Uptake Solves Dirty Data Problem with AI Data Integrity Capability for Maintenance Optimization

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A recent press release states, “Industrial artificial intelligence (AI) leader, Uptake, announced it is solving critical problems for the Energy, Industrial and Utilities sectors by providing comprehensive data integrity capabilities for operations and maintenance (O&M) that allows work order cost analysis overcoming the industry challenge of incorrect, dirty work order data. Companies worldwide are using advanced analytics and machine learning for predictive maintenance strategy optimization as a driving force to reduce operational costs. However, the lack of clean data is a major impediment to achieve those cost savings. Industrial companies are particularly susceptible to dirty data due to wildly disparate, legacy systems, frequently missing, or miswritten, data and the prevalence of proprietary systems that have their own data language. Uptake has solved this problem.”

The release goes on, “Asset IO, Uptake’s Asset Performance Management (APM) application, includes a new module capability to ingest years of work order data – from existing computerized maintenance management systems (CMMS) and enterprise asset management (EAM) systems – that leverage artificial intelligence and natural language processing to complete missing data, suggest asset labels and create an asset category schema where none exists. The result is a clean dataset for work order cost analysis that can be used to inform critical preventive and predictive maintenance strategies to de-risk operations and reduce annual O&M costs significantly.”

Read more at PR Newswire.

Image used under license from Shutterstock.com

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