Revolution of IoT

IoT is Revolutionizing The World

The first thing to realize about IoT data analytics is that it involves the use of sensors for data collection. Sensors are really cheap these days and are sophisticated enough to support a variety of use cases. Data analysis and data pattern recognition are important analysis tools that are used after the sensors have done their part in providing the raw data. Now, the ultimate goal behind IoT is not just fancy data. The goal is the usage of said data to understand people.

Product Usage Analysis

IoT has the ability to revolutionize businesses and the way they perceive their customers. In fact, it’s already happening. IoT is helping businesses gain more insights on how consumers are using their digitally connected products. Birst, a self-service analytics solution, is a good example of IoT analytics. Birst gathers data from internet-connected coffee makers and transmits information on the daily consumption of coffee. The data, once connected with data from their social media helps to determine whether the consumers who consume more coffee are more likely to discuss the brand on social media platforms

For business owners, the goal of IoT should not just be the introduction of a new line of internet-savvy products. They should also consider the data collected from the products to gain marketing insights, and how business operations are progressing.

Serving Consumers and Businesses

The data gathered from IoT has the potential to be used for both businesses and consumers at the same time. Let’s take the example of an IoT for home utilities, like an energy meter. The data collected from said IoT is sold to local and state governments within a country, who then use it for both revenue collection and fraud detection. On the consumer’s end, the same analytics can be used to help them manage and check their energy consumption

Data From Sensors Enabled Devices

The field of social analytics is, perhaps, one of the most exciting domains of IoT. It involves the use of multiple data sources to gain actionable insights into user behaviors. The phenomenon is referred to as “connected events”. Connected events are large-scale sensor deployments that help in understanding user behavior. The sensor enables analysis of human emotions rather than using device imaging. More commonly, it is akin to the field of sentiment analysis. A good example of this is the use of a heat-map of spectators during a sports game. Combined with other sensors and game data, we can gain an understanding of spectator sentiments during the game.

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