Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to estimate linking errors in customer relationships in a database and to identify metrics that improve the linking accuracy. In an embodiment, a plurality of sample customer relationships are selected from a database to form a sample data set that is statistically representative of the database. Potential linking errors are then identified within the sample data set. The identified potential linking errors are then validated to identify actual linking errors in the sample data set. Once validated, the actual linking errors within the sample data set are used to estimate linking errors within the database. Further, the estimated linking errors in the database may be analyzed to identify one or more factors that contribute to the linking errors.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships.

Abstract: The disclosed methods, systems, and computer-program products allow a business to estimate linking errors in customer relationships in a database and to identify metrics that improve the linking accuracy. In an embodiment, a plurality of sample customer relationships are selected from a database to form a sample data set that is statistically representative of the database. Potential linking errors are then identified within the sample data set. The identified potential linking errors are then validated to identify actual linking errors in the sample data set. Once validated, the actual linking errors within the sample data set are used to estimate linking errors within the database. Further, the estimated linking errors in the database may be analyzed to identify one or more factors that contribute to the linking errors.