IOT, known as the "Internet Of Things" is a growing field, with more and more physical devices being connected to the internet each day. In fact, it is expected that 41.6 billion IoT-powered devices will be connected to the internet by 2025. Data analysis goes hand in hand with Iot. IOT devices continuously generate huge volumes of data that companies need to sort, manage, and evaluate in order to continuously improve their organizations. This guide will give you some insight on how data from IOT devices is used by organizations to improve products, predict equipment maintenance, and optimize their office spaces/warehouses.
Companies constantly evaluate how consumers are interacting with their IOT devices to get ideas on which new features would be a great choice to add to their products. For instance, Philip’s Hue Lights are one of the most popular IOT devices, allowing users to control their lighting and customize the ambiance in their home. Philips analyzes their data to see how many people are using the custom color feature on their lights, which influenced them to release the sync feature/product in which the lights can sync to your tv, laptop, or music to create an immersive entertainment experience.
Organizations also use the data from IOT devices to predict when their equipment is due for maintenance or an upgrade. Restaurants install IOT sensors in their refrigerators and walk in freezers to help them detect temperature changes. They can set alerts that will notify them if the temperature goes above or below a certain threshold to predict when the equipment needs to be serviced, helping them avoid food spoilage.
Energy consumption and employee safety are some of the key internal metrics that organizations are continuously monitoring and IOT makes evaluating that information a lot easier. For instance, many companies install light sensors that will automatically shut off the lights in a room if the sensor doesn’t detect movement after an allotted period of time. Many companies also install sensors in the elevators to predict service maintenance, identify when key components need to be upgraded, and track elevator usage to increase route optimization during high traffic times.
