Data has always been a gold mine of insights. With IoT (Internet of Things) – essentially connected devices – sensors, virtual assistants, smartwatches, cars, home appliances, etc., the amount of data generated is humongous. According to IDC, there will be 41.6 billion devices in use by 2025, generating nearly 80 zettabytes of data every year. This is an excellent opportunity for businesses and government agencies to uncover the truth from the data generated by connected devices and make decisions.
With data analytics – processing and analyzing data – it is fairly convenient for businesses to unearth insights. Today, connected devices are everywhere. From your wrists to your hearts, 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 the data generated by IoT devices to make a decision. Implementing the entire workflow requires hardware, software, and several other resources.
A typical IoT data analytics process comprises of:
1. Data collection
IoT devices generate massive amounts of data. For example, a car sensor can collect your speed, the day and time you drive, etc. Similarly, a thermostat can collect the temperature of your room, the time you go to bed, etc. All this data from devices is collected and moved to a cloud or a physical server.
2. Storage
The data is stored for further processing on a server. The data can be stored on a cloud or physical server depending on the business choice.
3. Processing
All the data collected from IoT devices isn’t necessarily useful. Some data is garbage, so it is discarded. What’s left is processed and kept 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, chart, etc., so the pattern is visible.
Significance of IoT Analytics
IoT analytics adds a lot of value to the industry.
1. Real-Time Decision Making
IoT analytics enables businesses to understand what happens in real time while devices run. In industries like manufacturing, where critical automation is underway, this can help prevent industrial accidents. With real-time decision insight, businesses can quickly respond to the situation.
2. Draw Insights
IoT devices generate a lot of data. IoT data analytics helps businesses discover patterns and trends in the data and use the insights to optimize the devices for better performance.
3. Predictive Maintenance
IoT analytics can prevent a critical device from malfunctioning or breaking down. IoT analytics can inform when a device is about to malfunction. So, people can take proactive measures to prevent it by repairing the equipment or arranging a spare one. This timely intervention prevents 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 their connected devices and interact with them. This information can help improve the product and service, further improving customer satisfaction.
6. Regulatory Compliance
Many industries must abide by regulatory compliances concerning data storage and processing. IoT analytics can help companies to adhere to these regulations.
Use-Cases of IoT Analytics
IoT data analytics enables the industry to deliver better outcomes in several ways. Let’s look at a few ways.
1. Crop Monitoring
Data from sensors are used to monitor soil humidity, temperature, and weather conditions to optimize irrigation and improve crop yield. Companies like Weather Risk Management Services (WRMS) are enabling farmers to protect their crops with this technology. Farmers are empowered with apps like SECUFARM to manage their farms.
2. Monitoring Patient Health
Data from wearable devices, such as smartwatches, is analyzed to take proactive health measures. This approach often reduces patients’ visits to hospitals. Dexcom uses the technology to enable continuous glucose monitoring for diabetic people. Ultra human enables people to take charge of their nutrition and fitness with their wearable biosensors.
3. Traffic Management
Managing traffic is an excellent use case of IoT analytics. The data from traffic signals, connected cameras, and cars is analyzed to improve traffic management, reduce traffic congestion, and provide better mobility for the public. No Traffic enables cities to manage their traffic using a plug-and-play sensor and a control unit.
4. In-Store Customer Experience
Data from in-store cameras, sensors, and beacons is analyzed to understand customer behavior and preferences so their in-store shopping experience can be improved. Get Go, a chain of convenience stores in the U.S. has replaced conventional cooler doors with IoT-enabled display screens that show pricing, promotions, and nutritional value of the food items, enhancing the experience of 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 effectiveness. Duke Energy uses IoT devices coupled with data analytics to optimize the performance of their grids by predictive maintenance.
6. Fleet Management
Data from GPS installed on vehicles and other onboard sensors is analyzed to optimize routes, monitor fuel consumption, and improve the overall effectiveness of the feet. Controlant, a pharmaceutical supply chain company, uses IoT SIMS to optimize supply routes and ensure the secure delivery of goods.
7. Air Quality Management
Sensors collect data related to particulate matter (PM), NO2, and other pollutants in the air and transmit them for analysis. The devices can be located in residential areas, traffic sites, industrial sites, etc., to help people understand air quality clearly. BreezoMeter and Airly use IoT to detect the level of air pollution in the U.S. and enable users to plan their outdoor activities.
