What does Data Reviewer add to Attribute Rules?

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05-21-2019 05:01 AM
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New Contributor II

We have been using the Data Reviewer extension to create and evaluate validation rules for our editing our features.

Create validation rules as Attribute Rules in the geodatabase seems to do largely the same.

What is the added value of the Data Reviewer extension, or in other words, can we just save the license and go with attribute rules?

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Esri Contributor

The value add depends on how much your organization can invest in creating (and maintaining) custom code to replace data quality workflows currently implemented using Data Reviewer’s configurable tools.

Starting with the 2.3 release, Data Reviewer’s automated checks will be migrated to support Attribute rule constraint and validation workflows. Check migration will happen over a series of releases due to the number of checks to be migrated and are prioritized based on customer frequency of use. (Note: You can influence this prioritization by participating in our customer survey!)

Here are some items to consider:

  • Implementation and maintenance costs
    Implementation of a Data Reviewer-based Attribute rule does not require specialized programming skills and can often be done by a subject matter expert with a good understanding of the organization’s data quality requirements. This results in lower implementation and maintenance costs when compared to custom code.

    (Actually, there are whole host of things to consider when implementing a system based on customized commercial off-the-shelf software (COTS). For reference, here is an Esri white paper (slightly dated) that outlines patterns in COTS-based deployments.)

  • Functional equivalency
    Does Arcade support the data validation requirements that are currently implement with Data Reviewer checks? Depending on the Data Reviewer checks that are used in your system this may not be an issue.

  • Operational efficiency
    Support for Data Reviewer-based validation workflows is planned for the Q1 2020 release. In testing, Data Reviewer’s validation engine significantly outperforms equivalent Arcade-based validations. This improves operational efficiency that results in lower operating costs.

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Esri Notable Contributor
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Esri Contributor

The value add depends on how much your organization can invest in creating (and maintaining) custom code to replace data quality workflows currently implemented using Data Reviewer’s configurable tools.

Starting with the 2.3 release, Data Reviewer’s automated checks will be migrated to support Attribute rule constraint and validation workflows. Check migration will happen over a series of releases due to the number of checks to be migrated and are prioritized based on customer frequency of use. (Note: You can influence this prioritization by participating in our customer survey!)

Here are some items to consider:

  • Implementation and maintenance costs
    Implementation of a Data Reviewer-based Attribute rule does not require specialized programming skills and can often be done by a subject matter expert with a good understanding of the organization’s data quality requirements. This results in lower implementation and maintenance costs when compared to custom code.

    (Actually, there are whole host of things to consider when implementing a system based on customized commercial off-the-shelf software (COTS). For reference, here is an Esri white paper (slightly dated) that outlines patterns in COTS-based deployments.)

  • Functional equivalency
    Does Arcade support the data validation requirements that are currently implement with Data Reviewer checks? Depending on the Data Reviewer checks that are used in your system this may not be an issue.

  • Operational efficiency
    Support for Data Reviewer-based validation workflows is planned for the Q1 2020 release. In testing, Data Reviewer’s validation engine significantly outperforms equivalent Arcade-based validations. This improves operational efficiency that results in lower operating costs.

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New Contributor II

The Reviewer extension covers the full spectrum of QA/QC without having to write custom code. QA/QC requires more than automated validation. Attribute rules do not solve the need for visual review or error life cycle management, the Reviewer extension supports automated validation and all other QA/QC operations with an integrated and seamless process. Attribute Rules can cover automated validation to some extent, and at the cost of knowledge in writing and maintaining arcade scripts.

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New Contributor II

Thanks for the answers, I now have a feel for what the products are meant for.

We have been using Data Reviewer as data validator in a web app, with both checks on a certain version and nightly validation for the entire dataset. I am thinking for this use case, we might as well use attribute rules, which actually also provide visual review layers in the feature service.  

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