For many, if not most enterprises, the existing data analytics infrastructure is not ready for IoT prime time. In this new era of collecting IoT data, the traditional IT infrastructure is not capable of handling the massive increase in volume generated by the IoT. In fairness, these infrastructures were not originally built for this type of information processing and real time decision making. The reality is that IoT analytics has very unique requirements compared with analytics for traditional data. Some of these requirements need to take into consideration the format of the data, data depth and richness, time sensitivity, and where and how long the data is stored. The following article provides good depth, along with some real world examples of companies that have altered their IT environments to make them more IoT-ready.
The growth of the Internet of Things (IoT) is having a big impact on lots of areas within enterprise IT, and data analytics is one of them.
Companies are gathering huge volumes of information from all kinds of connected of objects, such as data about how consumers are using certain products, the performance of corporate assets, and the environmental conditions in which systems operate. By applying advanced analytics to these incoming streams of data, organizations can gain new insights that can help them make more informed decisions about which actions to take. And with companies placing IoT sensors on more and more objects, the volumes of incoming data will continue to grow.
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