For starters, IoT data is better suited for analysis and processing through using ML algorithms. Businesses can help organize and tag data by using trained algorithms. With ML, a company can ensure data provenance and classification, and whether it meets certain requirements for compliance. This could be especially helpful for IoT deployments in the highly regulated financial services and medical fields.
ML can also be used for the analysis itself. Dave Schubmehl, research director for cognitive and AI systems at IDC, said: “The technology can provide predictions, potentially prescriptive actions or advice for enterprise IoT deployments.”
The core use case for this type of algorithm is predictive maintenance. “This is finished when sensors on complex machines send data back. The data is used to predict when various sub-systems might fail and recommend when that machine should prevent from occurring failures. Therefore, businesses can save money and time,” said Schubmehl.
Another way that ML can be used with IoT initiatives is resource management. Schubmehl said, “Companies like John Deere use sensors on tractors and farm equipment to monitor the state of the plants, soil, insects, moisture, etc. in order to build predictive models to exactly gauge how much fertilizer, pesticides and water should be applied to maximize crop yield.”
ML can create business value by collecting data from radio frequency identification (RFID) tags. Schubmehl gave the case of RFID used in the shipping industry to optimize routes and logistics for supply chain. Christian Renaud, the firm’s research director of IoT, said: “This is seen a lot in trucking, where ML is used to determine which route impacts the engine the least and helps maintain the best fuel economy.”