ArcGIS Data Reviewer | Data Quality Management

Your source data must be high quality and well maintained, whether you manage resources for a nation or facility assets for a local community. ArcGIS Data Reviewer is an extension for ArcGIS Pro, ArcMap, and ArcGIS Enterprise designed to improve data quality. It provides a complete system for automating and simplifying data quality management.

Trust your GIS data

Integrate tasks that support a comprehensive data quality management process to ensure your output, whether it's analysis, printed maps or web services, meets the defined level of quality.

Achieve consistent quality

Implement a consistent, repeatable data review process. Find, record, and centrally track anomalies discovered in your data. Share data quality rules across teams and other contributors.

Save resources

Eliminate the cost of custom codes or apps. Out-of-the-box configurable checks will validate your data automatically. Pre-schedule checks to free-up resources.

Increase transparency

Gain insight into sources of poor data quality. Identify error trends and monitor data health using reports, dashboards, and other automatically captured statistics.

How ArcGIS Data Reviewer works

Automate detection

Define best practices, industry, and specific requirements by configuring out-of-the-box data checks. Automatically identify issues such as feature integrity, attribute completeness, and spatial relationships. Execute checks interactively and as a recurring event.

Explore Data Reviewer checks

Improve visual review

Not all errors in your data can be detected using automated methods. Data Reviewer provides simple-to-use visual review tools. You will be able to identify missing or misplaced features, improperly attributed features, and other types of issues.

See simplified visual QC

Track & communicate data quality

Reduce risk and improve decision outcomes by using automated reports and web-based analytical dashboards to communicate data issues. Track, manage, and monitor the status of your data’s “health.” A managed lifecycle demonstrates effective data management.

See how to manage results

Share work

Enlist collaboration in the data quality management process across a broad community of stakeholders and subject matter experts. Work from desktop, mobile, and web applications.

Learn more about collaboration