Smart watch, smart thermostat and smart lighting are the most common example of Internet of Things (IoT) analytics implementation in our daily life. Those devices will gather some data from one or more multiple sensor and from those data, they can provide you with some solution to make your life easier.
Approximately 25.5 billion IoT devices are expected to be deployed globally by 2026, resulting in an increase in IoT data volume. Without data analytics, those data will be squandered, and this is where IoT data analytics come in.
What is IoT Analytics?
Although most people just refer to it as “IoT analytics,” IoT analytics is also frequently referred to as “IoT data analytics.”
So, what is IoT analytics?
As the name implies, IoT analytics is the process which utilizes a particular set of data analytics tools and techniques to analyze data generated and collected from IoT devices. The primary goal of IoT analytics is to evaluate the wide range of data that gets collected from IoT devices and produce useful insights from it. A Additionally, IoT analytics can help with data pattern recognition using both new and old data. Furthermore, those patterns can be used to create a predictive maintenance.
Furthermore, IoT analytics allows businesses to gain a deeper understanding of their operations and make informed decisions. By analyzing the data from IoT devices, bussinesses can identify inefficiencies, optimize processes, and improve the overall efficiency of their operations. Moreover, IoT analytics can help businesses predict and prevent equipment failures by detecting anomalies in the data patterns and alerting maintenance teams in advance.
Types of IoT Analytics
1. Descriptive Analytics
By analyzing historical data, it provides an overview of historical events. Businesses can use it to better understand past events as well as identify trends and patterns that could affect decisions made in the future.
Once the devices’ historical data has been processed and examined, a report detailing the events, times, and frequency of each incident is generated. Analyses of this kind can be used to identify any abnormalities and address inquiries concerning the behavior of individuals or objects.
2. Diagnostic Analytics
As opposed to descriptive analytics, diagnostic IoT analytics is centered on determining the underlying cause of a particular problem by examining additional data to provide an explanation for why something occurred. Data processing and statistical analysis are two methods used in diagnostic analytics to search for hidden patterns and relationships in data that can reveal useful information about the root causes of particular issues.
3. Predictive Analytics
As it name suggest, this IoT analytics is used to predict the future events by analyzing the historical data. This type of analytics uses a variety of statistical as well as machine learning algorithms to build models that can be used to predict future events. This type of analytics is very beneficial when it comes to demand forecasting, inventory management, and other business decisions.
Predictive maintenance takes analytics and alerts to the next level by using them to identify damages or malfunctions in equipment early on. This is one of the most common IoT use cases for manufacturers which produce vital devices used in the aerospace, automotive, healthcare, and other industries. It helps businesses cut down on the time they spend physically inspecting equipment, respond quickly to anomalies, and prevent unexpected downtime.
4. Prescriptive Analytics
Businesses can take advantage of this kind of analytics by using the data gathered from descriptive, diagnostic, and predictive analyses to help them make their decisions. Businesses can use it to optimize their operations and make data-driven decisions.
The most sophisticated kind of IoT analytics is called prescriptive; it not only forecasts future events but also makes suggestions for how to proceed in order fulfill the goals of the business. This kind of analytics determines the best course of action to follow in order to accomplish a particular goal by utilizing optimization algorithms.
To do this, the company’s data—which includes device technical specs, user guides, and more—is merged with IoT analytics. As a result, the system can identify a problem or issue on a remote device and also provides information about known fixes, previous occurrences of the same problem, and recommended next actions. Prescriptive IoT apps can greatly enhance your technical support by combining the advantages of predictive maintenance, which identifies early symptoms, with automated action reports, which guarantee a prompt and accurate response.
The Implenation of IoT Analytics
Like mentioned in the opening, there are a number of use cases or applications of IoT analytics that we frequently encounter in our day-to-day lives, each of which may use a distinct kind of IoT analytics.
Here are the other implementation of IoT analytics that we may find:
1. Smart Home
Nowadays, a large variety of so called smart devices are offered by numerous manufacturers. For example, let’s look at voice-activated devices. They can do everything with Alexa, Siri, and Google Assistant—they can look up information, play music, order taxis, check the weather, set alarms, and more. The data that businesses obtain from your frequent interactions with digital assistants enables them to customize their services for you.
Another example is the use of home security cameras that connected with IoT, which they can send the data to your smartphone, so you can monitor your house condition from wherever you are. This growing trend of IoT home security cameras provides a convenient solution for homeowners concerned about safety. Not only can you remotely access the live feed, but many of these cameras also offer motion detection and real-time alerts. No matter where you are physically, this seamless integration of technology assures you of security and peace of mind.
2. Smart City
One way that IoT analytics are being used in smart cities is through smart CCTV, or smart surveillance. The transportation and traffic bureau can determine the exact time and location of rush hour thanks to the many cameras installed throughout the city.
Smart CCTV can also be used to monitor traffic conditions, such as vehicles that are speeding or running red lights. These cameras are also capable of recognizing license plates, which gives the police the ability to issue a ticket to the plate’s registered owner.
Implementing IoT analytics in smart healthcare involves deploying wearable devices with sensors to continuously monitor patients’ vital signs. Real-time health data is collected and sent to a cloud-based platform for advanced analytics, including machine learning, to find trends and abnormalities. Early disease detection, individualized treatment methods, and immediate alerts for caregivers and healthcare professionals are made possible by this. Advantages include better overall results, patient empowerment, preventive care, remote monitoring, and effective resource allocation. This application serves as an example of how real-time insights for proactive interventions and individualized care provided by IoT analytics improve healthcare.
The two most popular forms of analytics utilized in the manufacturing sector are descriptive and predictive analytics. Predictive maintenance on various tools or machinery used in the daily manufacturing process is one widely used application. By using predictive maintenance, companies can increase equipment efficiency, anticipate maintenance needs before they become expensive, and gain a better understanding of their machinery.
That analytics can be achieved by using sensors and data analysis techniques to monitor the performance of the machinery in real-time. By analyzing patterns and anomalies in the data, manufacturers can identify potential faults or failures before they happen. This not only reduces downtime and maintenance costs but also improves overall productivity and product quality. Moreover, predictive maintenance also enables manufacturers to schedule maintenance activities in advance, ensuring that the machinery is in optimal condition during critical production periods.
In short, IoT analytics is like the detective of the tech world. It analyzes the data from all those smart devices using advanced methods. The main goal? to identify any unusual trends and determine what the data is trying to tell us. The ability to predict and finds problems, streamlines processes, and even anticipates when devices will malfunction, all of which makes businesses run more smoothly. It’s like having a crystal ball for making smart decisions and keeping things ticking seamlessly.
These data can be analyzed in four different ways. First, descriptive analytics, which identifies trends and tells us what happened in the past. Secondly, diagnostic analytics delves into the “why” behind events, identifying the root causes. The third method is predictive analytics, which predicts future events like a crystal ball and is particularly helpful for predictive maintenance. And the fourth one, prescriptive analytics, which not only predicting but also telling us what actions to take for optimized operations.
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