Latest Contributions by JayCary

The ArcGIS Data Reviewer roadmap outlines the estimated timeline for planned features and enhancements in future releases. These timelines are subject to change as customer feedback and priorities evolve. Is there a feature missing or not prioritized that impacts your data quality management program? We encourage you to share your feedback in the Data Reviewer Ideas place in the Esri Community. Your suggestions directly influence future development. After you submit an idea, it is reviewed by the product team and considered for inclusion in upcoming releases, helping ensure that Data Reviewer continues to meet your needs. For more information on the latest release of ArcGIS Data Reviewer, refer to the What’s New in ArcGIS Data Reviewer blog. Near-term goals Near-term goals include new and enhanced features planned over the next couple of software releases. Typically, Data Reviewer releases twice a year with ArcGIS Pro and ArcGIS Enterprise. Near-term goals include the following: Near-term goals for Data Reviewer   Simplify data quality review To improve the user experience, ArcGIS Data Reviewer will introduce a series of enhancements that address common challenges in implementing automated quality control workflows. This includes minimizing errors during the process of sharing validation server-enabled web layers, particularly those containing attribute rules created using Data Reviewer checks. These enhancements will help detect missing layers and identify issues related to a federated server’s licensing and version.  Additionally, enhancements are planned that increase observability when troubleshooting the performance of validation attribute rules. Administrators will gain better tools and insights for diagnosing and resolving data validation issues, leading to more efficient and performant quality control processes. AI-powered error detection The ArcGIS Pro assistant brings AI-powered productivity directly into ArcGIS Pro, helping users transform everyday GIS tasks into intuitive, conversational experiences. With the ArcGIS Pro assistant 3.7 beta release, Data Reviewer capabilities introduce smarter automated data quality workflows, helping users predict validation checks, uncover data insights, and configure quality checks more efficiently. Initial support focuses on water and electric network datasets, with added support for indoor mapping. As these capabilities evolve, they will expand to more industries and be refined through customer feedback gathered during beta testing. Automated quality control enhancements Automated data quality checks are among the most widely used capabilities in Data Reviewer. Data Reviewer checks support multiple workflows, including attribute rules and the Run Data Checks command in ArcGIS Pro. These capabilities help organizations save time compared with more manual approaches to data quality control. Planned enhancements focus on making automated review more complete, flexible, and easier to apply across common GIS data management workflows. Expanded support for Z-enabled features. Continued work will improve automated review of Z-aware data. For example, surface transportation and pipeline customers need an enhanced Evaluate Intersection Count check to avoid false-positive results when identifying Z-enabled pipe or road centerline features that intersect other features. Improved connectivity validation. Customers managing indoor maps or road centerlines used in routing require enhancements to the Feature on Feature check to evaluate connectivity between linear features. For indoor mapping, this includes identifying transition features, such as stairs, that do not connect at both ends with pathway features, such as hallways. Expanded support for SQL in checks. Customers across multiple industries have requested updates to the Query Attributes check to support additional SQL operators and functions. Examples include using the Modulus function to identify mismatched address ranges on road centerlines and the Cast function to compare attribute values stored in different field types. New checks for duplicate and missing values. New automated review capabilities will support identifying duplicate attribute values, such as duplicated asset IDs in a water utility dataset, across multiple feature classes and tables in a geodatabase. A new check will also simplify the identification of features and rows that contain null, empty, or blank attribute values. Flexible error management Managing errors created during data quality review can be considerable given that each GIS feature could potentially have multiple error conditions associated with them. To address these challenges, planned enhancements focus on streamlining error management processes by offering greater flexibility and control. One key improvement is to provide tools that support the automated removal of error features that are no longer needed for data quality reporting. For example, organizations will be able to automate the removal of verified errors after a specified retention period (monthly, quarterly, etc.). For customers who do not require tracking of verified errors, a new option will opting out of Data Reviewer’s error lifecycle process. When enabled on the geodatabase, error features will be automatically deleted once the associated error condition is resolved, eliminating unnecessary steps and providing a more efficient workflow for users when their reporting needs are less complex. Some organizations require tracking errors and correction steps for regulatory compliance. Planned enhancements will increase traceability and reporting throughout the error management process. When enabled on the geodatabase, error features will no longer be automatically verified once the error condition has been resolved. Instead, users will manually manage each error’s status and document actions taken when correcting and verifying an error. Data quality reporting Data quality reporting plays a critical role in increasing transparency across an organization. By providing clear insights into the status and results of data quality checks, stakeholders can better understand where issues exist and how they are can be addressed. This visibility promotes accountability, enables informed decision-making, and promotes a culture of continuous improvement in data management practices. Planned data quality reporting features include enhancements to the existing data accuracy reporting tool to support branch-versioned geodatabases, and data quality summarization and trend reporting using dashboards. Support on Kubernetes With the recent certification of Data Reviewer for ArcGIS Enterprise on Linux, the team will continue efforts to address new and emerging customer deployment patterns. This includes enabling support for Data Reviewer-based data quality management capabilities in ArcGIS Enterprise on Kubernetes. Mid-term goals Mid-term goals include those features and capabilities planned for three to four releases in the future (typically 18–24 months in the future). These include support for the automated creation of Reviewer-based constraint and validation attribute rules, integration of Data Reviewer checks into automated workflows using Python, the ability to integrate data quality feedback into web applications, and support for data quality review in ArcGIS Online.  Mid-term goals for Data Reviewer   Long-term goals Long-term goals are items on the roadmap that have not yet been assigned to a specific release. They are intentionally open-ended to invite customer feedback and discussion. These goals include support for automated review of 3D features stored as either multipatch or 3D object features, the ability to store multipoint error features in validation attribute rules, support for automated review of metadata content, and AI skills that support error correction in common data editing applications. Long-term goals for Data Reviewer   ... View more