Many people use this device because it provides information that may be of use to companies and industries. For example, Azure Stream Analytics provides real-time analytic data from IoT applications. The data is collected from numerous sensors that are found in manufacturing products, delivery trucks and weather stations. These sensors then provide analytics which can be used to determine the management of industries such as retail and healthcare.
IoT analytics is composed of three main components. These components are storage, stream processing software and an analytics engine. Storage is important because of the huge volumes of data that are found in the thousands of sensors across industries. However, companies must be cautious about the data they are storing because storing unwanted data can be a problem since it takes up space. Stream processing allows users to monitor and analyze the data that is stored in memory. An analytic engine filters and modifies data that is obtained from IoT prior to storing them to store the most efficient data. This device collects data from devices, then modifies it into a useful structure and finally processes the data into storage.
IoT analytics is extremely useful because it can help optimize and provide marketing and sales to companies. For example, these analytics helps identify customer needs and trends based on product usage and reviews. Once the data is processed, the technology will predict future purchases and helps develop future products. IoT analytics also delivers new services to companies because it helps make predictions for companies about future products or customer segments. These analytics also provide data which can be used to create pricing models and subscriptions that are manageable to both the customer and the business.
