MDM refers to master data management, which provides dimensions for big data facts, the same way big data projects highlight a link to data governance and quality. A good example of data matching application is in contribution model, where App data consolidation combines with User collaboration. This happens when you need data from applications to merge in a consolidation hub, which requires data matching and merging, at record and field level, with complex rules of survivorship. Here are the benefits.
It configures sources that are more trusted than others to high trust score data providers, and this allows configuration of field level priority, for more trusted data for a particular area. Experts refer this to as the trust framework and works well for mergers and acquisitions. It also applies where connecting disparate, yet potentially duplicate data sources is vital.
You realize this better where there is the likelihood of having duplicate records, volume discounts, and relationships from the same provider. It is better to renegotiate one large volume discount, which is what MDM is can identify when applied.
Searching is one of the ways that involves implementing MDM by use of cloud MDM, which makes it possible to search duplicates as they enter. It matches batch job settings to pinpoint the duplicate and then feeds this logic into the search of records, to give potential duplicate at the point of entry. The aspect worth observation, in this case, is the ability to toggle between matching settings for searching, and that which is you can use to find duplicates to merge.