Data Reviewer Survey Blog - We asked, You answered!
Over the last few months, the ArcGIS Data Reviewer team requested feedback on various components of the extension. We would like to thank all our customers for providing valuable information to continue improving ArcGIS Data Reviewer! Here’s the breakdown of the results.
Supported Industries
When asked about industries you support when performing quality control tasks, a lot of you work with water datasets. A sizable portion work with Local/ State/ Provincial Government departments like Land Records. Others include electric, gas and telecom, National Government departments such as Military, Sciences, and Public Safety – Fire, Law Enforcement etc.
Types of Data Validated
The feedback we received for the question, “What types of data do you perform quality control tasks?” indicate that most of you derive and work on data in File Geodatabases. Almost half use Microsoft SQL Server while there are emerging groups that leverage Shapefile and PostgreSQL for storing data users.
Use of Data Reviewer
Probably the most important feedback was for this question.
The feedback collected indicated that a majority of you use Data Reviewer on a regular basis either daily or weekly.
Use of Automated Checks
It is important to highlight that respondents reinforced the popularity of Data Reviewer’s automated checks, as evidenced by this graph extracted from your responses.
The following graph shows how much our customers value the Data Reviewer capabilities that enable them to evaluate data based on specific conditions, search for conditions, such as polygon slivers or cutbacks, and also search for features that have spatial relationships with each other. For example, buildings that have been mistakenly placed on water features.
Most Frequently used Automated Checks
The next feedback pertains to ranking the automated checks. We have our top 7 most important checks!
Let’s go a bit deeper into what each check does, that resonated so well with our users:
1) Query Attributes (aka Execute SQL) --> Finds records based on a WHERE clause run against row attributes.
2) Invalid Geometry --> Finds features that contain invalid geometry. This includes features that contain null or empty geometries, empty envelopes, and may optionally include geometries that are not simple.
3) Domain --> Finds features that contain attribute values that do not comply with domain constraints. This includes numeric values that are greater than or less than those specified in a range domain and attribute values that are not found in a coded value domain.
4) Feature on Feature --> Finds features from the same or two different feature layers that share a spatial relationship and returns them as results.
5) Relationships --> Finds features and rows that are orphans or have improper cardinality in a relationship class.
6) Subtypes --> Finds features with improper or null subtype values.
7) Topology Rules --> Finds features that violate topology rules defined in the database.
Error Management and Reporting
Finally, when asked about the built-in Error Management and Reporting tools usage, more than half of you indicated you do not use these tools.
