ArcGIS Data Reviewer Roadmap (January 2025)
ArcGIS Data Reviewer provides capabilities that enable you to automate and simplify data quality workflows that lower data management costs and create high quality data. The purpose of the ArcGIS Data Reviewer Roadmap is to provide an estimate of when new capabilities will be released in the future.
By following this roadmap, we will be continuously improving the capabilities of ArcGIS Data Reviewer, meeting the evolving needs of users and providing a powerful and efficient solution for managing data quality.
What would you like to see next in ArcGIS Data Reviewer? Feel free to share your thoughts in the ArcGIS Data Reviewer Ideas board.
Near-term goals
Near-term goals include those capabilities planned for the next couple of releases of ArcGIS Pro and ArcGIS Enterprise. These include expanding Data Reviewer’s data quality control capabilities available in attribute rule-based workflows and support for exploratory/ad hoc assessment of data quality in ArcGIS Pro.
The ability to automate the detection of poor-quality features in your GIS is a major component of every Data Reviewer release. Near-term goals include expanding and simplifying data quality control capabilities within attribute rule workflows.
This includes the ability to identify incorrect spatial relationships between line and point features in your geodatabase. The Valency check enables you to evaluate a line’s spatial relationship (and optionally attribute relationship) to other lines or points at its end points (from-node and/or to-node). Some use-cases from water utilities include the following:
- Identifying reducer fittings that are not connected to two pipes
- Identifying reducer fittings that are connected to two pipes with different diameters
- Identifying two adjacent lines with different sizes that are not connected to a reducer fitting
- Identifying two adjacent sewage lines that flow into the same manhole
Additionally, the support for Z-enabled features in Data Reviewer checks is being expanded with an update to the commonly used Duplicate Feature check. This update will enable you to include a feature’s Z-values when determining whether it is a duplicate of another feature. In water utilities this enhancement would reduce false-positive errors when identifying stacked point features (such as valves) that have matching x/y coordinates but different Z values.
To support data migration workflows, there are plans for the Event on Event check to join other linear-referenced event checks (Find Event Gaps, Find Event Overlaps, Find Orphan Events, and Invalid Event Measures) in supporting evaluation of features stored in file geodatabases. The Event on Event check identifies linear referenced events that overlay other events based on a defined relationship. For example, in roadway data management workflows this check helps to identify speed limits that are incompatible with a road’s functional class designation.
Another key focus area in the near term will be the ongoing support for semiautomated data review in attribute rule-based workflows. Semiautomated review evaluate data quality through methods that typically involve human interaction and input. This includes improving visual review workflows by integrating random sampling, providing support in web applications created using the ArcGIS Experience Builder, and for managing errors originating from third-party tools and workflows.
Another near-term goal is to improve the management of errors resulting from both automated and semiautomated data review processes. The primary objectives include simplifying error management tasks by automating the retention of corrected/verified error records and improving the data correction workflows by using the Error Inspector in ArcGIS Pro. Additionally, within ArcGIS Enterprise, the Validation Service will be enhanced to support the life cycle management of error results generated during semiautomated data reviews.
Finally, users have expressed a need to evaluate data quality outside the use of attribute rule-based workflows. Common use-cases for this type of data review involve exploratory assessment of data from external sources, data stored in various formats, or evaluating features within automated workflows created using Geoprocessing tools or the ArcGIS API for Python.
Mid-term goals
Mid-term goals include capabilities planned for subsequent releases.
These include:
- Automated reporting to provide transparency into data quality to data consumers
- Automation for generating constraint/validation attribute rules
- Expanded support for evaluating the quality of Z-enabled features
In ArcGIS Pro, enhancements to the Error Inspector include enabling the export of error features to Excel and expanding attribute filtering to include additional error properties, such as error title and source.
Long-term goals
Long-term goals are those features in the roadmap that have not been assigned to a specific release. These include support for automated review for ArcGIS Online hosted features services, automated review for 3D object features, and error correction workflows.
Summary
As with any roadmap, the listed capabilities are subject to change, evolve, delays, or removal based on shifting priorities. The continued development or release is at the sole discretion of Esri.
For more information on the latest release of ArcGIS Data Reviewer refer to the What’s New in ArcGIS Data Reviewer blog.
