Generic data might contain either person names, organization names, or addresses. It might also contain a combination of these. For example, if the search for

'Greenhorn'

is expected to find the person,

Peter Greenhorn

, and the organization,

Greenhorn Industries

, then you can use

Generic_Field

to build search ranges based on the

Generic_Field

algorithm.

To match candidates to the search information, use match purpose ‘Generic’. ‘Generic’ match purpose matches general non-specific data. This purpose requires only

Generic_Field

.

The search ranges generated by

Generic_Field

assume that importance of information in the search text decreases from left to right. You can build ranges from the first entity in the search record. To meet this requirement, embed compound name markers between different entities in the search text. The

Generic_Field

algorithm limits the creation of search ranges to the first entity if compound name markers are present.

While the

Generic_Field

algorithm matches candidates to search record, it reduces the content in the significance of the order of words and allows flexibility to prepare match candidates. Algorithms designed for specific type of data, such as

Person_Name

,

Address_Part1

and

Organization_Name

carry significantly less overhead than the

Generic_Field

due to their targeted processing domain. The

Generic_Field

algorithm reduces the overhead to create and maintain separate indexes, and allows applications such as Identity Resolution to simplify the search experience by not having to categorize search data.

The

Generic_Field

algorithm has an edit list with rules that you can override with the Population Override Manager or Edit Rule Wizard.