Data Steward Program

Course description

Condensed training to acquire essential data management skills, covering data modeling, governance, data quality, database management system architecture, as well as the use of tools and best practices to ensure the security, integrity, and accessibility of data within an organization.

Course objectives

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

• Understand the principles of data governance: gain an in-depth knowledge of best practices and data management frameworks, as well as the responsibilities of a Data Steward.
• Implement data quality strategies: learn to define, monitor, and maintain data quality, ensuring accuracy, integrity, and reliability.
• Manage metadata and data flows: master tools and techniques to catalog, classify, and organize metadata, making it easier to manage and leverage.
• Know data standards and compliance: become familiar with legal and regulatory requirements, such as GDPR, and understand the importance of compliant data management.
• Optimize access and data security: learn to implement practices that protect sensitive data while ensuring accessibility for authorized users.

Who is this course for?

This course is intended for a wide audience, including:

• Data Analysts: Professionals wishing to deepen their skills in data management and governance to improve effectiveness in their role.
• Data Quality Managers: Experts seeking to standardize processes and ensure the reliability of information.
• IT or Data Project Managers: Managers involved in data-related projects who need to understand governance mechanisms and data structuring.
• Compliance Professionals: Specialists in compliance and regulations looking to integrate data management practices aligned with standards such as GDPR.
• Beginners in the Data Field: Anyone wishing to start a career in data management and governance, with a structured, job-oriented program.

Prerequisites

No specific prerequisites are required.

Course curriculum

Day 1: Introduction to data governance

• Objective: Understand the role of the Data Steward and the importance of data governance.
• Content:
  Overview of the fundamental principles of data governance.
  Roles and responsibilities of the Data Steward within an organization.
  Introduction to key concepts: governance, data quality, metadata, compliance.

Day 2: Data quality and metadata management

• Objective: Acquire skills to ensure data quality.
• Content:
  Methods to measure and maintain data quality.
  Data cleansing and validation practices.
  Introduction to metadata management and related tools.

Day 3: Data management policy and data management processes

• Objective: Establish effective policies and processes for data management.
• Content:
  Developing data management policies.
  Data lifecycle management processes: creation, storage, use, archiving, and deletion.
  Implementing controls to ensure data compliance.

Day 4: Data management tools and platforms

• Objective: Become familiar with data management tools used by a Data Steward.
• Content:
  Overview of data management, data quality, and metadata tools.
  Exploring data governance platforms: Cloud, databases, data integration tools.
  Introduction to audit and reporting tools.

Day 5: Data security and legal compliance

• Objective: Understand data security issues and legal requirements.
• Content:
  Data security policies and access management.
  Compliance with regulations such as GDPR and data protection law.
  Implementing security for sensitive data management.

Day 6: Enterprise data management strategy

• Objective: Develop effective strategies for enterprise-wide data management.
• Content:
  Building a data management strategy to maximize value.
  Creating a roadmap for data governance within the organization.
  Integrating data management into the company’s overall strategy.

Day 7: Data stewardship tools and practices

• Objective: Deepen data stewardship practice with advanced tools and processes.
• Content:
  Implementing continuous data management processes.
  Using data monitoring and management tools: dashboard, reports, audits.
  Optimizing practices for better metadata and data flow management.

Day 8: Case studies and hands-on practice

• Objective: Apply the knowledge acquired through practical case studies.
• Content:
  Real case studies on data management and governance.
  Hands-on workshops to simulate data management scenarios.
  Discussion of challenges encountered and possible solutions.

Course benefits

• Comprehensive and progressive program: A well-defined structure from fundamentals to advanced applications for deep understanding.
• Practical and contextual approach: Numerous hands-on workshops let participants use generative AI tools and models in real contexts.
• Expertise with cutting-edge tooling: Use the latest, most relevant frameworks and platforms for generative AI (Transformers, GANs, Cloud Platforms).
• Development of a real project: A full day dedicated to a capstone project, fostering integration of learning in a practical, professional scenario.
• Ethical and security dimension: In-depth reflection on ethical issues, bias, and regulation to ensure responsible use.
• Aligned with market needs: Training designed to meet current enterprise needs for innovative, high-performing AI solutions.
• Support and guidance: Mentoring by experts and access to resources to ensure sustained upskilling.

Teaching methods and tools used

• Live demonstrations with data science services.
• Hands-on workshops and real case studies across various sectors (industry, retail, healthcare).
• Experience sharing: Best practices and common pitfalls in enterprises.
• Simulations and tools: Use of simulators for interactive workshops.

Assessment

• End-of-course multiple-choice quiz to test understanding of the concepts covered.
• Practical case studies or group discussions to apply the knowledge gained.
• Ongoing assessment during practical sessions.
• Hands-on project: Complete project at the end of the modules to consolidate learning.

Standards and references

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.