# Viewing Data Quality Rules In OvalEdge, Author users can access data quality rules through the Data Quality Rules landing page.

Field	Description
Rule Name	It displays the names of the rules and helps users efficiently manage their rules: Search: Locate specific rules using the provided search bar. Sorting: Organize the list alphabetically for easier browsing. Summary Tab: Clicking a rule name opens its corresponding summary tab with detailed information.
Purpose	It explains the purpose of creating the Data Quality Rules by describing each rule's reasons and intentions.
Rule Creation Type	OvalEdge offers flexibility in creating data quality rules through various methods: Manual: Users can directly define rules on the Data Quality Rules page based on their needs. Recommended: OvalEdge analyzes data and suggests relevant rules, saving users time and effort. Load Metadata From Files: Users can upload predefined rule sets using a template for efficient rule creation. OvalEdge API: Users can create and manage rules through the OvalEdge API. The "Rule Creation Type" displays the method used to create the rules and enables the filtering option to filter rules by method.
Tags	The Tags help users organize and locate data quality rules efficiently. It displays all tags associated with each rule, allowing for: Filtering: Users can filter the rule list by specific tags, making it easier to find relevant rules. Grouping: Rules can be grouped based on shared tags, providing a more organized view for managing similar rules.
Status	The Status clarifies whether a rule is Active or in Draft form. This information determines what actions users can perform on the rule: Active Rules: Users can execute, edit, and schedule these rules to assess data quality automatically. Draft Rules: These rules are still under development and cannot be run yet. Users can edit them before activating them. The status also allows users to filter the list by status (Active or Draft), enabling them to focus on specific rules based on their current stage.
Object Type	The Object Type indicates the type of objects associated with each rule, such as Table, Table Column, File, File Column, or Code.
Functions	It lists the various functions used to build each data quality rule. These functions fall into two categories: Pre-built functions: OvalEdge provides a library of ready-made functions for common data quality checks, like verifying data accuracy or completeness. Custom functions: Users can create their own functions for specialized data quality needs, allowing for tailored rule creation. This column also allows users to filter the rule list by specific functions, making finding rules that address particular data quality concerns easier.
Dimensions	The Dimensions focus on the criteria used to assess data quality. These categories help users evaluate data reliability, accuracy, and usefulness in various contexts. They provide a framework for ensuring data meets specific standards. Pre-built Dimensions: OvalEdge offers pre-defined dimensions like accuracy or completeness to evaluate data quality. Custom Dimensions: Users can create their own custom dimensions to address specific data quality needs. This column allows users to see which dimensions are applied by each rule.
Associated Objects	The Associated Objects display the number of data objects connected to each rule. These data objects, called "associated objects," can include tables, columns, files, or code. See the count: Quickly view each rule's associated objects. Sort by count: Organize the rules based on the number of data objects linked to them, making it easier to identify rules with a wide reach. This helps users understand the scope of each rule and how many data items it impacts.
Steward	It displays the name of the person responsible for each data quality rule. Users can search this column to find rules assigned to specific stewards.
Last Result	It displays the data quality rule's last execution status, and users can search for rules based on that status.
Last Run On	It assists users in understanding the timestamp of the last run for each data quality rule. Users can sort the rules based on the date provided in the timestamp.
Created By	It displays the username of the user who created the data quality rule.
Created Date	It displays the creation date of the data quality rule.
Last Modified By	It displays the username of the user who made the last changes to the data quality rule.
Last Modified On	It displays the date and time of the last modification made to the data quality rule.

### Configure Views OvalEdge allows users to customize how data quality rule information is presented on the landing page through Configurable Views.

* Column Management: * Adjust Columns: Users can easily select, hide, or rearrange columns to focus on the data quality rule details most relevant to their needs. * Multiple Views: * Custom Views: Users can define personalized views for various use cases. These custom views can be saved for quick access in the future. * System-Defined Views: OvalEdge also provides pre-built views optimized for common tasks, saving users time and effort. * Reset Functionality: * Restore Default View: Users can easily revert to the default view configuration by clicking the Reset icon. ### User Actions The Data Quality Rule landing page offers a 9-Dots menu for managing rules in bulk. This menu allows users to perform the following actions efficiently: * Delete Multiple Rules: Select and remove unwanted data quality rules simultaneously. * Bulk Tagging: Assign relevant tags to multiple rules simultaneously, simplifying the organization and filtering for easier user rule management. * Mass Tag Removal: Quickly remove tags from a group of data quality rules, streamlining the user categorization process. * Execute Rules in Bulk: Run multiple data quality rules concurrently to assess data quality and consistency across various datasets, providing users with a comprehensive overview.