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Imagine such a system processing unstructured text data like historical maintenance logs, technician notes, defect reports and warranty claims, and correlating it with structured sensor data such as IoT readings and machine telemetry.
Cognitive analytics is basically the opposite of descriptiveanalytics. In descriptiveanalytics, the task is to find answers to predetermined business questions (how much, how many, how often, who, where, when), whereas cognitive analytics is tasked with finding the business questions that should be asked.
IoT examples such as telematics-based travel or car insurance enable a very personalized insurance policy (more on this in a prior post ). The next step leads to performing exploratory, descriptiveanalytics, “why is this happening,” and so on.
Descriptiveanalytics also help them understand the number of athletes and workers required to support that specific competition or sport. “We get access, post-Games, to the ticket data to analyze any patterns in terms of incidents and responses.”.
Integrating IoT and route optimization are two other important places that use AI. Artificial Intelligence Analytics. Tasks which include billing, scheduling, operating machines like forklifts and workforce management can be enabled with an AI-driven warehouse management system, fleet management system or freight management system.
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