Think before you deploy: the before, during and after of IoT data

Utilizing the digital twin

A digital twin is a data-model of a physical system using IoT sensor data to represent all the parameters of that physical system. If you think of a jet engine and all of the telemetry sensor data streaming from it in real-time, you could build a software model of that engine based directly on that sensor data. With the digital twin, you can manipulate various characteristics of the engine and see the effects in real time, all without having to use a real jet engine on an actual commercial airliner.

Mechanical engineers can use this timely, accurate and actionable sensor data to make informed and accurately tested adjustments to existing jet engines or influence the design of future jet engines.

Think before you design and deploy

Before designing and deploying a system, determine what data you want and need to collect and how it will be used to inform your business decisions. In the manufacturing scenario, you might want to monitor the vibrations coming off of a large piece of industrial equipment, so that you can identify upcoming maintenance problems, such as a bearing going bad.

Without this monitoring, the bearing would simply fail, and you would be forced to shut the line down while a part is ordered, shipped and installed. Having a production line down for any significant amount of time can be extremely costly. If you were monitoring the vibrations on the machine, you would be able to monitor changes, identify the problem, and order the replacement before it failed, thus minimizing and potentially eliminating any effect on production output.

Downsampling your data

Historical archiving is often overlooked when it comes to the analysis of IoT data. We’ve seen how collecting and acting on data in real-time can have huge effects on your business, but storing all that data forever can be extremely costly. Downsampling data is a process of reducing the sample of data down to the most important parts and in the least amount of space possible.

Collecting data at the millisecond level will help you identify and avoid potential problems, but will you really need that level of detail two, three or even four years down the road? Probably not. What you really need is to identify the data trends and highlight any anomalies or events that may have occurred. You can significantly reduce the amount of data stored over time by downsampling and expunging the highly granular data that is unnecessary and expensive to store.

You can store your millisecond-level data from the past 30-days, which is enough time to get highly accurate analysis of potential changes and looming problems. You could then downsample that data to one-minute averages while preserving any events or anomalies and the data immediately surrounding those events. You could even further downsample that data to five-minute averages — again, preserving the events and anomalies, and the highly granular data surrounding them — which could be stored indefinitely.

If a database has the ability to automatically expunge data via data retention policies, it could easily be completely automated. Further, automating could significantly decrease data storage costs, all without losing the important insights gained from IoT sensors.

Embrace the data wave

The data deluge is real. Rather than avoid it, businesses need to take the necessary steps to embrace it. Insights gathered from timely, accurate and actionable data have the potential to unlock business opportunities, streamline operations and minimize costs.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.