Data reduction

Data on the same event is often produced by multiple devices

Data on the same event is often produced by multiple devices, leading to staggering amounts of duplication and data bloat. Likewise, devices such as network attached storage frequently generate hundreds of lines of the identical log data for a single incident.

Making matters worse, IoT devices are capable of generating multiple data transmissions per second, resulting in overwhelming volumes of nearly identical data sets. Instead, perhaps you only need one data point per minute from these devices, so as to send a fraction of the data to your analytics platform while still adhering to your accuracy requirements.

Onum data reduction
Onum arquitecture

Data bloat from IoT devices

The Onum Platform can group all generated data by a specified timeframe (e.g., one minute) and provide the following information on the metric for that timeframe: statistical average and median; the minimum and maximum; the standard deviation. All parameters, including the timeframe assessed, can be completely customized, and individual event alerts and aggregate metrics at the edge can be defined.

Similarly, logs typically aren’t designed to be size efficient

You frequently only need a small fraction of the fields they include, but they’re difficult to change at the source. As a result, you end up paying for all the data they include, no matter how little of it is of use to you.

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