The more data you have, the more you can learn about your business operations and drive improvement. At least, that’s how it should work in theory.
Many industrial organizations continue to add more sensors to their machines in an effort to drive data collection and analysis, but this solution doesn’t scale. Discovering anomalies that could lead to production issues or failures requires a large group of domain experts that tirelessly sift through this mountain of data. It’s hard to know which anomalies are important and how they influence each other, especially when businesses may not even have all the information they need.
With the launch of our cognitive anomaly detection and prediction framework, Progress DataRPM can help you capitalize on these opportunities. Download this datasheet to learn how DataRPM enables you to navigate this world of “unknown unknowns” with utmost certainty.
Anomaly Detection and Prediction powered by Progress® DataRPM™ automates data science, enabling asset-intensive organizations to gain exceptional control over the torrent of sensor data coming from every machine.
The automated, patented solution detects and predicts anomalies, delivers machine health insights, reduces the time required to develop and operationalize models, and helps data scientists be more effective.
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