Data Matching Best Practices

It is recommended to always follow these guidelines for data matching:

1. Make sure to include at least one Exact match criteria in every match definition created.
2. For matching addresses, configure match definition as:
   1. Exact match on A Street Number.
   2. Fuzzy match on A Street Name.
   3. Exact or fuzzy match on A Zip Code (depending on the type of the ZIP code column).
3. For names, configure match definition as:
   1. Fuzzy match on First Name with Weight at 80%.
   2. Fuzzy match on the Last Name with Weight at 95%.
4. For projects with only one match definition, always check the “Similar Records in Group” box. This ensures that all records in a result group are a match to each other. If left unchecked, you may end up with groups in which record A matches record B, and B matches C, but A and C are not a match.
5. Increase “Max Matches Per Record” to a value larger than the largest expected group so that no matches are lost.
6. Use like-kind criteria for each definition. Unrelated criteria should be added to separate definitions.