by Exosite, on April 12, 2016
The first step in data analytics maturity is describing what is happening with the devices in a connected product deployment. What is the average pressure inside a device? What is the device's maximum runtime each day? What is the greatest period of time that a device is at its highest pressure?
The measurements and visualizations typically seen in descriptive analytics include moving average standard deviation, histograms, and quartiles. Descriptive analytics do not really dive into the causes of device behavior or even look much further than the facts themselves. However, descriptive IoT data analytics are the building blocks of a mature analysis program; even at this level, it is possible to see how descriptive analysis leads to action.
An important part of any IoT strategy is being able to visualize how your team will be using the data from your connected solution. Imagine a pump in an industrial application that has an average pressure of 600 PSI and never exceeds 650 PSI for more than a few seconds. Descriptive analytics can help identify when a pump is operating at pressure levels outside this normal range. For example, a technician with this type of descriptive information is much more likely to notice that a pump has been running at 700 PSI for several minutes and identify this as a potential problem. In this way, descriptive analytics enables troublesome behavior to be addressed immediately rather than letting it run its course, helping avoid device failure and potentially thousands or millions of dollars in damage and lost productivity.
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