A Guide to IoT Analytics: Benefits and Use Cases
By Bill Dykas
September 14, 2023
What Is IoT Analytics?
When discussing Internet of Things (IoT) analytics, people tend to think of artificial intelligence (AI) and machine learning (ML). However, the most basic form of IoT data analytics is detecting anomalies and trends.
In an immediate sense, real-time analytics in IoT look at data to see if something is statistically crossing an established threshold. These thresholds provide better visibility, allowing businesses to react quickly to changes.
A time series method takes large pools of data and examines multiple variables to determine if there are any patterns. By probing these trends, companies can:
- Open new revenue streams
- Resolve operational issues
- Recognize areas needing improvement
Get Your Manufacturing Plant Ready for Industrial IoT
The Two Types of IoT Data
IoT data analytics can be divided into two types:
- Data about the device
- Data generated by the device
The former usually refers to health, safety, location and maintenance. For example, if there is a battery-powered sensor in the field, it’s critical to know the state of the device’s power level. The signal strength would be relevant if it is a wireless- or cellular-based device.
An example of device-generated data would be a temperature sensor that tracks the internal environment of a refrigerated vegetable truck. You can monitor this data to prevent products from spoiling, thereby avoiding loss of potential revenue.
How Does IoT Analytics Work?
There are six elements of IoT analytics:
- Capture: IoT devices will collect data and process it in a cloud environment.
- Rationalization: This process is helpful in a cellular environment. For instance, if a device sends the same temperature reading every ten seconds, it wastes cellular data, as it should only send when there is an anomaly.
- Transformation: Converting data for one business application into a usable form for another.
- Event stream processing: Technology that helps users detect and react quickly to crossed thresholds or matched statistical patterns.
- Normalization: As part of data orchestration, this helps the customer make sense of the data coming into their IoT analytics platform. It enables them to learn more about their products and drive innovation.
- Visualization: Making the analytic results easy to understand may leverage visualization techniques like dashboarding to simplify the presentation.
IoT Analytics Use Cases
While there are many use cases, here are several impressive examples of industrial IoT analytics.
Tracking Engine Performance and Anti-Theft Measures for Construction Equipment
The data generated from telematics devices on compact tractors and loaders is sent to a data orchestrator, which moves it to a resource planning system. Then, the customers can use this data in a product life cycle management application to track the performance of their equipment’s engine and maintenance level.
The customer can also send the data to the dealer network application. Here, data thresholds can serve anti-theft purposes (e.g., why did the vehicle start at midnight, why is it far from a geolocation, etc.).
AI and ML in an Automobile Factory
When wheels are installed on a car in an assembly line, an AI and ML inference engine can recognize if a wheel is missing a lug nut or has a scratch. It can trigger a ticket system without human intervention. In this scenario, the AI and ML inference engine and supporting camera are connected to the deviceWISE® IIoT platform, powered by Telit Cinterion. deviceWISE empowers factory operators to:
- Decrease operational costs
- Optimize resource utilization
- Minimize errors
Connect the Factory to the Enterprise for Industrial Digital Transformation
With deviceWISE EDGE, you can connect plant floor devices and integrate enterprise systems and business services without custom code. Speak with our experts to get your manufacturing plant IIoT-ready.
