Governance

This article explains how to identify, register, and remediate Legacy Data Quality Issues in askEdgi. It describes how approved data quality remediation rules are associated with datasets and applied within the workspace without modifying the source data.

What is a Legacy Data Quality Issue?

A Legacy Data Quality Issue is a known data quality problem that exists in historical data and cannot be easily corrected in the source system. These issues often result from changes in business rules, regulations, system migrations, or data standards over time.

Instead of modifying the source data, organizations define approved remediation rules to correct the data during analysis.

Example

A product dataset contains the $ symbol for multiple currencies:

• US Dollar (USD)

• Australian Dollar (AUD)

• Singapore Dollar (SGD)

• Canadian Dollar (CAD)

Although the symbol is the same, each currency represents a different monetary value. This can lead to incorrect reporting, analysis, and business decisions.

Registering a Legacy Data Quality Issue

Before users can clean data in askEdgi, the issue must be reviewed, approved, and registered as a Legacy Data Quality Issue.

Identify the Data Quality Issue

Data quality issues can be identified:

• Manually by users

• Through automated Data Quality Rules

• Through Data Quality Recipes

  

In this example, users identify that the OECountryCurrencies table uses the same $ symbol for multiple country currencies.

Create an Output Table

After identifying the issue, users create an output table containing the records that require remediation.

The output table captures the affected data and serves as the source for creating service requests.

Create a Data Quality Service Request

Navigate to the output table. Select the three dots (⋮) menu next to the output table, and select Configure Action.

Choose Create Data Quality Service Request.

The Create Data Quality Service Request wizard opens.

The Table Column Data Quality Issue template is mapped to the Take Action feature in askEdgi. For this functionality to work, Legacy Data Quality Status must be enabled in the template's properties.

If a custom template is used instead of Table Column Data Quality Issue, ensure that this property is enabled on the custom template.

Map Output Table Columns

Map the output table columns to the fields required to configure the Service Request. It includes Object Id, Object Type, Summary and Description. After completing the mapping, save the configuration.

Execute the Service Request Job

When the configuration is submitted:

• The system creates a background job.

• A Job ID is generated for tracking.

• Service Requests are created for the identified records.

Users can monitor the job status using the generated Job ID.

Review the Job Results

After the job completes successfully, the output table displays:

• Service Request Number

• Request Status

• Related issue details

Users can select the Service Request Number to open the Service Request page and review the request details.

Review and Approval

Data Owners, Data Stewards, or designated administrators review the Service Request and the proposed remediation.

For the OECountryCurrencies example:

Issue

Multiple currencies use the same $ symbol.

Corrective Action

Prefix all non-USD dollar ($) symbols with the first two characters of their ISO currency code.

If the issue cannot be corrected in the source system and requires an approved workaround, the Service Request is approved and marked as a Legacy Data Quality Issue.

The approved Corrective Action becomes the cleanup context associated with the dataset.

Dataset is Registered as a Legacy Data Quality Issue

Once approved:

• The dataset is associated with the approved remediation rule.

• The issue is registered as a Legacy Data Quality Issue.

• The remediation becomes available for use in askEdgi.

When users add the dataset to an askEdgi workspace, the system displays a Data Quality Debt indicator, showing that approved cleanup logic is available for the dataset.

Data Cleanup

Data Cleanup allows users to apply approved remediation rules directly within the askEdgi workspace without modifying the source data.Data Cleanup allows users to apply approved remediation rules to datasets that contain legacy data quality issues directly within the askEdgi workspace when those issues cannot be corrected in the source system.

Add a Dataset to the Workspace

When a dataset is added to the askEdgi workspace, the system checks whether any approved Legacy Data Quality Issues exist for that dataset. If approved cleanup rules are available, askEdgi displays a Data Quality Debt indicator next to the dataset.

Review the Cleanup Context

Select the Data Quality Debt indicator to view the available cleanup context.

The cleanup context includes:

• Context Cleanup Rule

Users can review the details before deciding whether to clean the data.

Execute Data Cleanup

Select Clean Data to apply the approved remediation logic.

askEdgi creates a new thread and executes the cleanup process in the workspace. During execution:

• The approved remediation logic is applied to the dataset.

• Source data remains unchanged.

• A cleaned version of the dataset is generated.

Review the Results

After execution, the cleaned dataset becomes available in the workspace.

Users can:

• View the cleaned data

• Review the execution details

• View the generated code and logic used during cleanup

• Verify how the remediation was applied

Analyze the Cleaned Dataset

The cleaned dataset can be used for:

• Analysis in askEdgi

• Reporting and dashboards

• AI-driven insights

• Data exports

Since the cleanup is applied only within the workspace, the original source data is not modified.

Data Quality Debt Status Update

After the cleanup is executed successfully:

• The approved remediation logic is applied to the workspace dataset.

• A cleaned dataset is generated and becomes available for analysis.

• The Data Quality Debt indicator is removed from the dataset in the workspace, indicating that the approved cleanup has been applied.

  

Viewing Cleanup Information

Users can select the cleaned dataset in the workspace to review information about the applied cleanup. A Note section is displayed, providing details such as:

Controlled Transformation

Cleanup results in a derived dataset:

• Used only within the workspace session

• Can be removed to revert to original data

• Does not affect source systems

Explainability and Governance

All cleanup actions are fully governed and transparent:

• Based only on approved context

• Clearly explains issue, logic, and remediation

• Fully auditable and traceable

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