by Exosite, on May 6, 2016
Enabling predictive IoT analytics is a turning point for an organization. It marks the point when the hard work of understanding what happened–and why it happened–can finally be put to use. An organization that implements a strong IoT business strategy that includes analytics is structured to reap the benefits of their connected devices. Building from our previous IoT analytics blog segment, this series continues to break down key predictive analytic features and this week focuses on cluster analysis.
Cluster analysis is a huge component of IoT data analytics. It involves predicting device behavior by group devices according to their attributes, which could include descriptive data and metadata like user age, model, and manufacture data.
For example, in the case of a fleet of IoT-connected elevators, a number of them may be experiencing too much downtime each month. Upon undergoing cluster analysis, data might point to a certain subset of the elevators that are problematic based on similar characteristics that include having been serviced more than 12 months ago and exceeding average runtimes of eight hours each day. Cluster analysis provides the elevator manufacturer with a reliable and efficient course of action: to schedule maintenance on elevators approaching twelve months since they were last serviced, prioritizing those running more than eight hours a day. Understanding this correlation also allows the elevator manufacturer to ensure that these problems do not arise in the future.
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