In our industrial Internet of Things (IIoT) data problem series, we’ve arrived at the tricky issue of misidentified data in remote monitoring systems. Misidentified data is high-quality, complete, and on time. However, the identity of the asset it represents in the...
Through our data problem series, we’ve covered several data challenges that industrial companies face when pursuing digital projects. Some struggle with low-quality data, while others are limited by data silos and legacy systems. Many of the specific issues we’ve...
In the industrial space, data silos are everywhere. Many industrial companies today still rely on cumbersome data management systems that render operations data inaccessible to those who need it most. In other words, the data is “siloed”. Siloed data is a...
Another data issue in our series of Industrial IoT data problems is that of Latent Data. High latency is limiting productivity for industrial companies across the globe. Simply put, latent data is data that doesn’t show up quickly enough to have value. Or, at...
Continuing with our data problem blog series, we tackle the topic of incomplete data, another challenge many businesses face when considering digital technologies like SCADA systems to improve their business processes. Incomplete data is distinct from the problem of...
Bad data collected by a remote monitoring system can wreak havoc on industrial organizations. When prevalent in business processes and analysis, bad data creates a fundamental disconnect between what is actually happening in the field and what decision-makers think is...
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