New matching method could untangle data when the same object gets recorded differently across systems

A mathematician proposes a way to figure out when the same real-world thing has been recorded multiple times in different databases with slightly different information. The method handles both exact data (numbers) and fuzzy data (categories), without requiring anyone to convert values to make them comparable. In practice: if a patient's medical records are split across three hospitals, or a company's supply chain data lives in five different vendors' systems, this approach helps you know which entries refer to the same person or object.

Data deduplication is a real problem in systems that pull information from multiple independent sources. Right now, organizations usually either spend enormous effort cleaning and normalizing data by hand, or they accept duplicate records and the errors that follow. This paper proposes a mathematical measure that could automate the matching process across heterogeneous data. The practical consequence: systems that integrate data from multiple vendors or institutions could do it faster and cheaper. But this is a theoretical paper with no deployment evidence or real-world testing reported, so the gap between 'mathematically sound' and 'actually works in production systems' remains unknown.