What is Data Governance and how can it be implemented in Collibra?

Understanding Data Governance and Its Implementation in Collibra: A Banking Sector Example

What is Data Governance?

Data Governance refers to the collection of processes, policies, standards, roles, and technologies required to ensure the effective and efficient use of data to meet an organization’s goals. Its primary focus is on ensuring data quality, availability, integrity, security, and compliance.

Key aspects of data governance include:

1. Data Ownership: Defining who owns and is responsible for different datasets.
2. Data Quality: Ensuring data accuracy, completeness, consistency, and reliability.
3. Data Security: Safeguarding data from unauthorized access and breaches.
4. Compliance: Adhering to industry standards and regulations (e.g., GDPR, CCPA, Basel III).
5. Metadata Management: Documenting data lineage, definitions, and usage.

How to Do Data Governance in Collibra

Collibra is a leading Data Intelligence Platform that simplifies the implementation of data governance by providing tools for data cataloging, workflow automation, policy management, and collaboration.Want to dive deeper into learning Collibra? Explore this comprehensive course: Collibra Data Quality and Workflow and Integration Development.

Here’s a step-by-step guide to implementing data governance using Collibra:

1. Define Roles and Responsibilities

Collibra allows organizations to assign roles such as:

• Data Owners: Responsible for data assets.
• Data Stewards: Ensure data quality and compliance.
• Data Consumers: Use the data for decision-making.

In the banking sector, a Data Steward might oversee customer data quality, ensuring that fields like “Customer Name” and “Account Number” are always complete and accurate.

2. Create and Manage a Data Catalog

Collibra’s Data Catalog provides a central repository where all data assets (e.g., databases, tables, reports) are documented, searchable, and easily discoverable.

Example: A bank can catalog all its datasets, such as:

• Customer Accounts: Fields like Account_Number, Customer_Name, Balance.
• Transactions: Fields like Transaction_ID, Amount, Date, Location.

This allows teams to quickly locate and understand available data, eliminating silos.

3. Implement Data Quality Rules

Using Collibra, you can define data quality rules to ensure data meets predefined standards.

Example in banking:

• A rule to ensure that all account numbers are unique.
• A rule to validate that transaction amounts are positive.

Collibra integrates with external tools (like Informatica or Ataccama) to automate the enforcement of these rules.

4. Establish Workflows for Data Governance

Collibra provides customizable workflows for governing data. Workflows enable automation of tasks like data access requests, issue resolution, and policy approval.

Banking Example:

• If a Data Analyst requests access to sensitive customer data, Collibra initiates an Access Request Workflow where the Data Owner must approve or deny the request based on the bank’s policies.

5. Monitor and Report Data Lineage

Collibra’s Data Lineage feature provides a visual representation of how data flows through systems, ensuring transparency and accountability.

Banking Example:

• Track how customer transaction data flows from the core banking system to analytics dashboards used for fraud detection.

6. Ensure Compliance with Regulations

Collibra helps banks comply with strict regulations like:

• GDPR: Ensuring personal customer data is properly handled.
• Basel III: Managing financial data integrity for risk management.
• CCPA: Protecting customer privacy.

Example: Use Collibra to enforce a policy that restricts access to customer Personally Identifiable Information (PII) unless authorized.

7. Enable Collaboration Across Teams

Collibra fosters collaboration by providing a single platform where business and IT teams can interact, share feedback, and resolve issues.

Example: If a data quality issue is detected in loan processing data, Collibra can assign the issue to the Data Steward for resolution, tracking progress in real-time.

Example Use Case in Banking: Improving Loan Data Quality

Problem:

A bank faces discrepancies in its loan application data, where customer income and credit score values are often incomplete or incorrect, leading to regulatory compliance risks.

Solution Using Collibra:

Catalog Loan Data:

• Document datasets like Loan Applications, Credit Scores, and Customer Profiles in Collibra’s Data Catalog.

Define Data Quality Rules:

• Income fields must be numeric and above $1,000.
• Credit Scores must be integers between 300–850.

Create Workflows:

• Automate an Exception Workflow to notify Data Stewards whenever a record fails the quality check.

Data Lineage:

• Map data lineage to trace issues back to their source, e.g., incorrect data entry in a branch office system.

Monitor Compliance:

• Generate reports to ensure compliance with lending regulations.

By leveraging Collibra, the bank can significantly improve its data governance, reduce regulatory risks, and enhance decision-making capabilities.

Additional Resource

If you’re eager to deepen your knowledge of Collibra, explore these comprehensive courses on Udemy: Collibra Data Quality and Workflow and Integration Development

These courses equip you with the skills to design custom workflows, develop integrations, and leverage advanced technologies to enhance data governance capabilities.