Bookshelf v7.5: Siebel Data Quality Functionality

Siebel Data Quality Administration Guide > Siebel Data Quality Product Overview >

Siebel Data Quality Functionality

The name and address data stored in account, contact, and prospect records in Siebel eBusiness Applications represents your profile of existing and potential customers. Because of the importance of this data, maintaining its integrity is critical.

Siebel Data Quality helps enterprises standardize and consolidate their account, contact, and prospect data in the following ways:

Data matching (deduplication). Data matching identifies possible duplicate-record matches for account, contact, and prospect records, based on administrator-defined parameters. You can merge duplicate records into a single record using the Data Quality administrative views. This guide generally uses the term data matching, or just matching, for this functionality. The term deduplication is also used in some circumstances to describe matching, typically in discussing matches in configuration files, user properties, and other system parameters. You can use the Siebel Data Quality Matching Server or the Siebel Data Quality Universal Connector to perform data matching tasks.

Data cleansing. Data cleansing standardizes the structure of data in the customer profile, and is most often used to standardize name and address information. You use the Siebel Data Quality Universal Connector to perform data cleansing tasks. Data cleansing typically consists of the following functions:

Address correction. Street address, city, state, and postal code information is stored in a uniform and consistent format, as mandated by United States postal requirements. For recognized U.S. addresses, address correction provides ZIP+4 data correction and stores the data in certified U.S. Postal Service format.

Capitalization. Account, contact, and prospect names are converted to mixed case (uppercase and lowercase letters). Address fields are converted to mixed case, all lowercase, or all uppercase.

Standardization. Account, contact, and prospect information is stored in a uniform and consistent format.

Data cleansing can also be extended to include modifying or enhancing fields from within a customer profile by using the capabilities of external vendors, such as demographic, psychographic, or geocode attributes. Geocode is a standard set of information that many companies sell, including latitude and longitude coordinates, and other location information.