What is IoT Analytics? Importance and use cases

Data has always been a gold mine of information. With IoT (Internet of Things) – essentially connected objects – sensors, virtual assistants, smart watches, cars, household appliances, etc., the amount of data generated is colossal. According to IDC, 41.6 billion devices will be in use by 2025, generating nearly 80 zettabytes of data each year. This is a great opportunity for businesses and government agencies to uncover the truth about data generated by connected devices and make decisions.

With data analytics – processing and analyzing data – it is quite convenient for businesses to discover insights. Today, connected devices are everywhere. From your wrists to your heart, they generate data every millisecond. This article will explore the intersection of IoT and data analytics (IoT analytics) and how it is transforming the industry with its various use cases.

Understanding IoT Analytics – Convergence of IoT and Data Analytics

IoT analytics is the process of collecting, storing, processing, analyzing and visualizing data generated by IoT devices to make a decision. Implementing the entire workflow requires hardware, software, and several other resources.

A typical IoT data analysis process includes:

1. Data collection

IoT devices generate huge amounts of data. For example, a car sensor can collect your speed, the day and time you drive, etc. Likewise, a thermostat can collect your bedroom temperature, what time you go to bed, etc. All this device data is collected and moved to a cloud or physical server.

2. Storage

The data is stored for further processing on a server. Data can be stored on a cloud or a physical server depending on the company’s choice.

3. Treatment

Not all data collected from IoT devices is necessarily useful. Some data is waste, so it is deleted. What remains is processed and stored for analysis.

4. Analysis

The necessary data is analyzed using statistical models to discover patterns and insights.

5. Visualization

The discovered pattern is visualized as a graph, table, etc., so that the pattern is visible.

Importance of IoT Analytics

IoT analytics adds a lot of value to the industry.

1. Real-time decision making

IoT analytics allows businesses to understand what is happening in real time while devices are operating. In sectors like manufacturing, where critical automation is occurring, this can help prevent industrial accidents. With real-time actionable information, businesses can respond quickly to the situation.

2. Pull insights

IoT devices generate a lot of data. IoT data analytics helps businesses discover patterns and trends in data and use the insights to optimize devices for better performance.

3. Predictive maintenance

IoT analytics can prevent a malfunction or breakdown of a critical device. IoT analytics can inform when a device is about to malfunction. So people can take proactive steps to avoid this by repairing the equipment or arranging a replacement. This rapid intervention avoids downtime in the manufacturing industry, where downtime is a key performance indicator.

4. Anomaly detection

Data analytics combined with AI and machine learning can help detect anomalies in devices and systems. This can help prevent security breaches, equipment malfunctions and inefficiencies.

5. Product development

IoT analytics can help understand how customers use and interact with their connected devices. This information can help improve the product and service, further improving customer satisfaction.

6. Regulatory compliance

Many industries must comply with regulations regarding data storage and processing. IoT analytics can help businesses comply with these regulations.

IoT Analytics Use Cases

IoT data analytics enables the industry to achieve better results in several ways. Let’s look at a few ways.

1. Crop monitoring

Sensor data is used to monitor soil moisture, temperature and weather conditions to optimize irrigation and improve crop yield. Companies like Weather Risk Management Services (WRMS) allow farmers to protect their crops using this technology. Farmers have applications like SECUFARM to manage their farms.

2. Patient health monitoring

Data from wearable devices, such as smart watches, is analyzed to take proactive health measures. This approach often reduces patient visits to the hospital. Dexcom uses this technology to enable continuous monitoring of blood sugar levels in people with diabetes. Ultra human empowers people to take charge of their diet and fitness through their wearable biosensors.

3. Traffic management

Traffic management is a great use case for IoT analytics. Data from traffic lights, connected cameras and cars is analyzed to improve traffic management, reduce traffic jams and provide better mobility to the public. No Traffic allows cities to manage their traffic using a plug-and-play sensor and control unit.

4. In-store customer experience

Data from in-store cameras, sensors and beacons is analyzed to understand customer behavior and preferences to improve their in-store shopping experience. Get Go, a convenience store chain in the United States, replaced conventional cooler doors with IoT-enabled display screens that show prices, promotions and nutritional value of food products, improving the customer experience. customers in the store.

5. Smart Grid Optimization

Data from smart grids and energy meters is collected to optimize energy distribution, prevent outages and improve energy efficiency. Duke Energy uses IoT devices coupled with data analytics to optimize the performance of their networks through predictive maintenance.

6. Fleet management

Data from GPS installed on vehicles and other on-board sensors is analyzed to optimize routes, monitor fuel consumption and improve overall foot efficiency. Controlant, a pharmaceutical supply chain company, uses IoT SIMS to optimize supply routes and ensure secure delivery of goods.

7. Air quality management

The sensors collect data related to particulate matter (PM), NO2 and other pollutants in the air and transmit it for analysis. The devices can be located in residential areas, traffic sites, industrial sites, etc., to help people clearly understand the air quality. BreezoMeter and Airly use IoT to detect the level of air pollution in the United States and allow users to plan their outdoor activities.