Cursus data Steward

Description of the training

Condensed training to acquire essential data management skills, covering data modelling, governance, data quality, architecture of database management systems, and the use of tools and good practices to ensure data security, integrity and accessibility within an organisation.

Objectives of training

At the end of this training, participants will be able to:

• Understanding the Principles of Data Governance: to 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.
• Managing Metadata and Data Flows: mastering tools and techniques for cataloguing, classifying and organizing metadata, thus facilitating their management and operation.
• Know the Standards and Data Compliance: become familiar with legal and regulatory requirements, such as the GDPR, and understand the importance of data management in accordance with these standards.
• Optimize Data Access and Security: Learn how to implement practices to protect sensitive data while ensuring its accessibility for authorized users.

Who is this training for?

The training is aimed at a wide audience, including:

• Data Analysts: Professionals who want to deepen their skills in data management and governance to improve their effectiveness in their role.
• Data Quality Managers: Data quality experts seeking to standardize processes and ensure the reliability of information.
• IT or Data Project Managers: Managers involved in data-related projects that need to understand data governance and structuring mechanisms.
• Compliance Professionals: Compliance and regulatory specialists seeking to integrate data management practices aligned with standards such as GDPR.
• Beginners in the Data field: Any person wishing to start a career in data management and governance, with structured and career-oriented training.

Prerequisites

No specific prerequisites are required.

Training programme

Day 1: Introduction to Data Governance

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

Day 2: Data quality and metadata management

• Objective: To acquire skills to ensure data quality.
• Content:
  Methods for measuring and maintaining data quality.
  Cleaning practices and data validation.
  Introduction to the management of metadata and associated tools.

Jour 3 : Politique de gestion des données et processus de gestion des données

• Objective: To establish effective policies and processes for data management.
• Content:
  Development of data management policies.
  Data life cycle management process: creation, storage, use, archiving and deletion.
  Implementation of controls to ensure data compliance.

Day 4: Data Management Tools and Platforms

• Purpose: To become familiar with the data management tools used by a Data Steward.
• Content:
  Presentation of data management tools, data quality and metadata.
  Exploration of data governance platforms: Cloud, databases, data integration tools.
  Introduction to audit and reporting tools.

Day 5: Data security and legal compliance

• Objective: To understand data security issues and legal requirements.
• Content:
  Data security policies and access management.
  Compliance with regulations such as the GDPR, the Data Protection Act.
  Implementation of security in the management of sensitive data.

Day 6: Data Management Strategy for the Enterprise

• Objective: To develop effective strategies for enterprise-wide data management.
• Content:
  Development of a data management strategy to maximize their value.
  Creation of a roadmap for data governance in the organization.
  Integration of data management into the company's overall strategy.

Day 7: Data Stewardship Tools and Practices

• Objective: To deepen the practice of Data Stewardship with advanced tools and processes.
• Content:
  Implementation of ongoing data management processes.
  Use of monitoring and data management tools: dashboard, reports, audits.
  Optimization of practices for better management of metadata and data flows.

Day 8: Case Studies and Practice

• Objective: To apply the knowledge gained through practical case studies.
• Content:
  Actual case studies on data management and governance.
  Practical workshops to simulate data management scenarios.
  Discussion of challenges and possible solutions.

Training assets

• Complete and Progressive Program: A well-defined structure ranging from fundamental concepts to advanced applications for in-depth understanding.
• Practical and Contextual Approach: Many hands-on workshops allow participants to manipulate the tools and models of generative AI in concrete contexts.
• Expertise in Point Tools: Use of the latest and most relevant frameworks and platforms for general AI (Transformers, GANs, Cloud Platforms).
• Development of a Real Project: An entire day devoted to a project of completion of the course, promoting the integration of the acquired into a practical and professional scenario.
• Ethical and Security Dimension: In-depth reflection on ethical issues, biases, and technology regulation to ensure responsible use.
• Adapted to Market Issues: Training designed to meet current business needs for innovative and efficient AI solutions.
• Support and support: Guidance by experts and provision of resources to ensure a sustainable increase in skills.

Pedagogical methods and tools used

• Live demonstrations with data science services.
• Practical workshops and real case studies in various sectors (industry, trade, health).
• Feedback: Sharing best practices and common mistakes in business.
• Simulations and tools: Using simulators for interactive workshops.

Evaluation

• End of training QCM to test the understanding of the concepts addressed.
• Practical case studies or group discussions to apply the knowledge gained.
• Ongoing evaluation during practical sessions.
• Implementation: Complete project from the end of the modules to consolidate the achievements.

Normative References

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