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To produce high-quality information products and perform accurate spatial analysis, your source data must be high quality and well maintained. Data Reviewer allows you to manage data for data production and analysis by providing a system for automating and simplifying data quality control that can improve data integrity.

Data Reviewer provides a set of quality control (QC) tools that allow an efficient and consistent data review process. This includes tools that support both automated and semiautomated data analysis to detect errors in a feature's integrity, attribution, or spatial relationships with other features. Detected errors are stored so you can review them to correct workflows and perform data quality reporting.

Automated data review

Automated data review evaluates a feature's quality without human intervention. Data Reviewer includes a library of configurable checks that allow you to validate data based on your quality requirements. Data Reviewer checks are designed to assess various aspects of a feature’s quality, including its attribution, integrity, or spatial relationship to other features. Data Reviewer automated checks are configurable and do not require specialized programming skills to implement. In many cases, GIS professionals with a good understanding of their data’s quality requirements can implement automated review with minimal training.

In attribute rule-based workflows, checks are configured and stored in the geodatabase to assess a feature's quality and its fitness for use. Automated review capabilities can be integrated in multiple ways:

  • Assessing a feature’s quality during creation or modification to prevent the introduction of features that do not meet quality requirements and to reduce the need for rework.
  • Assessing a feature’s quality after it is created. This can be useful when existing data is of unknown quality and a baseline assessment is needed to identify the effort required to achieve quality requirements.

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

Semiautomated data review

Not all errors in your data can be detected using automated methods. Semiautomated 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 assess quality in ways that automated data review cannot. This includes identifying missing, misplaced, or miscoded features and other issues that automated checks may not detect.

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

Error management

Data Reviewer allows management of errors from detection through correction and verification. These capabilities improve data quality by identifying the source, location, and cause of errors. Costs are reduced and duplicated work is avoided by providing insight into how the error was detected, who corrected it, and whether the correction has been verified as acceptable.

Errors detected during the data review process are tracked through a defined life cycle process. This process includes three phases: Review, Correction, and Verification.

Reviewer life cycle phases

Each phase contains one or more status values that describe the actions taken as the error progresses from one phase to another.

In attribute rule-based workflows, errors are stored in the geodatabase within a series of system-maintained tables. Errors are accessed using the Error Inspector pane, which provides tools for reporting, navigation, and selection of features that facilitate error correction.

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