Data quality rules provide the methods by which the results of data quality assessment can be employed for data validation. Yet there are different types of data quality rules that can be applied at different levels of precision in an assortment of platforms, products, and utilities. In many organizations, the absence of a formal framework for defining, managing, and deploying data quality rules allows for inconsistency in data validity despite a replicated effort in application.
In this tutorial, we provide a framework for defining data validation and data transformation rules that can be managed as content, shared across the enterprise, and implemented consistently. We will show how data quality rules are to be aligned with enterprise metadata, how rules can be deployed in different execution models, and how data quality services can help operationalize key facets of a data stewardship capability.
Attendees will learn about:
- The different classes of data quality rules
- Rule precision: applying rules to data elements, records, and tables
- Declarative models for data quality rules
- Building data quality services
- Rules and data quality reporting
- Using rules for root cause analysis