![]() ![]() Some of them are generic, such as and are often associated with a place of business and are never associated with a unique individual. Many email addresses are not useful for identity resolution. If there are more than 25 distinct email addresses, those records are collapsed into a trivial duplicate. settings / soft - trivial - dupe - size - threshold 25įor each unique combination of PII–excluding email addresses!–the distinct email addresses that are associated with that unique combination of PII are compared. Apply the email semantic to both columns, and then apply email-primary to the personal_email column and email-alternate to the work_email column.Īmperity will create columns in the Unified_Coalesced table similar to: ![]() This approach provides a way for downstream processes to have an opportunity to query against specific types of email addresses, such as “email-primary” and “email-alternate”.įor example, a data source has two columns for email addresses: personal_email and work_email. There are some options available as you determine the best way to configure Amperity for email addresses:Īpply namespaces to emails (recommended when multiple email addresses are present)Ī namespace appends a string to the email semantic. When a data source provides more than one email address in the data, it’s important to apply the email semantic to all of the fields, and then apply a custom semantic that appends a namespace (recommended) or ordinal to support potential downstream workflows. A customer record may be associated with multiple email addresses.Ī customer data source may have more than one field that contains an email address, such as for personal email and work email addresses. The email address that is associated with an individual customer record. This will create columns in the Unified_Coalesced table like address, address_1, address_2, address_3 and so on and will keep each location (which is a combination of address, address2, city, state, postal) intact. Use an ordinal to append an integer to each semantic within an address group. When a data source provides more than one address group in the data, it’s important to apply the address, address2, city, state, and postal semantics to the correct fields in every group, and then apply a custom semantic that appends a namespace (recommended) or ordinal to each semantic within the address group to support potential downstream workflows. Many individuals are often associated with home addresses, billing addresses, work addresses, and so on. An address group depends on all of these details to define a complete address. Some address groups also have apartment numbers or PO boxes. Address groups ¶Īn address group consists of a street address, city, state, and postal code. Verify that custom semantics and foreign keys do not have any typos or misspellings. Make sure the application of custom semantics is done consistently across all data sources. ![]() Make sure that you do not miss any opportunities to correctly tag customer data for all semantics, custom semantics, and foreign keys. ![]()
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