ArcGIS Data Reviewer

Data Quality Matters

To perform accurate spatial analysis and create high-quality information products, your source data should meet a defined level of quality. Data of an unknown quality reduces the value of your work and may negatively impact decision-making or other business operations that rely on accurate analysis results.

ArcGIS Data Reviewer, an extension to ArcGIS, reduces this risk by implementing workflows that highlight data that does not meet your quality requirements, leading to increased confidence in your data, enhanced productivity, and reduced costs. It reduces data management costs by providing a unified set of capabilities that supports detection, management and reporting of errors in your data. It enhances productivity by automating the detection of common errors in GIS data and simplified workflows for identifying poor-quality data that cannot be detected in an automated manner. By identifying and addressing errors that impact decision-making overall confidence in the information products and services is increased.

We have so much more confidence in our data with ArcGIS Data Reviewer. We can walk into a room and feel good about having a conversation with someone because we know exactly where our data stands and what we need to do to fix it.
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Terri Bunting, Tucson Water Department

Implement automated and semiautomated workflows that improve data quality.
Repeatable workflows that detect and report poor quality data

Automate the detection of poor-quality data

Automated data review is a capability that evaluates a feature’s quality without human intervention. For resource-constrained teams, automation enables you to implement data quality assessment with minimal impact to staffing while improving data quality. For large organizations, automation enables consistency in data production across multiple teams and increases knowledge management by integrating quality requirements into repeatable workflows.

"We're in a position now where we can run mass checks against our entire system during the workday. By implementing ArcGIS Data Reviewer, we went from taking hours to find and fix errors to minutes."
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Daniel Johns, Clay County Utility Authority

Data Reviewer provides a library of checks to validate data based on your unique quality requirements. The checks are designed to assess various aspects of a feature’s quality, including its attribution, integrity, or spatial relationship to other features. For example, utilities need to maintain accurate positions of their assets because incorrect information can affect their completion of construction or repair projects and outage notifications to customers.

Reviewer’s automated checks are a configurable/no-code capability that does not require specialized programming skills to implement. In many cases, GIS professionals with a good understanding of their organization’s quality requirements can implement automated review workflows using Data Reviewer with minimal training.

Data Reviewer checks poster illustrating checks that automate data review
Use the ArcGIS Data Reviewer Checks poster as a guide to automate data review. Click the image above to see the full poster.

Automated review can be integrated in multiple ways:

Learn more about automating data review using these resources:

Visual review identifies errors in spatial accuracy and completeness.
Harness the input of data subject matter experts to improve your quality control workflows

Implement a comprehensive quality review

Not all errors in your data can be detected using automated methods. Semiautomated data review assesses data quality using methods that typically involve guided workflows requiring human interaction and input. Visual review is the most common form of semiautomated review and is used to evaluate quality in ways that automated methods may not support. This includes finding missing, misplaced, or miscoded features and other issues.

These types of errors are routinely found by subject matter experts and other data consumers who leverage GIS data in their daily work. By leveraging Data Reviewer’s workflows to harness this feedback you can further improve the quality of data you produce and share.

Learn more about integrating feedback from subject matter experts and other data consumers using these resources:

Track and report progress on data quality goals

Data Reviewer enables the management and tracking of errors through a defined lifecycle. This workflow includes the tracking of errors detected using automated checks, as well as, those reported by data consumers. Information collected during data review includes the source and description of the error, its location, who found the error and its severity.

This information can be useful to multiple communities who manage and use GIS data. For data editors, this information helps them get their work done more quickly and avoids duplication of effort when multiple teams are working to improve quality. For data consumers, this information can help in identifying risks that impact decisions that depend on high-quality data.

Data Reviewer also tracks the details of who, when and how errors are corrected and whether the correction has been verified as acceptable. This additional information enables you to report progress on data quality goals and forecast when quality goals will be achieved.

Errors detected during the data review process are tracked through a defined lifecycle that consists of three phases (Review, Correction, and Verification).

Data Reviewer three-phase error life cycle (Review, Correct, Verify)
Track and report the progress of your data quality journey

Learn more about error management workflows using these resources:

If you have questions or would like to share your experience in implementing Data Reviewer, please visit the ArcGIS Data Reviewer place in the Esri Community.

About the author

Jay Cary is a Product Manager at Esri. He has over fifteen years of GIS management experience in local and federal government sectors, and has been with Esri for thirteen years, where he focuses on spatial data management.

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