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Reviewer rules enable you to detect features that do not comply with established data quality requirements defined by your organization. This includes the detection of errors that affect a feature's attribution, geometric integrity, or relationship with other features. These rules can be used to assess a feature's quality during different phases of the data production workflow.
Automated validation capabilities
The first step when designing a Reviewer rule is to understand the capabilities of automated checks in Data Reviewer. The following are examples of check types that are used to assess different aspects of a feature's quality.
- Spatial relationship checks—Analyze the spatial relationships between features. You can analyze whether features overlap, intersect, touch, or are within a specified distance of each other. For example, you may want to verify that a road does not cross into the ocean or that a fire hydrant is connected to a water lateral.
- Attribute checks—Analyze the attribute values of features and tables. This can be simple field validation, such as a geodatabase domain, or have more complex attribute dependencies. For many features, one attribute is dependent on another attribute of the same feature. For example, if a road is under construction, it may not be accessible. An attribute check can be configured to monitor the status and accessibility of the roads.
- Feature integrity checks—Analyze the properties of features. Not all features in a database follow the same capture criteria. For example, you may have collection rules that define how close two vertices can be or whether multipart features are allowed in your data. Feature integrity checks ensure that collection rules are followed for each feature class.
For more information, see ArcGIS Data Reviewer checks.
Identification of Data Reviewer checks
The identification of automated Reviewer checks is often a task performed by a subject matter expert with deep knowledge of the data model and quality requirements. A subject matter expert can quickly identify a variety of issues, such as feature integrity, attribute completeness, and spatial relationships.
Using a requirements traceability matrix, the subject matter expert can associate Data Reviewer checks with data quality requirements. For more information, see Identify data quality requirements.
The following table is an example of a populated requirements traceability matrix that references some of the automated validation capabilities described above.
This list can provide organizations with a quick reference for identifying a specific capability in a product and its use when collecting requirements.
| ID | Requirement | Requirement number | Requirement category | Software | Product capability | Data Reviewer check |
|---|---|---|---|---|---|---|
5 | Ability to ensure that source data will be migrated into the production database and have appropriate domains and relationships | D002 | Data Requirement—Logical consistency | Data Reviewer | Domain check | Yes |
Relationship check | ||||||
Subtype check | ||||||
7 | Ability to ensure that production data is for mobile collectors and is attribute accurate | D004 | Data Requirement—Thematic accuracy | Data Reviewer | Regular Expression check | Yes |
Table to Table Attribute check | ||||||
8 | Ability to ensure that there is no overlap between event measures during the project period of 2010–2020 | D005 | Data Requirement—Temporal quality | Data Reviewer | Invalid Events check | Yes |
10 | Ability to identify the number of cells that are not populated (NULL) for each required attribute field | D006 | Data Requirement—Thematic accuracy | Data Reviewer | Query Attributes check | Yes |
11 | Ability to identify parcels that have no overlaying building footprint features | D007 | Data Requirement—Logical consistency | Data Reviewer | Feature on Feature check | Yes |
13 | Ability to validate a unique ID attribute linking a parcel to matching building footprint features | D008 | Data Requirement—Logical consistency | Data Reviewer | Feature on Feature check | Yes |
Quality control in workflows
Reviewer rules enable you to assess quality across multiple phases in the data life cycle. This includes assessment of a feature during creation, maintenance, updates, sharing, and archiving or deletion. Further, data currently in use within the organization can be assessed to identify compliance with changing data quality requirements that emerge in response to new business processes.
Detect errors in existing data
Automated checks can be used to assess the overall quality of your data based on your organization's unique quality requirements. This can include the validation of all features in a dataset to establish a baseline understanding of the data's fitness for use, as well as validation of a subset of features as a step in a workflow. Errors detected using this form of validation are stored in your geodatabase to support corrective workflows and quality reporting.
For more information, see Create Reviewer rules in a map.
Prevent errors in editing workflows
Automated checks can also be implemented to assess quality when creating or modifying data in your geodatabase. This form of validation serves to enforce data integrity like other forms of geodatabase constraints, such as domains and subtypes. Edits that result in data that does not comply with the organization's data quality requirements are rejected and will not be saved.
For more information, see Create Reviewer rules in a geodatabase.