Rule Recommendations

Data Quality Rule Models streamline data management. Rule Models enable users to simultaneously handle rules for multiple Object types (tables, files, etc.). Users can create, run, and adjust rules in bulk, offering a more efficient approach.

Additionally, Rule Models provide:

• Clear recommendations.

• Allowing users to accept or reject them quickly.

• Minimizing errors and accelerating decision-making.

Users can build rule models that provide suggestions for multiple data objects, whether they are Tables or Files. This enhancement allows users to easily create, run, modify, and delete these models. Each model generates recommendations that users can either accept or ignore.

Where do we use Rule Models?

• Based on the Table or File: When users are unsure about what rule to apply to a Data Object, they can use rule models to get recommendations. This feature helps eliminate confusion and provides tailored suggestions for tables or files, ensuring that the proper data quality rules are applied to the correct data objects.

How do we use the Rule Models? Users can manually run models to generate recommendations. They can edit Rule Models and modify them as needed. Within the Rule Models, users can view the recommendations for data objects and decide whether to apply or ignore them. Accepting the recommendations will convert them into Data Quality Rules.

How do the Rule Models Work? Rule Models help recommend data quality rules by analyzing key information about the data objects. The system considers three factors—connector information, object type, and data type—to suggest the most relevant rules. Once the recommendations are generated, users can review them and decide whether to apply or ignore each rule. Rule Models also provide metrics to track effectiveness, enabling users to refine and improve data quality management across multiple objects.

Security - Who can Create a Rule Model The Rule Models are created by the author license user, and role admin users will act like the admins.

Author License

Creating a New Rule Model

Two-Step Process

The creation of a new recommendation model is a two-step process.

• Model Configuration (Model Name and Model Description)

• Object Selection (Table or File)

Model Configuration (Step 1)

In Step 1, the user will be configuring the Model Name and Model Description. The model name should not include any special characters or spaces. All the fields are mandatory.

Object Selection (Step 2)

Select Object Type: In this step, users need to select the Object Type before selecting the objects. It can be either a Table or a File. Based on the selected Object Type, objects will be populated.

Select Objects: In this step, users must select objects based on the selected object type.

Note: The user will be able to select only tables or files; however, the suggestions will be based on both the table and its columns if the table is selected, and the file's columns if the file is chosen.

Model Details

The landing page displays a list of recommendation models. Running a model will give users recommendations for data objects. Users can accept/ignore rule recommendations on data objects. Each model will have model metrics, allowing users to view the model's performance.

• Rule Model Name: This column displays the Rule Model Name which the user creates.

• Description: This column displays the Rule Model Description which the user gives.

• Status: This feature will show the status of each recommendation model

• New - When the Rule Model is created for the first time

• In Progress - When the Rule Model identifies the recommendations

• Completed - When the Rule Model execution is completed successfully

• Failed - When the Rule Model execution is failed

• Last Run Date: This column will display the date and time of the last execution of the recommendation model.

• Duration: This column displays the duration for which the recommendation was active.

• Created By: This column tells the user who has created and configured the recommendation model.

• Created On: This column indicates when the model was created. It tells the date and the time for better understanding.

• Updated By: This column tells the user who has updated and configured the recommendation model.

• Updated On: This column tells the user when the model was updated. It tells the date and the time for better understanding.

User Actions using Nine Dots

• Run Rule Recommendation: This option enables the user to run the model and receive rule recommendations for the specified data object. Running a model triggers a job in the backend.

• Delete Recommendation Model: This option allows the user to delete a particular recommendation model if it is not being used

Recommendation Model Summary Page

Recommendations for Data Objects: Whenever users click or enter the recommendation model, they will be presented with rule recommendations for the selected data objects.

Object details, along with the recommended rules, are shown to the user. Users can click the Thumbs up icon for the given recommendation. By doing this, the Recommendation will turn into a Data Quality Rule.

Other Options

• Bulk operations (Bulk Accept) within recommendation models

This option enables users to perform bulk actions, such as approving recommendations. This feature will be handled through nine dots, where the user selects Accept Recommendations.

• Rule Settings

This option allows users to set their preferences on the Rules, like Sending Alerts on Failure, creating a service Request on Failure, avoiding reporting Duplicate Service Requests, Apply Caution to Downstream Objects Upon Failure, Add Failed Values to the Remediation Center, Avoid Duplicate Failures, Max Failed Values Limit, and Schedule.

• Model Metrics

Model metrics give an overview of the Rule Model to the user. Model metrics help the user to understand how a particular Rule Model is performing. It shows all the execution details and how many recommendations were accepted after each execution. Based on these details, the model creator can decide whether to modify the model or delete the Rule Model if not in use.

Integration with other Modules

• Integration with Data Quality Rules: If any Rule Recommendations were accepted from the Rule Model, those rules that are created will be displayed here.

• Integration with Notification: The executor will receive notifications in the following manner.

Events	Execution Started	Execution Completed	Who will be notified
Manual Execution	No Notification	Notified	The one who executed the Model