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Utilizing a Distributed Data & Computing Architecture in Analytics Applications

Advances in analytics for equipment and device data have proven their value in the sustainability arena by dramatically improving energy efficiency, comfort and reducing operational costs. The first generation of analytics solutions were designed with the requirement to transmit all data from equipment systems to the cloud or centralized servers where analytics and the generation of visualizations and reports would then be performed. However, there is tremendous value in recognizing the strategic impact of distributing analytics at different layers of our data networks and efficiency to be gained. It may not always be possible or necessary to transmit every piece of data from every IoT device to the cloud before being able to gain value from that data. To realize the full benefits of analytics and the promise of the IoT, systems need to embrace the highly distributed nature of facilities and equipment systems by providing an architecture that enables data acquisition, analytics processing and control to occur where it is most efficient, cost effective, and reliable.

Learning Objectives

Understand the concept of a distributed, edge-to-cloud analytics architecture
Understand the benefits provided by implementing a distributed architecture
Understand the use of a distributed architecture in multiple real world applications
Understand analytics-driven control at the edge