Data Quality Module – An Overview

1. Overview

The Data Quality module in Governata is one of the core modules aimed at measuring and improving data quality within the organization.

This module enables continuous monitoring of data quality through clear indicators, tracking issues, and applying quality rules to ensure data accuracy, completeness, and consistency.

The module helps to:

• Measure data quality using clear indicators
• Discover problems in data
• Track and address data quality issues
• Apply data quality rules
• Improve data reliability within the organization

2. System Objectives

The Data Quality module aims to ensure that data used within the organization is:

• Accurate
• Complete
• Consistent
• Up to date

This supports decision-making based on reliable data.

3. Scope of the Data Quality Module

This manual covers the use of the Data Quality module, including:

• Data Quality overview dashboard
• Data quality issue tracking
• Data quality rule management
• Quality indicator analysis

4. Key Definitions

Data Quality

The degree of accuracy, completeness, and consistency of data within the system.

Data Quality Rules

A set of rules applied to verify the validity of data.

Data Issues

Errors, incorrect values, or missing data in the dataset.

Data Quality Tracking

The process of monitoring issues and resolving them.

5. Roles and Responsibilities

Several roles share responsibility for managing data quality:

Data Stewards

Responsible for monitoring data quality and resolving issues.

Data Analysts

Use quality indicators to evaluate data before using it.

Governance Teams

Set policies and monitor compliance with data quality standards.

Technical Teams

Support the implementation of rules and technical improvement of data quality.

6. Target Audience

• Data Stewards
• Data Analysts
• Governance Teams
• IT Teams

7.How to Access the Data Quality Module

To start using the Data Quality module:

1. Navigate to the side menu.
2. Select Data Quality.
3. Select one of the sections:

• Overview
• Data Quality Tracking
• Data Quality Rules

As shown in Figure (1).

[Figure (1)]

8. Main Interface Overview

 

First: Overview

The Overview page displays comprehensive indicators about data quality within the system.

As shown in Figure (2).

[Figure (2)]

Includes:

• Total rows
• Valid rows
• Invalid rows
• Overall quality percentage

 

Quality indicators are also displayed such as:

• Consistency
• Accuracy
• Data timeliness
• Validity
• Completeness
• Data uniqueness

 

📌 Tip These indicators help evaluate data quality quickly.

 

Second: Data Quality Tracking

This section is used to monitor and manage data quality issues.

As shown in Figure (3).

[Figure (3)]

It displays:

• Number of issues
• Verified issues
• Resolved issues

 

It also contains a table including:

• Affected fields
• Issue description
• Status
• Person assigned for resolution

 

Third: Adding a Tracker

The Add Tracker feature is used to register a new data quality issue for monitoring and resolution.

As shown in Figure (4).

[Figure (4)]

Steps:

1. Navigate to Data Quality Tracking.
2. Click Add Tracker +.
3. Enter: (a) Name, (b) Description.
4. Click Submit.

 

Outcome:

The issue is registered and appears in the tracking list for follow-up.

 

Fourth: Data Quality Rules

This section allows management of data quality rules.

As shown in Figure (5).

[Figure (5)]

Displays:

• Total records examined
• Successful records
• Failed records

 

Also contains a table showing:

• Rule name
• Table name
• Database
• Rule status

9. Using the Module

Monitoring Data Quality

The user can:

• Review quality indicators
• Determine data quality level
• Analyze performance over time

Issue Tracking

Steps:

1. Navigate to Data Quality Tracking.
2. Review the list of issues.
3. Select the issue.
4. Monitor its status or update it.

 

Managing Quality Rules

Steps:

1. Navigate to Data Quality Rules.
2. Review existing rules.
3. Analyze examination results.
4. Edit or improve rules as needed.
5. Permissions

Permissions depend on the user role within the system.

May include:

• View quality indicators
• Manage issues
• Create or edit quality rules

 

Access may also be restricted based on:

• Data type
• Data classification
• Sensitivity level

10. Usage Scenarios

Scenario 1: Discovering a Data Issue

Situation

A user noticed a drop in data quality.

Steps

1. Enter Overview.
2. Review indicators.
3. Navigate to Data Quality Tracking.
4. Identify the issue.

Outcome

The cause of the issue is discovered and resolution begins.

Scenario 2: Tracking a Data Quality Issue

Situation

A Data Steward wants to follow up on an issue.

Steps

1. Enter Data Quality Tracking.
2. Search for the issue.
3. Review its status.
4. Update the status.

Outcome

The issue is tracked until resolved.

Scenario 3: Analyzing Quality Rule Results

Situation

A user wants to evaluate data quality.

Steps

1. Enter Data Quality Rules.
2. Review success and failure rates.
3. Analyze rules.

Outcome

Data quality is evaluated and decisions are made to improve it.

11. Best Practices

• Review quality indicators periodically
• Address issues as soon as they are discovered
• Apply clear quality rules
• Assign a responsible person to each issue
• Use quality indicators before making decisions

12. Frequently Asked Questions

What is the purpose of the Data Quality module?

To ensure the accuracy, completeness, and consistency of data within the organization.

How do I know that data is incorrect?

Through quality indicators or registered issues.

Can quality rules be edited?

Yes, rules can be edited to improve data quality.