When business leaders research and consider IoT adoption, its easy to find lists that cite the benefits of IOT, such as more efficient operations and long-term cost savings. Although this can be true, such conversations are mainly tangential to the principal overarching benefits of IoT: knowledge and insight.
Accurate and timely decisions demand knowledge and insight that can be difficult or even impossible to obtain. Businesses strive for such knowledge and insight, using it each time a sales manager forecasts the next quarters revenue or a production manager decides whether to shut down a key machine in a vital production line for routine maintenance. The stakes are far higher when state inspectors discover structural defects in long-neglected municipal infrastructure or physicians struggle to keep an aging patient healthy.
IoT provides better immediate knowledge through measuring and reporting specific real-world conditions. Its modern instrumentation. The real-world condition can be examined and responded to in real time. If a heart rate monitor alerts to an excessive heart rate, the patient can slow down and relax to lower the heart rate to an acceptable level, take appropriate medication, contact their physician for further guidance or even call for medical assistance. If a traffic monitoring system sees a backup on a major highway, it can update travel apps of the prevailing conditions and enable commuters to select alternate routes and avoid the congestion.
But the real power and benefit of IoT is the long-term insights that it can provide to business leaders. Consider the vast number of IoT sensors that can be distributed throughout equipment, vehicles, buildings, campuses and municipal areas that enable better long-term insight through advanced analytics -- the back-end computing processes capable of evaluating and correlating a huge quantity of seemingly unrelated data to answer business questions and make accurate predictions about future circumstances. The data collected can also be used to train ML models, supporting the development of AI initiatives that achieve a deep understanding of the data and its relationships.
For example, the varied sensors distributed in an industrial machine can be analyzed to detect variations in operation and condition, which might suggest the need for maintenance or even predict an impending failure. Such insights enable a business to order parts, schedule maintenance or make proactive repairs while minimizing the disruption to normal operations.