Getting started with data validation and analysis

ArcGIS Data Reviewer is an ArcGIS extension that contains several tools for data validation and analysis. Checks allow you to perform various types of analysis on the data in a geodatabase, which includes evaluating feature extents and spatial relationships between features. They can be grouped together using batch jobs so you can check for multiple conditions all at once.

However, before you start validation and analysis tasks, there is some setup work involved. Below are some tips and guidance for setting up Data Reviewer for your quality assurance/quality control process.

Before data validation begins

Checks and batch jobs are the tools used to perform data validation with Data Reviewer. However, before you begin to configure checks and create batch jobs, it is recommended that you understand what types of conditions you want to find in the data. Examples of these conditions include the following:

These types of conditions can also be referred to as business rules for your data. Business rules can come from industry standards or product specifications, subject matter experts, or standard operating procedures. Checks can be configured to validate any of these conditions. You may also require multiple checks to search for all the rules you want to validate within a single feature class or table.

Learn more about the checks available with Data Reviewer

If you are working with a large extent or plan to do visual quality control, you can create a polygon grid to divide the data into smaller, more manageable sections. These smaller sections can be used to systematically review the large extent and track the visual review process.

Check and batch job configuration

Most of the Data Reviewer checks run on tables or point, line, or polygon feature classes, but there are some that have special requirements. Below are the types of checks and individual checks that have specific requirements.

Learn more about running data checks

Organizing checks in batch jobs

When you are configuring checks to add to batch jobs, you can choose to organize them in several different ways. Generally, you can have one or more groups with checks within them. Several scenarios are presented below.

Organizing Reviewer sessions

Depending on your organization, it may be useful to have several Reviewer sessions set up for data validation and analysis. At a minimum, you can choose to have separate sessions for automated and visual quality control. This allows you to separate manual quality control results from batch job results, which may be necessary for reporting data quality. Typically, automated validation must pass 100 percent, while manual visual quality control does not. By separating the results into different sessions, you can use the reports available in Data Reviewer to determine the level of quality on your data.

Learn more about the reports available with Data Reviewer

Thèmes connexes

4/26/2014