Introduction

In an era of data-driven decision-making, ensuring the quality, security, privacy, and integrity of data is paramount. Data governance and stewardship play a critical role in managing and protecting organizational data assets while ensuring that data is used effectively and ethically. This course covers the essential practices, policies, and technologies involved in data governance, with a strong emphasis on the roles and responsibilities of data stewards. Through practical examples, case studies, and hands-on exercises, participants will gain a deep understanding of data governance frameworks, data quality management, regulatory compliance, and how to establish a culture of data stewardship within an organization.

Objectives

By the end of this course, participants will:

• Understand the principles and frameworks of data governance.
• Learn how to implement data governance policies and best practices.
• Understand the roles and responsibilities of data stewards and data governance teams.
• Learn how to manage data quality, privacy, and security.
• Develop strategies for complying with data regulations and standards (e.g., GDPR, CCPA).
• Gain practical experience with tools and technologies that support data governance and stewardship.
• Learn how to drive a data governance program and build a data governance roadmap.

Who Should Attend?

This course is ideal for:

• Data governance professionals and teams.
• Data stewards, data managers, and data analysts.
• IT professionals involved in data management and security.
• Project managers overseeing data governance initiatives.
• Compliance officers and legal professionals dealing with data privacy regulations.
• Senior leaders and executives responsible for data strategy and management.

Day 1: Introduction to Data Governance

Morning Session: Fundamentals of Data Governance

• What is data governance? Understanding its importance and impact on organizations.
• Key principles of data governance: accountability, transparency, stewardship, and integrity.
• Overview of data governance frameworks: DAMA-DMBOK, DCAM, and others.
• The role of data governance in business strategy and risk management.
• Data governance vs. data management: Differences and synergies.
• Hands-on: Mapping data governance goals to organizational strategy.

Afternoon Session: Components of a Data Governance Framework

• Data governance policies and standards: Defining guidelines and rules for data management.
• Data ownership and stewardship: The role of data stewards in data governance.
• Establishing a data governance team: Roles and responsibilities of key stakeholders.
• Data governance tools and technologies: Software, platforms, and frameworks for managing data governance.
• Case study: Review of a successful data governance implementation in a leading organization.

Day 2: Data Stewardship and Roles

Morning Session: The Role of Data Stewards

• What is data stewardship? Understanding the role and significance of data stewards.
• Key responsibilities of data stewards: Ensuring data quality, compliance, and security.
• Managing data quality: Data validation, cleansing, and enrichment.
• Data stewardship in practice: Day-to-day tasks and challenges.
• Building effective data stewardship programs and teams.
• Hands-on: Developing a data stewardship framework for a business unit.

Afternoon Session: Data Stewardship Best Practices

• Data stewardship in various contexts: Enterprise data, operational data, and analytical data.
• Collaboration between data stewards and other roles (e.g., data governance officers, IT, legal).
• Creating data stewardship policies: Guidelines for data access, usage, and lifecycle management.
• Metrics and KPIs for data stewardship: Measuring the success and impact of stewardship efforts.
• Real-world example: How a global organization successfully implemented data stewardship best practices.

Day 3: Data Quality Management

Morning Session: Managing Data Quality

• The importance of data quality in governance: Impact on decision-making and operational efficiency.
• Key dimensions of data quality: Accuracy, completeness, consistency, timeliness, and reliability.
• Data quality management techniques: Profiling, validation, monitoring, and cleansing.
• Setting up a data quality framework: Standards, policies, and procedures.
• Hands-on: Conducting a data quality assessment for a sample dataset.

Afternoon Session: Data Quality Tools and Technologies

• Overview of data quality management tools: Informatica, Talend, SAS, and open-source alternatives.
• How to integrate data quality tools into data governance practices.
• Automating data quality monitoring: Continuous data validation and reporting.
• Best practices for data quality governance: Implementing monitoring and alerting systems.
• Case study: Real-world example of a data quality management initiative and its impact.

Day 4: Data Security, Privacy, and Compliance

Morning Session: Data Security and Privacy Regulations

• Overview of key data security and privacy regulations: GDPR, CCPA, HIPAA, and others.
• Understanding the risks associated with poor data governance: Data breaches, fines, and reputation damage.
• Best practices for securing sensitive data: Encryption, access controls, and data masking.
• The role of data governance in ensuring regulatory compliance.
• Hands-on: Developing a data privacy compliance checklist for a data governance program.

Afternoon Session: Ensuring Compliance with Data Regulations

• Implementing policies for data retention, access, and disposal.
• Cross-border data transfer compliance: Managing international data flows under GDPR and similar regulations.
• Data audits and monitoring: Ensuring continuous compliance with privacy laws.
• Building a compliance-first data governance culture: Training, awareness, and accountability.
• Case study: Analyzing the regulatory challenges faced by a multinational corporation and the lessons learned.

Day 5: Building a Data Governance Program and Roadmap

Morning Session: Developing a Data Governance Program

• Steps to building a data governance program: From planning to execution.
• Defining data governance goals, objectives, and success metrics.
• Aligning data governance with business priorities: Creating a roadmap.
• Organizational structures for data governance: Centralized vs. decentralized models.
• Risk management in data governance: Identifying and mitigating potential risks.
• Hands-on: Creating a data governance program roadmap for an organization.

Afternoon Session: Implementing and Sustaining Data Governance

• Communicating the value of data governance to stakeholders and executives.
• Overcoming common challenges in data governance implementation: Resistance to change, lack of resources, etc.
• Continuous improvement in data governance: Evolving policies and procedures as business needs change.
• Scaling data governance for large and complex organizations.
• Final Q&A and discussion: Sharing experiences and challenges in implementing data governance programs.

Materials and Tools:

• Software: Data governance platforms (e.g., Collibra, Alation), data quality tools (e.g., Talend, Informatica), privacy management tools (e.g., OneTrust).
• Templates: Data governance policy templates, data stewardship frameworks, compliance checklists.
• Reading: “The DAMA Guide to the Data Management Body of Knowledge (DMBOK)” by DAMA International, “Data Management for Researchers” by Kristin Briney.

Post-Course Support:

• Access to course materials, recorded sessions, and additional resources for continued learning.
• Follow-up workshops on advanced topics such as data governance in cloud environments, AI/ML governance, and governance for big data projects.
• Community forum for sharing experiences, asking questions, and collaborating on data governance challenges.