4.1 Data Quality Reports

This Data Quality Report offers an in-depth overview of data quality issues across multiple entities. It provides key insights into the checks performed, their distribution, and outcomes, along with customizable filters and visualizations. The report helps organizations monitor, analyze, and address data quality challenges systematically.

Data Quality rules can be created, modified, or approved within the Data Quality Summary framework which is a scalable rule-based engine providing robust tools for maintaining high-quality, and reliable data.

The Data Quality Rules are predefined validation checks that ensure the accuracy and integrity of data within the system. These rules support validations such as Range, Data Length, Column Reference/Specific Value, List of Values/Codes, Null Value, Blank Value, Referential Integrity, Duplicity, and Custom Check/Business logic.

Users can group, execute, and manage these rules, enabling efficient monitoring and correction of data quality issues.

To access these reports, navigate using the following path:

1. Navigate to Home > Catalog > Shared Folders > Data Quality Visualization > Data Quality.

The Data Quality visualization features several default filters, such as Number of Checks, Type, Class, and Severity, enabling users to customize their view based on specific preferences or needs.

To enhance usability and customization, the Data Quality visualizations include several default filters as listed below:

Table 4-1 Default Filters

Filter	Description
Period	Allows users to filter data by time levels, including years, months, weeks, or days.
As of Date	Facilitates analysis based on specific dates.
Group	Enables segmentation based on data groupings

Table 4-2 Type Filters

Type Filter	Description
Custom Check	Validates data against user-defined business rules or custom logic tailored to specific organizational requirements.
Duplicity Check	Identifies and flags duplicate records within the dataset to ensure data uniqueness and accuracy.
List of Values or Code	Ensures that data values in specific fields match predefined valid entries or codes from a reference list.
Null Values Check	Detects and flags fields with missing (null) values to address gaps in data completeness.
Other	Captures data quality validations that do not fall into standard predefined categories, often accommodating miscellaneous checks.
Referential Integrity Check	Ensures that relationships between linked tables are consistent, verifying that foreign keys correctly reference primary keys.

Table 4-3 Class Filters

Class Filter	Description
Conformity	Ensure that data adheres to defined formats, patterns, or standards, such as date formats or character limits.
Consistency	Validate that data values remain logically and structurally aligned across different datasets or fields to maintain data integrity.
Generic	Cover flexible, custom-defined validations tailored to specific business rules or requirements.
Others	Include specialized or advanced validations, such as cross-referencing external datasets or verifying complex relationships between data elements.