Decoding Scorecard Rule Occurrences in Informatica Cloud Data Profiling

Introduction:

Score Card Rule Occurrence is a way to check the quality of data in cloud Profiling and display the results in CDGC. It involves the application and tracking of specific data quality and governance rules within a cloud environment. CDGC platforms help organizations manage and ensure the integrity, privacy, and compliance of their data assets.

To create Score Card Rule Occurrence, we need to create Data Profiling task for the object for which data quality rule need to be executed.

Below are steps to create the Score Card Rule Occurrence in Cloud Data Profiling

1.Click the Data Profiling service and navigate the New icon then click the Data profiling Task asset.

2.Provide the name, Description and Location as per your project naming standards.

3.Choose the source connection and table in source detail tab.

4.Here we can choose Run profile on option to process full or partial rows in a source table.

5.Choose the columns in column tab. We can choose all columns or selected columns.

6.Navigate to Rules tab and click the plus icon to add the rule specification. Here we took 3 columns to valid the rules as per our source data.

7.Click on the three dot and navigate to Scorecard Metrics.

8.Click plus icon to add the already defined rule specification to create the Score card rule occurrence.

 

9. Provide the name, Description as per your project naming standards in Rule Occurrence.

10.Define the Rule occurrence Thresholds as per your source data.

11.Added the score metrics and we can see the View Scorecard is disabled. Save and Run the Data Profiling task.

12. Data Profiling task is completed successfully and we will check the results.

13.Here we can see the profiled data for each and every column in Result tab.

14.Now the View Scorecard option is enabled. Click the View Scorecard and it will navigate to the CDGC service page.

15.In CDGC service we can see the rule occurrences. Click on each rule occurrence and validate the data.

16.Click on one of the Rule occurrences, navigate the Score tab to verify the total rows and failed records status.

17.We can see the total number of runs for this rule occurrence.

18.To check the failed records, click on the three dot and we can see the preview of valid and failed rows.

Benefits of Score Card Rule Occurrence in CDGC:

1. Enhanced Data Quality: Identifies and resolves data quality issues.
2. Improved Compliance: Ensures adherence to regulatory standards.
3. Actionable Insights: Highlights frequent issues for targeted action.
4. Prioritization of Remediation: Focuses efforts on the most critical problems.
5. Visibility and Transparency: Provides clear metrics and insights.
6. Continuous Improvement: Supports ongoing refinement of data practices.
7. Efficient Monitoring: Automates tracking, saving time and effort.
8. Risk Management: Helps mitigate data-related risks and compliance fines.

Limitations of Score Card Rule Occurrence in Cloud Data Governance and Compliance (CDGC):

1. Complexity and Maintenance: Complex rules can be hard to define and maintain, requiring frequent updates.
2. Data Quality vs. Rule Limitations: Rules may not capture all aspects of data quality and can produce false positives or negatives.
3. Scalability Issues: Large datasets can strain system performance, and complex datasets may challenge rule efficiency.
4. Integration Challenges: Integrating diverse data sources and ensuring tool compatibility can be difficult.
5. Contextual Understanding: Rules may lack contextual insight, leading to incomplete understanding of data issues.
6. Resource Requirements: Managing rules can be resource-intensive and require significant training.
7. Regulatory and Compliance Constraints: Adapting to changing regulations and handling global compliance can add complexity.

To address these limitations, regularly update rules, optimize performance, improve integration, and enhance contextual understanding.

Conclusion Score card rule occurrence in Cloud Data Governance and Compliance (CDGC) provides significant value by enhancing data quality, ensuring regulatory compliance, and offering actionable insights. While it helps in prioritizing remediation efforts and improving transparency, it also comes with limitations such as complexity, scalability challenges, and integration issues. By addressing these limitations and leveraging the benefits effectively, organizations can maintain robust data governance practices, mitigate risks, and continuously improve their data management processes.

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