Keywords: fuzzy match, fuzzy data matching, data matching, string similarity, approximate string similarity, approximate string matching, record linkage, records linkage, entity resolution, dedupe, deduplication, data deduplication, QDeFuZZiner, data cleansing

Do you need to link, merge, combine, cleanse or deduplicate your datasets?
Availability of a reliable master data for a supply chain can be crucial for success of a company. Supply chain performance is dependent on consistent definitions of customers, products, items, locations, and other master data objects. When data is poorly governed and inconsistent, supply chain become less competitive. Good data leads to efficient supply chains, allowing resources to be spent on innovation rather than on coping with problems.
However, business data is often messy, containing duplicate entries of same entities, missing information, having misspellings and various other errors.
In many cases, there is no unique identifier which can be used to unambiguously identify an entity. When such data sources need to be linked or deduplicated solely based on string similarity, it is called fuzzy data matching.
Fuzzy data matching is set of techniques to determine same identities, i.e. related records in two different datasets, when there is no unique identifier to identify entities. In such cases, records are compared based on approximate string similarity. Records that have calculated string similarity above a custom threshold are considered to be same entities.
QDeFuZZiner is a powerful and user-friendly fuzzy data matching, record linkage, data merging and data de-duplication software, which can help you to consolidate and cleanse your messy business data.