Uptake, a leading provider of industrial AI software, and Element, the provider of the only data hub designed to manage asset data for industrial organizations, have partnered to create an AI solution for industrial business. The solution automates data integration, data science model configuration and the production of insights that detect and prevent failures and trigger work orders.
"We're taking the complex process of turning messy industrial data into rich insights and simplifying it to a level that's never been seen before," said Jay Allardyce, Uptake Head of Industry and Ecosystem. "When you look at the output that comes from knowing how to analyze and take action from that data, you're talking about measurable results like increased annual energy production and millions of dollars in new revenue for businesses."
"Element and Uptake are helping industrial business accelerate growth and top-line savings through AI and machine learning," said Andy Bane, Element CEO. "What used to take industrial businesses months and years to accomplish through manual data entry and processing is now done in a matter of minutes."
With the massive amount of data generated by industrial assets, companies are increasingly searching for simple ways to turn this data into action that improves their bottom line. Data historians such as the OSIsoft PI Systemä are crucial for organizations to get data off of their assets in real time and into a centralized repository. This data then needs to be combined and correlated with data from other enterprise and public sources into a platform, where it can be analyzed. Traditionally, this process has been slow, manual and cost prohibitive.
The offering from Uptake and Element provides one AI solution that automates the onboarding of trusted, normalized data to curated data science models — built using industry expert content and pre-configured for specific equipment types.
The joint solution will:
- Automate and significantly speed up data ingestion, preparation and the contextualization of industrial assets
- Create a single repository of trusted industrial data in order to make it useful for analytics
- Build data science models that have been curated for those specific device types
- Generate insights that help detect anomalous behavior, predict failures, trigger work orders and optimize the performance of specific outcomes