling based on product depletion. When the machine’s stocks reach a certain level, this can be triggered.

Predictive maintenance:

Regular maintenance is required for various infrastructures. Pre-set templates can aid in the development of excellent predictive maintenance models for specific purposes using IoT analysis. For example, in long-distance transport vehicles equipped with heating and cooling systems, IoT analytics can indicate when vehicles need to be overhauled to prevent cargo damage.

Process Efficiency scoring:

Every organization has a variety of processes in place. IoT analytics can assess the efficiency of various processes and recommend modifications. IoT analytics results can detect present and potential bottlenecks and boost efficiencies.

Learn how to secure your IoT devices. Explore our guide on IoT Security and protect your connected world!

How does IoT Analytics work?

There is a constant flow of data in massive amounts from a variety of devices. Without any hardware or infrastructure, IoT analytics can assist examine this data across all linked devices.

As your organization’s demands vary, computational power and data storage scale up or down correspondingly, ensuring that your IoT analysis has the necessary capacity.

Let’s talk about working with IoT analytics in 5 simple steps:

  1. The first step is to gather data from various sources, in various forms, and at various frequencies.
  2. This information is then processed using a variety of external sources.
  3. The data is then saved in a time series for later analysis.
  4. The analysis can be performed in a variety of methods, including specialized analysis tools, regular SQL queries, and machine learning analysis techniques. The outcomes can be utilized to create numerous predictions.
  5. Organizations can use the information they get to create a variety of systems and applications to help with business processes.

Benefits of IoT Analytics

IoT analytics provides numerous advantages, including actionable intelligence and essential insights. These can lead to:

  • Better visibility and control result in faster decision-making.
  • Scalability of business requirements and expansion into other markets.
  • Automation results in lower operational costs and greater resource utilization.
  • New revenue streams as a result of operational difficulties being resolved.
  • Quicker solutions result from accurate problem identification.
  • Issues are resolved faster, and they do not reoccur.
  • Improved customer experience as a result of purchase history analysis.
  • Product development that is both faster and more relevant.
Read on: IoT Projects to enhance your knowledge!


The development of IoT and big data is accelerating, affecting all technological and business sectors and increasing the benefits for both businesses and individuals. The increasing volume of data created by IoT systems has played an important role in the big data environment, promising to improve the capabilities of existing IoT systems.

Come to Intellipaat’s IoT Community if you have more queries on the Internet of Things (IoT)!

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About the Author

Head of Data Engineering & Science

As a head of Data Engineering and Science at Chargebee, Birendra is leading a team of 50+ engineers, specializing in high-scale data and ML platforms. Previously, held key roles at Razorpay and as CTO, with extensive experience in Big Data, ML, and SAAS architecture. Recognized for 85+ contributions to tech publications and patents.