WinPure Fuzzy Matching Software will find the truest matches with the least false matches thanks both to its intelligent data matching engine and combining it with the in-built knowledge base library system.
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Duplicate matches are beautifully presented with individual scores and many tools are included to help with merge/purge, selecting master records, deleting, updating and exporting the results.
A method that provides an improved ability to process word-based matching queries to find matching phrases or sentences from a lists or database. When an exact match is not found for a sentence or phrase, fuzzy matching can be applied. The process compares any data type of any length and from any place in a ﬁeld to ﬁnd non-exact matches. Fuzzy logic software tools like WinPure Clean & Match can be used to identify duplicated records across lists and databases, to help save on duplicate mailings and increase customer satisfaction.
Fuzzy matching is a type of data matching algorithm that is used to calculate probabilities and weights in order to determine similarities and differences between business entities like customers.
Many businesses need fuzzy matching so that they can clean and profile their data efficiently. Fuzzy matching steps in revealing duplicated and linked customer data, the human eye misses.
While machine learning and fuzzy matching do use patterns, fuzzy matching algorithms don’t need to train a machine in order to independently decide what data to clean and how. Instead, fuzzy matching relies on fuzzy logic matching, returning values between 0 (not true) and 1 (true). These fuzzy matching results are analyzed for the degree of likeness between two data sets to make decisions about data cleansing.
Fuzzy search is a process that uses several fuzzy matching techniques to filter and group customer data according to the set of user characteristics, likeness thresholds, and patterns you specify. In return, you get the potential matching customers of interest and the weight describing how likely one customer’s record resembles another.
The reliability of fuzzy search depends on suitable fuzzy search parameters and software to return a low number of false positives and negatives.
To avoid the likelihood of false positives and negatives, you will want to use reliable software to profile your data ahead of time. Next, you want to come up with the business rules and plans to clean the data. Then you want to use trustworthy automation to clean the data, meeting your goals.
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