ArcGIS Data Reviewer SOE capabilities

Remarque :

ArcGIS Enterprise 10.9.x, part of the ArcGIS 2021 releases, are the last ArcGIS Enterprise releases that support services published from ArcMap. You are encouraged to migrate automated review workflows to ArcGIS Data Reviewer capabilities available in ArcGIS Pro attribute rules.

The Data Reviewer server extension provides a comprehensive set of quality control (QC) capabilities, with the ArcMap runtime-based server object extension, that enables an efficient and consistent data review process. The following Data Reviewer server extension workflows are supported with the ArcMap runtime.

Automated data review

Data Reviewer-enabled services allow clients to implement automated data review using checks. These services leverage ArcGIS Server to carry out automated review using an organization's on premise or cloud-hosted infrastructure.

In a production environment, services-based data validation can be scheduled on a nightly basis to assess data created or modified during regular business hours. Alternatively, automated review can be triggered on an as-needed basis to support ad hoc assessment of data quality as a component of a data management workflow.

To learn more about using Data Reviewer to automate data review, refer to the following topics:

• Checks in Data Reviewer
• Batch jobs and Data Reviewer
• Deploy data quality services (tutorial)
• Working with Data Reviewer (JavaScript API)
• Data Reviewer—Execute Ad Hoc Batch Validation (JavaScript API)
• Data Reviewer—Scheduled Batch Validation (JavaScript API)
• Batch Validation (REST API)

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 include human interaction and input.

Visual review is 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.

Data Reviewer services support these workflows by enabling client applications to create error results using geometry and attributes from existing or temporary web features. For example, you can enlist users of your web apps to help identify data errors using a simple Report Error workflow. The feedback is stored as an error result, where it is reviewed and either rejected or allowed to pass on to technicians for correction as any other error identified by Data Reviewer would be. The geodatabase serves as a centralized place for managing errors detected using automated checks and errors detected manually by data consumers.

To learn more about using Data Reviewer to implement semiautomated workflows for assessing data quality, refer to the following topics:

• Managing quality feedback (tutorial)
• Working with Data Reviewer (JavaScript API)
• Data Reviewer—Write Reviewer Results (JavaScript API)
• Write Feature as Result (JavaScript API)
• Write Result (REST API)

Results management

Data Reviewer enables comprehensive management of 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 eliminated by providing insight into the status and how it was detected, who corrected it, and whether the correction has been verified as acceptable.

To learn more about using Data Reviewer for error life cycle management workflows, refer to the following topics:

• Result management of the quality review process
• Working with Data Reviewer (JavaScript API)
• Data Reviewer—Update Result Lifecycle Status (JavaScript API)
• Update Lifecycle Status (REST API)

Data quality reporting

Data Reviewer-enabled services provide both summary and detailed reporting of data quality results. These services are 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 or as a stand-alone dashboard for reporting data quality.

To learn more about using Data Reviewer to report the quality of your data, refer to the following topics:

• Report data quality (tutorial)
• Working with Data Reviewer (JavaScript API)
• Data Reviewer—Dashboard Results (JavaScript API)
• Data Reviewer—Dashboard Results with Filter (JavaScript API)
• Dashboard (REST API)