What is IoT Analytics? Types, Benefits, and Use Cases

A data analysis tool called Internet of Things (IoT) analytics evaluates the vast amount of data that IoT devices collect. IoT analytics analyze enormous amounts of data and draw forth relevant information.

 

Often, industrial IoT and IoT analytics are addressed together (IIoT). On manufacturing infrastructure, weather stations, smart meters, delivery vans, and other kinds of gear, data is gathered from a variety of sensors. Data center and application management for the retail and healthcare industries may be done using IoT analytics.

 

IoT data and big data are comparable in several ways. The primary distinction between the two relates not only to the volume of data but also to the variety of sources it was gathered from. All of this information must be transformed into a single, understandable data stream. Data integration becomes quite challenging when there are so many different types of information sources. Here is where IoT analytics may help, even if it might be challenging to build and deploy.

 

IoT devices are becoming more and more popular, and data is plentiful. Data from mobile devices, smart home devices, cars, and other sources is constantly streaming into organizations. Building a solid IoT analysis-based system is essential for businesses hoping to profit from this. The actual potential of IoT data may be unlocked via IoT analytics, creating several advantages that a business can make use of to outperform the competition.

 

What is IoT analytics?

 

IoT analytics is the use of tools and processes for data analysis to extract value from the massive amounts of data produced by connected Internet of Things devices.

 

The Industrial IoT is frequently brought up when talking about the possibilities of IoT analytics. Organizations may gather and analyze data from sensors on delivery trucks, weather stations, smart meters, industrial equipment, pipelines, and other forms of infrastructure thanks to the IIoT. The management of data centers and other infrastructure, as well as retail and healthcare applications, may all benefit from IoT analytics in a similar way.

 

IoT data is a subset and special instance of big data and as such, comprises heterogeneous streams that need to be merged and converted to produce consistent, complete, up-to-date, and accurate data for business reporting and analysis. IoT data integration is difficult. There are several different device categories, and the majority of them are not made to work with other systems. The two major obstacles to the growth of the Internet of Things are data integration and the analytics that rely on it.

 

The 3Vs model—volume, variety, and velocity—is occasionally used to describe big data. Volume, variety, and velocity all describe how much data there is, how many various sorts of devices there are, and how quickly it is processed. Instead of simply the volume, all three of these qualities are simultaneously expanding, which presents issues for big data analytics and IoT analytics. IoT analytics, sometimes referred to as IoT data analytics, is the process of assessing data created and acquired by IoT devices using a particular set of data analytics tools and methodologies.

 

IoT data analytics' main objective is to turn vast volumes of unstructured data from various devices and sensors throughout the heterogeneous Internet of Things ecosystem into insightful and useful knowledge that can be used to make wise business decisions and conduct more data analysis. IoT analytics also makes it possible to find patterns in data sets, including both historical and present data, which may subsequently be utilized to forecast and alter future occurrences.

 

How does IoT analytics differ from traditional analytics?

 

Structured data is the focus of traditional analytics. IoT devices create unstructured data. Data is not standardized and comes in many forms. Because physical processes and unpredictability have an impact on sensors, data may comprise missing points, distorted signals, and false readings. This is not like data that was manually entered or scanned in from forms.

 

Conventional data systems do not undergo frequent modification. For instance, many fields on a form have a predetermined range. Systems using IoT are more dynamic. IP addresses for devices can alter. There may be device upgrades. It's possible to add new, more capable gadgets. Environmental factors may have an impact on sensor precision or accuracy.

 

Real-time data from IoT devices is common. Real-time analysis and insights are also necessary for many IoT applications. If IoT data is not evaluated right away, it is considered to quickly become stale and worthless. Non-real-time conventional analysis is deciding whether to grant someone a loan based on an examination of prior behavior. Yet real-time IoT analytics are needed to know where parking spaces are available.

 

Types of IoT analytics

 

• Descriptive Analytics: The most fundamental type of analytics that enables users to summarise and characterize incoming IoT data is descriptive analytics. Calculating the mean and the standard deviation is all that is necessary for descriptive analysis. It may be applied to swiftly comprehend the information being gathered.

 

• Diagnostic Analytics: The basic insight given by descriptive analytics is expanded upon by diagnostic analytics. It aims to explain why something happened in the past. It's root cause analysis, but one that is supported by data and algorithms. Some helpful procedures include data discovery, data mining, data drill through, and drill down.

 

• Predictive analytics: It uses previous data to forecast upcoming actions or events. If a sensor starts displaying data outside of its typical range, there may be a problem. It is possible to arrange predictive maintenance before problems worsen. Another illustration is the proactive analysis of customer inventories by a supplier, which enables early or just-in-time delivery.

 

• Prescriptive Analytics: To attain a certain objective, prescriptive analytics is utilized to optimize future decisions. It could advise the ideal operating circumstances to optimize output or uptime, for instance.

 

Algorithms or techniques used in IoT Analytics

 

IoT employs several ML/AI algorithms that are chosen to suit the requirements of each application. There are several of them, including feature selection, estimating, denoising, clustering, and estimation. A crucial method is time-series analysis. For example, data mining, search, or time-series statistical analysis. According to one method, incremental algorithms are used to chunk data. It reduces the need for plenty of memory.

 

By spreading out the processing and storage, edge computing reduces the need for enormous capacity. Data that requires sophisticated analytics or long-term storage is the only data that is stored on the cloud. Certain applications solely save aggregates (sum, mean, min, max).

 

The analytics platform analyzes data as it enters to provide real-time insights. Streaming analytics refers to this. In a data lake, raw data could be kept. Data warehouses or databases are frequently used to store processed data.

 

IoT Analytics Use Cases

 

1. Wearables:

 

Some smartwatches, like Fitbit, go beyond only counting steps. You may monitor your friends' exercise routines, engage in competition with them, send them messages, and even take their calls by connecting your gadgets to the Internet.

 

Fitness companies collect this information, which enables them to create customized packages if you sign up. This can include, among other things, eating patterns, exercise routines, and goals. Even heart rates and rhythms may be monitored, and the most latest smartwatches can accurately identify cardiac abnormalities in their wearers.

 

2. Smart Home:

 

Smart homes have security systems that can be accessed and managed while you are away from the house as well as electronic appliances that can be turned on and off. Many devices can be incorporated into your home, and many data sets can be gathered to look at consumption trends, system effectiveness, and other things.

 

3. Healthcare:

 

The usage of IoT devices in healthcare is widespread. Producing hearing aids, monitoring pulse-based alarm systems, and making emergency calls all employ Bluetooth technology. As a result, healthcare has considerably improved. The information gained is crucial for creating new and improved technologies.

 

4. Voice-Activated Systems:

 

Digital assistants are one type of IoT device. Alexa, Siri, and Google can play music, order taxis, take notes, search for information, check the weather, set alarms, and perform a wide range of other tasks. To make these digital assistants more useful, the internet is continually upgrading them. Such data enables businesses to tailor their services for you based on your frequent encounters with digital assistants.

 

5. Smart Agriculture:

 

IoT analytics is used to run linked field equipment based on data generated by IoT analysis. The research takes into account time, place, weather, height, and local environmental variables. For instance, irrigation systems can be enhanced to deliver the precise volume of water that is anticipated by rainfall.

 

6. Regularly Replenishing Stocks:

 

Monitoring of inventories in real-time When a company's food vending machines are networked, they may make replenishment requests depending on product depletion. This may be activated after the machine's stockpiles reach a specific level.

 

7. Predictive Maintenance:

 

Many infrastructures need regular upkeep. Using IoT analysis, pre-set templates can help in the creation of superior predictive maintenance models for certain applications. IoT analytics, for instance, may show when long-distance transport trucks with heating and cooling systems need to be updated to prevent cargo damage.

 

8. Scoring For Process Efficiency:

 

There are several different procedures in use at every firm. IoT analytics can evaluate the effectiveness of various processes and suggest changes. Results from IoT analytics help identify current and prospective bottlenecks and increase productivity.

 

Benefits of IoT Analytics

 

Businesses may uncover faults and difficulties that result in inefficiencies and then take action to fix them by analyzing data produced by IoT devices. For example, a firm that produces food and beverages may utilize IoT data analytics to monitor the temperature of its refrigerators in real-time and stop food from spoiling as a result of power outages or broken equipment.

 

IoT data analytics may assist companies in making financial savings in a variety of ways, including by lowering energy usage, decreasing downtime, and increasing asset utilization. For instance, a manufacturer may utilize IoT data analytics to track the efficiency of its production line and make necessary modifications to prevent material waste.

 

To gather and analyze client data to comprehend their wants and preferences, IoT data analytics may be employed. As a result, firms may be able to develop better goods and services that satisfy their clients' demands. One retailer's usage of IoT data analytics is to track client movements throughout the shop and then provide them with tailored suggestions based on their preferences.

 

Businesses can spot possible safety risks and take preventative action to avert them by evaluating data from a variety of sensors. For instance, a construction business may utilize IoT data analytics to keep an eye on the health of its machines and equipment to prevent mishaps. The capacity to evaluate real-time data points is one of the key benefits of IoT data analytics. That is feasible because streaming analytics, a kind of analytics that can process data as it is being created, is being used.

 

Businesses may simply and quickly scale up their operations using IoT data analytics without having to pay any additional expenses. This is because IoT data analytics may be implemented on the cloud, allowing companies to only pay for the resources they utilize.

 

IoT data analytics also has the benefit of assisting firms in making their data analysis very accurate. This is because modern analytical approaches may be utilized to collect data from a wide range of sources and then evaluate it using IoT data analytics. Analytics from IoT data may also assist firms in enhancing the security of their data. This is because IoT data analytics may be utilized to locate and monitor possible hazards before taking preventative action.

 

Conclusion

 

Since its start, IoT data analytics has gone a long way and has firmly established itself in numerous industries and enterprises. It's time to adopt IoT data analytics if you want to maximize the value of your data assets and improve the effectiveness of your business choices. As long as you know how to use IoT data analytics properly, it may benefit organizations in a variety of ways.