Terminology used in data validation
Automated review
Automated review is a data quality review process that evaluates data without manual intervention using configurable ArcGIS Data Reviewer checks. This form of review uses Data Reviewer validation attribute rules or Run Data Checks tools to automatically perform review.
Data constraint
Data constraints are automated methods that detect features, attributes, and relationships during data editing workflows that don't meet established quality requirements. These methods include Data Reviewer automated checks implemented in constraint attribute rule workflows, domains or subtypes, and contingent values.
Data quality management
Data quality management standardizes processes and improves workflow efficiency to support the delivery of products and services.
Data Reviewer checks
Data Reviewer 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 Reviewer rules
Data 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. Rules are created using ArcGIS Pro and are stored in the geodatabase that contains the features to be validated.
Data validation
Data validation uses formal methods to identify features, attributes, and relationships in a database that do not meet established quality requirements. These methods include automated capabilities, such as Data Reviewer validation attribute rules and Run Data Checks tools, as well as semiautomated capabilities for visual review, such as the Browse Features and Flag Missing Features tools.
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. The error result's life cycle includes three cycles: review, correction, and verification. Status information identifies how an error record was reviewed, corrected, or verified, as well as, who updated the error record to the new cycle and when.
Error result
An error result is a feature or row record that identifies deviations in the accuracy or correctness of a feature or table row. The record contains information about the data source, error condition, severity, and life cycle and status information. Data Reviewer checks and visual review tools create error results and store them in geodatabase system tables (attribute rules).
Reviewer batch jobs
Legacy:
Data quality workflows and related tools based on the Reviewer workspace have been deprecated and are no longer supported. It is recommended that you migrate to ArcGIS Data Reviewer-based validation attribute rules.
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, which are no longer supported in ArcGIS Pro. To use them in ArcGIS Pro, use the Export Batch Job to Attribute Rules tool to convert batch job files into attribute rules.
Sampling
Sampling is a method for selecting features or table rows to verify a dataset's quality. In ArcGIS Pro, use the Select Random Sample geoprocessing tool to create a random data sample. Once a data sample is generated, use the Run Data Checks tool or the Browse Feature tool to identify errors within the sample.
Semiautomated review
Semiautomated review is a data quality review process that involves guided human interaction to find errors. This type of review includes tools such as Browse Features and Flag Missing Features.