The number of IoT devices in industrial control systems continues to grow at a rapid pace. With this growth in networked devices comes a significant increase in the volume of data that industrial companies must be able to manage and leverage for business outcomes.
Scalable, flexible analytics can contextualize your information and deliver value incrementally in devices, the plant and the enterprise.
We’re learning when it makes the most sense to analyze the data in real-time at the source or store it in the cloud for more long-term examination. Conditioning raw data into contextualized data, preferably at the source, is becoming an increasingly valuable best practice.
I’m seeing more focus now on edge computing from companies and industry groups. Companies realize now that if they store every bit of unstructured data with the hope of finding patterns and business value, they will spend significant resources to clean up and organize the data later. A scalable analytics approach can help you prevent data overload by solving problems that exist at different levels of your enterprise.
Local maintenance analytics, for example, can use device-level data to produce real-time alerts about critical device and machine health. This can help you implement faster decision-making closer to the process, where time is critical.
Machine-level or plant-level analytics implemented in edge devices such as controllers and plant-floor servers can be used to optimize machines, processes and plants. They also can be leveraged to implement predictive-maintenance strategies.
Enterprise-level analytics integrate plant-floor information with business intelligence. This can help you improve your operational productivity or compliance efforts across several sites.