Many of the data-quality workflows you implement using ArcGIS Data Reviewer for Desktop can be made available as web services and accessed through web or mobile client applications using ArcGIS Data Reviewer for Server. For example, you can publish web services that support automated validation of data workflows for identifying missing or misplaced features, and data-quality reporting and visualization.
With ArcGIS Data Reviewer for Server, you can do the following:
- Schedule and execute recurring data validations to automatically identify data which does not meet data quality standards.
- Execute pre-configured data validations from web or mobile applications.
- Extend data quality tasks to non-GIS users using simple web-based data mark-up tools.
- Manage and report the status of error results through a defined life cycle.
- Integrate data quality reporting with other business performance metrics to communicate data-quality-related impacts to stakeholders and other interested parties.
Working with Data Reviewer on the web
Data Reviewer services enable you to extend data-quality workflows to clients on the web. These include capabilities that support semi-automated and automated validation of data, data-quality reporting, and visualizing reviewer results.
Automated Data Review
Data Reviewer validation services enable clients to execute automated data validation based on business rules implemented using Data Reviewer batch jobs. These services leverage ArcGIS Server to offload time-consuming data validations from ArcGIS Desktop clients to an organization's intranet or cloud-hosted server infrastructure. In a production environment, server-based data validation can be scheduled on a nightly basis to validate data created or modified during regular business hours. Alternatively, automated data validation can be triggered on an as-needed basis to support ad hoc validation of data as a component of a larger web-based, data-editing workflow.
To learn more about using Data Reviewer to automate data validation, see the following topics.
Semi-Automated Data Review
Not all errors in your data can be detected using automated methods. Semi-automated 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 semi-automated review and is used to assess quality in ways that automated 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 Reviewer results using geometry and attributes from existing or temporary web features. For example, you can enlist users of your web applications to help identify data errors using a simple "Report Error" workflow. The feedback is logged to the Reviewer workspace where it is reviewed and either rejected or allowed to pass on to technicians for correction as would any other Data Reviewer-identified error. The Reviewer workspace serves as a centralized place for managing errors—those errors detected using automated checks—and those detected manually, by data consumers.
To learn more about using ArcGIS Data Reviewer to implement semi-automated workflows for assessing data quality, see the following topic.
Data Quality Reporting
Data Reviewer services enable both summary and detailed reporting of data-quality results. These services can be used to communicate the source, quantity, severity, and location of noncompliant features detected in your data. Non-compliant features include those detected using Data Reviewer automated checks or feedback provided by data consumers in the form of mark-ups.
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, see the following topics:
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 Data Reviewer error life cycle management workflows, see the following topics: