Data Reviewer includes a library of configurable out-of-the-box automated checks which enable data owners and managers to implement data validation processes based on business rules defined by their organization. These business rules may come from multiple sources including national and industry standards, project/program-specific quality-assurance plans, subject matter expertise, and the organization's own experience and training. Regardless of the source, Data Reviewer's automated checks can be configured to address the specific data quality requirements and provide a holistic evaluation of a database's fitness for use.
Data Reviewer provides over 40 different checks with which to validate different aspects of data quality, including attribution, spatial relationships, and item metadata content.
Additionally, Data Reviewer's automated validation capabilities can be extended through the use of geoprocessing models, Python scripts, or custom programming using the custom check to address situations when a business rule cannot be validated using one or more off-the-shelf checks. All of these methods enable data owners to address specific data quality requirements while continuing to leverage Data Reviewer's framework for managing errors.