Available with Data Reviewer license.
To produce high-quality information products and perform accurate spatial analysis, your source data must be of high quality and well maintained. Data Reviewer enables management of data in support of data production and analysis by providing a complete system for automating and simplifying data quality control that can quickly improve data integrity.
Data Reviewer provides a comprehensive set of quality control (QC) tools that enable an efficient and consistent data review process. This includes tools that support both automated and semiautomated analysis of data to detect errors in a feature's integrity, attribution, or spatial relationships with other features. Errors detected during analysis are stored to facilitate corrective workflows and data quality reporting.
Automated data review
Data Reviewer includes a library of configurable checks that enable organizations to automate data validation based on their own quality requirements. These quality requirements may come from multiple sources, including national and industry standards, project- or program-specific quality assurance plans, subject matter experts, and the organization's own experience and training. Regardless of the source, automated checks can be configured in Data Reviewer to address the specific data quality requirements and provide a holistic evaluation of a database's fitness for use.
To learn more about using Data Reviewer to automate data validation, see the following topics:
Semiautomated data review
Not all errors in your data can be detected using automated methods. Semiautomated review is the process of assessing data quality using methods that typically involve guided workflows requiring some human interaction and input. Visual review is predominantly the most common form of semiautomated review and is used to assess quality in ways that automated data review cannot. This includes the discovery of missing, misplaced, or miscoded features and other issues that automated checks may not detect.
In ArcGIS Pro, Data Reviewer provides tools that facilitate visual data review. There are tools that help in selecting and browsing features, committing existing features as errors, and flagging missing features.
To learn more about using Data Reviewer to implement semiautomated workflows for assessing data quality, see the following topics:
Data Reviewer enables comprehensive management of error results from detection through correction and verification. These capabilities increase efficiencies in improving data quality by identifying the source, location, and cause of the errors. Costs are reduced and duplicative work is avoided by providing insight into how the error was detected, who corrected it, and whether the correction has been verified as acceptable.
To learn more about Data Reviewer error management workflows, see the following topics:
Data quality reporting
Data Reviewer enables both summary and detailed reporting of data quality results. These reports can be used to communicate the source, quantity, severity, and location of noncompliant features detected in your data. Noncompliant features include those detected using Data Reviewer automated checks or feedback provided by data consumers in the form of markups.
By communicating data quality, you can alert stakeholders and other interested parties when data does not meet agreed-upon standards and provide a reporting method for tracking data compliance through time. Reporting capabilities can be integrated as a component of an organization's overall business performance management system.
To learn more about using Data Reviewer to report the quality of your data, see the following topics: