IoT Data Analytics: A process of finding value out of Data

What is IoT Data Analytics?

IoT data analytics is the use of data analysis tools and procedures to derive actionable insights from the huge volumes of data generated by connected Internet of Things (IoT) devices.

Types of Data analytics in iot

Historical data analytics

Historical data is capable of providing insights into the reasons an incident happened and how often. In this way, it helps to achieve greater efficiencies in production processes. Analytics software assists in root cause analysis so that similar incidents/behaviour can be avoided in the future.

Real-time Streaming analytics

Streaming analytics inputs live data that is coming in from different IoT devices, analyzes it the instant it is measured and takes an action based on that analysis.

This action can be as simple as sending a text to an engineer to alert them to an equipment malfunction/failure, or it might be something more complex and continuous such as monitoring the temperature on a machine and causing it to stop before it overheats.

Parks’ waste management is another example. Here, streaming analytics can monitor bin full status, and senses when a bin is full and needs to be emptied. This leads to hassle-free park waste management, saving time as well as eliminating any possibility of the creation of a mess.

In fleet management, streaming analytics can highlight the location of vehicles, identify any off-course vehicles, as well as send reports on the condition of transported goods.

Predictive analytics

Predictive analytics using IoT comprises a variety of statistical techniques such as data mining, predictive modelling, and machine learning that help to analyze current and historical data to do a trend analysis, and predict future or otherwise unknown events.

Let’s study the case of an engineering company that develops and tests systems like combustion engines, batteries and fuel cells. The company wishes to provide its customers with better and deeper insights into the performance of this critical equipment. The company combines real-time streaming with historical IoT data. . Based on the analysis of the comparison between present and historical trends, the company recommends how its customers can improve operational performance and save money.

IoT Data analytics

Why Data analytics of IoT data is needed?

Data Analytics is immensely beneficial for businesses. It adds value to every process and improves outcomes drastically. Full-stack IoT development enables Edge analytics and cloud analytics of the data collected in real-time. 

Here are some of the major benefits of IoT data analytics for businesses:

Health Monitoring

IoT Data Analytics is immensely useful in the healthcare sector. Every health parameter of a patient can be monitored in real-time, from blood pressure to chipped heart monitors. Based on this data, doctors and healthcare professionals can carry out effective diagnostics and chart out the most suitable course of treatment.

In the Covid times, we all were aware of how SPO2 readings need to be measured to monitor the patients in home Quarantine. Other such parameters can also be monitored and analyzed to predict a patient’s health.

Adaption to Automation 

IoT data analytics and metrics can help in achieving automation in the next generation of products. By embedding smart IoT devices into your items, We can analyze their usage patterns and recognize errors in the existing design, giving you a chance to make some modifications.

Customer Experience 

IoT data and analytics boosts business insights and its ability to produce the best experiences for its customer. This real-time analysis is able to reveal the customer needs and other related information from the data collected.

Improved Decision-Making

An immense volume of new data from smart sensors and equipment adds to the huge pool of data. Now, companies utilize that data to inform operational and strategic decision-making using prescriptive analytics. Operational decision-making is where analytics and data are already available to every member of the organization, usually through a self-service tool, while strategic decision-making is where the team leader recognizes crucial questions it needs solutions or answers.

Important use cases of data analytics

Improving Marketing and Sales

IoT Analytics play a key role in boosting the marketing and sales of businesses by helping in the following scenarios:

  • Anticipating Customer Needs – Analytics helps you to collect and analyze customer requirements and trends based on product usage and reviews. 
  • Help Deliver New Value-Added Services–It is through Analytics that you can aggregate data from original sources to perform analysis, prediction and action.
  • Flexible Billing and Pricing – By capturing data from various sources, it is possible to plan outcome and subscription-based pricing models. This also aids in the increase of value-added market penetration.

Real-time Data Analysis for Manufacturing Sector

An entirely automated IoT Analytics in a Control panel aids in using real-time data to watch out for certain patterns and send alerts to the concerned departments. Manufacturers in all the major industries – electronics, automotive, chemical, durable goods, etc. have all heavily invested in IoT Analytics to improve their efficiency and production.

New manufacturing equipment with intelligent sensors is already incorporated in these industries to help with smart manufacturing. This generates huge monetization opportunities aiding in revenue generation initiatives and cost containment.

For example, ThyssenKrupp partnered with two other companies CGI and Microsoft Azure to send alerts when their elevators need repairs. Predictive maintenance sends alerts when an elevator is about to go out of function and even teaches the technicians the areas of error.

Drastic improvements in the healthcare sector 

IoT has brought about remarkable changes and improvements in the healthcare sector. People and apps are connected in a way that was never deemed possible before. This has improved healthcare outcomes as well as reduced healthcare expenditure. IoT-enabled medical devices are equipped with high-end sensors to help doctors understand medical emergencies even before they come up.

The IoT sensors can be embedded in various equipment such as surgical robots, personal health and fitness equipment, drug dispensing systems and implantable devices. The data is collected and analyzed in real-time. Additionally, the equipment themselves is monitored for minimizing downtime and avoiding potential failures etc.

The rise of health apps and connected medical devices has been a game-changer in the medical industry because they provide patient centralized analytics. The parameters are set (in the apps or devices) to automatically trigger alerts and initiate a response from concerned healthcare givers when a problem is detected.

Leveraging Video Analytics for better Surveillance

IoT analytics is capable of detecting anomalies and lapses and protects us from critical situations. Surveillance using video analytics can be used for preventing crimes and accidents. Example- Roadside speed monitors.

Now organizations and systems do not need to rely on closed-circuit cameras (CCTV) for protecting their premises from unwanted intruders. They can use video analytic techniques fitted with smart sensors, instead. When the video is combined with data, it is possible to derive far greater insights for predicting future events.

Video analytics help in grasping shopper habits. When sensors on devices are placed in retail centers, they can help avert road accidents and traffic jams by pinpointing time and location of maximum traffic and alerting the users/motorists. Video surveillance can also be placed in worksites to ensure worker safety or for improving security

IoT data Analytics


trends in IoT data analytics

IoT Edge analytics

IoT edge analytics is a fast-emerging trend in the IoT domain. IoT Edge analytics are tools located close to IoT devices. They collect, process and analyze data at the source itself rather than transmitting that data back to the cloud for analysis. This improves the data analysis process by performing it in real-time thereby ensuring that as much useful information is collected from the device as possible. Devices are designed to have their own analytical capabilities to this end. Edge systems help to reduce lag in the performance of IoT monitoring and analytics systems.

Artificial Intelligence (AI) and Machine Learning (ML) in IoT Data analytics

Ordinary data analysis helps in IoT deployment, whereas AI can do it faster and much more accurately. In specific terms, AI can structure a data set, improve IoT device interoperability, and draw conclusions in real-time. With the help of AI and ML, systems can derive the most useful insights from huge volumes of unstructured data. Thus, AI algorithms can save valuable time by aggregating unstructured data from multiple sources, processing it, and representing it in a usable format. 

Hence, bringing AI to the IoT architecture Is becoming a necessity. IoT systems, including endpoint devices, must become smarter and more autonomous in order to deal with the ever-increasing volume and scale of data. Thankfully, we have AI and ML for that.


At PsiBorg, we understand the capabilities of IoT data analysis and strive to leverage the same to provide you with the most cutting-edge analytics solutions for your IoT systems’ needs. We have developed IoT dashboards for data analytics for various sectors and systems, helping businesses to grow.

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