Terminology used in data validation

Data checks

Data checks automate the validation of a specific condition, based on its configuration, against one or more features. Checks assess different aspects of a feature’s quality, including spatial accuracy, thematic accuracy, completeness, and logical consistency.

Data constraint

Automated methods that detect features, attributes, and relationships during data editing workflows that don't meet established quality requirements. These methods include Data Reviewer's automated checks implemented in attribute (constraint) rule workflows, domains or subtypes, and contingent values.

Data quality management

Data quality management provides tools to assist in the successful delivery of products and services by standardizing processes and increasing workflow efficiencies.

Data validation

The process of using formal methods to detect existing features, attributes, and relationships in a database that do not meet established quality requirements. These methods include automated capabilities (such as Data Reviewer's automated checks) and semiautomated capabilities that facilitate visual review (such as the Browse Features tool).

Error results and their life cycle

Error results and their life cycle describe an error result's state in the quality assurance (QA) or quality control (QC) process. There are three cycles in an error result's life cycle: review, correction, and verification. Status information provides updates on how a record has been reviewed, corrected, or verified, and includes who changed the error to a new cycle and when.

Error result

A feature or row record that identifies deviations in accuracy or correctness of a feature or table row in data. The record contains information that identifies the data source, error condition and severity, and life cycle and status information. Error results are created using data checks or inspection tools and are stored in a geodatabase using either the Reviewer workspace schema or geodatabase system tables (Attribute rules).

Reviewer batch jobs

Reviewer batch jobs are containers for configured ArcGIS Data Reviewer data checks. They can include checks that validate spatial relationships, attribute consistency, and feature integrity metadata content. Batch jobs are created in ArcMap and stored as .rbj files that can be added to a project. You can use a batch job to validate your data using the Execute Reviewer Batch Job tool.

Reviewer rules

Reviewer rules are preconfigured checks that validate aspects of a feature's quality. These include checks that validate spatial relationships, attribute consistency, and feature integrity. Reviewer rules are created using ArcGIS Pro and are stored in the geodatabase that contains the features to be validated.

Reviewer session

Error results discovered during automated validation or visual inspection are organized within a session. Sessions define a series of validation and quality control transactions performed by data checks or manual review. Sessions are stored in a Reviewer workspace and are an item in ArcGIS Pro projects to facilitate data correction tasks. Sessions are created and deleted using the Create Reviewer Session and Delete Reviewer Session tools.

Reviewer workspace

A Reviewer workspace is a geodatabase that contains the tables and feature classes needed to store error results created using Data Reviewer tools during the data validation process. You can enable a geodatabase to store error results by using the Enable Data Reviewer tool.

Sampling result

A feature or table row that has been selected as a member of a sampled population. In Data Reviewer, feature sampling is implemented using the Sampling check.