As manufacturers across the U.S. increasingly digitize their operations, data management and standardization have become a growing concern.
More than 60% have a strategy or guidelines for data management, but only 15% follow the plan in its entirety, according to a recent survey by the National Association of Manufacturers (NAM). 44% of manufacturing leaders say the amount of data they collect has doubled in the last two years alone, and it’s anticipated to triple by the end of the decade.
86% of the respondents believe that the effective use of manufacturing data will be essential, yet only 1 in 4 have high confidence that the right data is being collected.
AI models and agentic systems depend on accurate, real-time data for every decision, so when this data is wrong at the source, it can have ripple effects on model output and cost millions, said Prateek Kathpal, president of SymphonyAI’s Industrial Division. The company makes IRIS Foundry, specifically designed to integrate data from IT, OT, and engineering to deliver AI-ready analytics.
“Machines in plants generate massive volumes of data that are often trapped in isolated systems, limiting visibility and slowing down everything from root-cause analysis to predictive maintenance,” he said.
