What is Data Matching?
Data Matching is the errand of discovering records that allude to a similar entry. Regularly, these records come from different data sets and have no normal entity identifiers; however, data matching procedures can likewise be utilized to recognize duplicate records inside a solitary database.
Distinguishing and matching records across different data sets is a difficult assignment for some reasons. Above all else, the records ordinarily have no characteristic that makes it direct to recognize the ones that allude to a similar entity, so it is important to examine credits that give incomplete ID, for example, names and dates of birth (for individuals) or title and brands (for items). Therefore, data matching calculations are touchy to data quality, which makes it important to pre-measure the data being connected to guarantee the lowest quality norm, at any rate for the key identifier columns.
How does Data Matching work?
We use "unique identifiers" to perform data matching. These are set properties and do not change with the passage of time. We can link and perform probabilities for each property. For instance, supposing two data sets as identical and then applying unique identifiers on them to distinguish them.
Types and Examples:
Data matching is being used in a wide range of industries. For example, e-commerce, this market contains the same products presented with different descriptions. At this point, many comparing applications have been developed that do data matching across brands and bring the same products. In computing, Data matching is used to avoid redundancy which ultimately saves storage space. It is also used in the healthcare industry where it records and retrieves patient data.