Using inheritance in a metadata based approach to data quality assessment

Abstract

Currently available data quality tools provide development environments that significantly decrease the effort in dealing with common data problems, such as those related with attribute domain validation, syntax checking, or value matching against a reference master data repository. On the contrary, more complex and specific data quality functionalities, whose requirements usually derive from application domain business rules, have to be developed from scratch, usually leading to high costs of development and maintenance. This paper introduces the concept of inheritance in a metadata-driven approach to simplified data quality rule management. The approach is based on the belief that even complex data quality rules very often adhere to recurring patterns that can be encoded and encapsulated as reusable, abstract templates. The approach is supported by a metamodel developed on top of OMG's Common Warehouse Metamodel, herein extended with the ability to derive new rule patterns from existing ones, through inheritance. The inheritance metamodel is presented in UML and its application is illustrated with a running example.