The Industrial Internet provides a way to get better visibility and insight into the company’s operations and assets through integration of machine sensors, middleware, software, and backend cloud compute and storage systems. Therefore, it provides a method of transforming business operational processes by using as feedback the results gained from interrogating large data sets through advanced analytics.
The business gains are achieved through operational efficiency gains and accelerated productivity, which results in reduced unplanned downtime and optimized efficiency, and thereby profits. Although the technologies and techniques used in existing machine-to-machine (M2M) technologies in today's industrial environments may look similar to the IIoT, the scale of operation is vastly different.
For example, with Big Data in IIoT systems, huge data streams can be analyzed online using cloud-hosted advanced analytics at wire speed. Additionally, vast quantities of data can be stored in distributed cloud storage systems for future analytics performed in batch formats. These massive batch job analytics can glean information and statistics, from data that would never previously been possible because of the relatively tiny sampling pools or simply due to more powerful or refined algorithms.
Process engineers can then use the results of the analytics to optimize operations and provide the information that the executives can transform to knowledge, in order to boost productivity and efficiency and reduce operational costs.
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