One of the key goals of Industry 4.0 is business optimization, whether it’s from predictive maintenance, asset optimization, or other capabilities that drive operational efficiencies. Each of these capabilities is driven by data, and their success is dependent on having the right data at the right time fed into the appropriate models and predictive algorithms.
Too often data analysts find that they are working with data that is incomplete or unreliable. They have to use additional techniques to fill in the missing information with predictions. While techniques such as machine learning or data simulations are being promoted as an elixir to bad data, they do not fix the original problem of the bad data source. Additionally, these solutions are often too complex, and cannot be applied to certain use cases. For example, there are no “data fill” techniques that can be applied to camera video streams or patient medical data.
Any data quality management effort should start with collecting data in a trusted environment. This, in turn, implies that the data sources (machines, IoT devices, etc.) and the data collection processes are all trusted.
When a new device, is added to a connected factory system, it’s onboarded by authenticating it, typically by using its IP address and a password. Then it’s authorized to communicate data over the network. The flow of data across the network is typically protected through the use of a secure protocol like TLS or SSL, so there is a secure channel established for ongoing data transfer. The foundation is set for trustworthy data.
But as time moves forward, the dynamic nature of an industrial environment means that this initial foundation must be renewed over time. For example, unless there are proactive checks to validate the ongoing authenticity of the device or regular password updates, the device may have been compromised. The original authentication of the trustworthiness of the device as a data source is no longer valid. A method for continuously updating this verification is required.
Data generation and communication from these devices is also key. Many sensors only communicate “events” or changes from the norm. In the case of a temperature sensor, if temperatures in a production environment are being maintained in a tight, valid range, the sensor may not be transmitting data for a significant period of time, say days or even weeks. But this quiet period can be difficult to interpret and impossible to rely on. What if the sensor has lost connection or malfunctioned? You need a way to validate the proper operation of the sensor itself to know for certain that you aren’t missing valid data just because none has been received.
And while sensor downtime may be unavoidable, rapid insight into the state of the sensor also provides benefits. You’ll have the ability to act more quickly to rectify any operational issue with the sensor. And you’ll have a measure of the actual time period for which data were not received due to a sensor malfunction.
Finally, a trusted device is one that is working in its “right” operational state. That implies more than that the device is working and properly authenticated. For more complex devices, like a camera, it includes a measure of its complete set-up.
