To produce high-quality information products and perform accurate spatial analysis, your source data must be of high quality and well maintained. ArcGIS Data Reviewer enables management of data in support of data production and analysis by providing a complete system for automating and simplifying data quality control that can quickly improve data integrity.
Data Reviewer provides a comprehensive set of quality control (QC) tools that enable an efficient and consistent data review process. This includes tools that support both automated and semiautomated analysis of data to detect errors in a feature's integrity, attribution, or spatial relationships with other features. Errors detected during analysis are stored to facilitate corrective workflows and data quality reporting.
Automated data review
Data Reviewer services enable clients to implement automated data validation using automated checks configured using ArcGIS Data Reviewer Desktop. These services leverage ArcGIS Server to offload time-consuming data validations from ArcGIS Desktop clients to an organization's intranet or cloud-hosted 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 web-based data-editing workflow.
To learn more about using Data Reviewer to automate data validation, see the following topics:
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 typically involve guided workflows requiring some human interaction and input. Visual review is predominantly 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 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 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, 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:
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. 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, see the following topics: