By the end of this course, learners will be able to:

1. Understand the fundamental concepts, importance, and benefits of data governance.
2. Explore industry-standard frameworks like DAMA-DMBOK, CMMI, and IBM’s DG framework.
3. Define and establish data governance roles, responsibilities, and organizational structures.
4. Design and implement data governance policies, standards, and procedures.
5. Evaluate and utilize modern governance tools such as data catalogs, lineage, and compliance platforms.
6. Address key regulations and ensure organizational data privacy and security.
7. Monitor and measure data governance effectiveness using KPIs and metrics.

Module 1: Introduction to Data Governance

• Definition and purpose of data governance
• Key components and stakeholders involved
• Understanding the need for data governance
• Benefits and challenges

Module 2: Data Governance Frameworks

• Overview of leading frameworks (DAMA-DMBOK, CMMI, IBM)
• Core components: policies, standards, and processes
• Tools and technologies supporting governance

Module 3: Organizational Structures and Roles

• Establishing DG structures
• Defining roles and responsibilities:
• Data Stewards
• Data Owners
• Data Users
• Data Quality Analyst
• Compliance Officer
• Data Architect
• Business Users
• Management Team
• Data Governance Council

Module 4: Developing a Data Governance Plan

• Essential components of a governance plan
• Integration of stewardship and ownership
• Creating role-based accountability

Module 5: Compliance and Privacy in Data Governance

• Overview of key regulations (GDPR, HIPAA, etc.)
• Implementing privacy and compliance measures
• Ensuring data security

Module 6: Tools & Technologies in Data Governance

• Data cataloging solutions
• Data quality and lineage tools
• Master Data Management (MDM)
• Governance platforms (Collibra, Talend, Informatica)
• Security and privacy tools
• Workflow and collaboration systems

Module 7: Implementation of DG Policies

• Establishing effective procedures
• Aligning policies with business objectives
• Using Metrics & KPIs to measure success

Module 8: Trends and Innovations in Data Governance

• Automation in governance
• AI-enabled governance systems
• Future directions in data stewardship

Upon successful completion of the Data Governance course, learners will be awarded a Course Completion Certificate from Uplatz, demonstrating their understanding and practical skills in enterprise data governance strategies and tools.

This certificate validates your ability to implement governance initiatives, define policies, and manage compliance across enterprise systems. It serves as a valuable credential for roles such as Data Governance Analyst, Compliance Manager, Data Steward, and Chief Data Officer.

This course also acts as a preparatory path for industry-recognized certifications such as:

• CDMP (Certified Data Management Professional) from DAMA International
• DGSP (Data Governance & Stewardship Professional)
• Other vendor-specific certifications in governance tools (Collibra, Informatica, IBM, etc.)

Completing this course opens up diverse roles in the data management and compliance space, such as:

1. Data Governance Analyst
2. Data Steward / Data Owner
3. Chief Data Officer (CDO)
4. Compliance Manager
5. Data Quality Specialist
6. Enterprise Data Architect

1. What is data governance and why is it important in modern enterprises?
Data governance refers to the framework of policies, processes, roles, and standards that ensure the effective management, quality, integrity, and security of data across an organization. It is important in modern enterprises because it ensures that data is accurate, accessible, consistent, and protected. With increasing reliance on data-driven decision-making and compliance requirements like GDPR and HIPAA, data governance helps mitigate risks, improve operational efficiency, support analytics, and ensure regulatory compliance.

2. How do DAMA-DMBOK and IBM’s frameworks differ in structure and implementation?
The DAMA-DMBOK (Data Management Body of Knowledge) is a vendor-neutral framework that outlines core data management principles, including 11 knowledge areas such as data governance, data quality, and data security. It serves as a comprehensive guide for establishing data governance practices.
In contrast, IBM’s Data Governance Framework is more tool-centric and focuses on implementation through IBM’s suite of data governance tools. It provides structured workflows, integration mechanisms, and automation capabilities for operationalizing governance in enterprise environments. DAMA offers conceptual guidance, while IBM’s framework is more implementation-oriented and tailored to its ecosystem.

3. What are the main responsibilities of a Data Steward vs. a Data Owner?
A Data Steward is responsible for ensuring the quality, integrity, and consistency of data. They manage day-to-day data issues, perform data profiling, and ensure compliance with policies. They serve as custodians of data within their domain.
A Data Owner, on the other hand, holds decision-making authority over specific datasets. They define data access policies, approve data sharing, and are accountable for data compliance and protection. While stewards manage data quality, owners define and enforce the rules.

4. How would you design a data governance policy for a multinational company?
Designing a data governance policy for a multinational company involves:

·         Understanding global compliance needs such as GDPR, CCPA, and local regulations.

·         Defining roles and responsibilities across regions and business units.

·         Establishing a central governance council with local data stewards and owners.

·         Creating standardized data definitions and classifications to support consistency.

·         Implementing scalable tools for data cataloging, quality monitoring, and access control.

·         Developing escalation procedures for data issues and non-compliance.

·         Continuous training and communication across the organization to promote a data-driven culture.

5. What compliance regulations must a data governance plan address?
A robust data governance plan must address several regulations depending on the industry and geography. Key ones include:

·         GDPR (General Data Protection Regulation) – for personal data protection in the EU.

·         HIPAA (Health Insurance Portability and Accountability Act) – for health data in the U.S.

·         CCPA (California Consumer Privacy Act) – for consumer data rights.

·         SOX (Sarbanes-Oxley Act) – for financial reporting accuracy.

·         PCI-DSS (Payment Card Industry Data Security Standard) – for credit card data security.
It must also align with industry standards such as ISO/IEC 27001 for information security.

6. Describe tools used for data lineage and data quality.

·         Data Lineage Tools: These track the lifecycle of data as it moves through systems. Tools like Informatica Enterprise Data Catalog, Collibra, and Apache Atlas visualize data flow, transformations, and dependencies.

·         Data Quality Tools: These tools assess and cleanse data by identifying inaccuracies, duplicates, or missing values. Popular ones include Talend Data Quality, IBM InfoSphere QualityStage, and SAP Information Steward. They support data profiling, rule enforcement, and automated corrections.

7. How does a Data Governance Council function within an organization?
A Data Governance Council serves as the governing body responsible for setting the strategic direction of data governance initiatives. It is typically composed of senior executives, data owners, compliance officers, and business stakeholders. The council defines policies, approves frameworks, resolves conflicts, allocates resources, and ensures alignment with business goals. It also provides oversight and monitors adherence to governance standards across the organization.

8. What metrics would you use to assess the effectiveness of data governance?
To evaluate data governance effectiveness, organizations can use metrics such as:

·         Data Quality Scores: Accuracy, completeness, consistency, and timeliness of key datasets.

·         Policy Compliance Rate: Percentage of users adhering to data policies and procedures.

·         Issue Resolution Time: Average time taken to detect and resolve data quality issues.

·         Data Stewardship Activity: Volume and efficiency of stewardship tasks completed.

·         User Satisfaction: Feedback from business users regarding data usability and trust.

·         Audit Findings: Frequency and severity of audit issues related to data governance.

·         Access Control Violations: Number of unauthorized access attempts or breaches.

1. Who should take this course?
   Anyone interested in data governance, including data analysts, compliance officers, and IT managers.
2. Is this course beginner-friendly?
   Yes, it starts with foundational concepts and builds toward advanced practices.
3. Do I need a technical background to join?
   No, the course covers both technical and non-technical aspects of data governance.
4. What is the course format?
   Self-paced, online with pre-recorded lectures and downloadable resources.
5. Will I receive a certificate?
   Yes, a certificate is provided upon successful course completion.
6. Does this course prepare for certification exams?
   Yes, it lays a strong foundation for DG certifications such as CDMP and DGSP.
7. Are real-world tools demonstrated in the course?
   Yes, examples of data catalogs, quality tools, and governance platforms are included.
8. How long is access granted to the course?
   Lifetime access to course materials.