Siebel Data Quality Administration Guide > Overview of Data Quality >

Data Matching and Data Cleansing

The data stored in account, contact, and prospect records in Oracle's Siebel Business Applications represents your existing and potential customers. Because of the importance of this data, maintaining its quality is essential. To ensure data quality, functionality is provided to clean this data and to remove duplicated data.

Data Cleansing

Data cleansing is used to correct data and make data consistent in new or modified customer records and typically consists of the following functions:

• Automatic population of fields in addresses. If a user enters valid values for Zip Code, City, and Country, data quality automatically supplies a State field value. Likewise, if a user enters valid values for City, State, and Country, data quality automatically supplies a Zip Code value.
• Address correction. Data quality stores street address, city, state, and postal code information in a uniform and consistent format, as mandated by U.S. 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. For example, 100 South Main Street, San Mateo, CA 94401 becomes 100 S. Main St., San Mateo, CA 94401-3256.
• Capitalization. Based on configuration, data quality converts fields for account, contact, prospect, and address to mixed case, all lowercase, or all uppercase.
• Standardization. Data quality ensures account, contact, and prospect information is stored in a uniform and consistent format. For example, IBM Corporation becomes IBM Corp.

Data cleansing is supported for the Account, Business Address, Contact, and List Mgmt Prospective Contact business components. For each business component, particular fields are used in data cleansing and this set of fields is configurable.

Data Matching

Data matching is the identification of potential duplicates for account, contact, and prospect records. Potential duplicate records are displayed in the Siebel application allowing you to manually merge duplicate records into a single record.

Data matching is supported for the Account, Contact, and List Mgmt Prospective Contact business components. For each business component, a set of fields is used for comparisons in the data matching process. The set of fields is configurable, and you can also specify other matching preferences such as the degree of matching required for records to be identified as potential duplicates.

In data quality you can enable and use both data cleansing and data matching at the same time, or you can use data cleansing and data matching on their own.