Probabilistic matching is a technique used to identify and link records that may refer to the same entity, even when the data is incomplete or inconsistent. It relies on statistical methods to assess the likelihood that two records match based on various attributes, such as names, addresses, or dates. This approach is particularly useful in fields like data integration and customer relationship management. By calculating the probability of a match, probabilistic matching can handle variations in spelling, formatting, and missing information. It contrasts with deterministic matching, which requires exact matches. This flexibility makes it valuable for organizations dealing with large datasets, such as healthcare providers or e-commerce companies.