Data Governance Fundamentals
Once you've mastered the technical fundamentals, how do you manage this asset? This second session focuses on Data Governance: a set of practices to enhance, secure, and ensure the reliability of information.
Data governance is not just about compliance: it's a powerful lever for "making data business-ready". This session demonstrates how good data asset management can break down silos, accelerate innovation, and ensure reliable strategic decisions.
We will see how to move from "Chaos" to "Collaboration" by activating the 4 pillars of governance: Organization, Policies, Tooling, and Mapping. Through key concepts like Data Catalog (to find data) and Quality (to trust it), you will learn to transform raw data into a reliable and shared asset, essential for the success of AI and analytics projects.
What you will learn
- Define data governance, its objectives and its 4 fundamental pillars (Organization, Policies, Tooling, Mapping)
- Understand compliance challenges and risks related to poor data quality
- Master Knowledge Management concepts: Data Catalog, Business Glossary and Data Dictionary
- Implement a Data Quality approach (the 6 dimensions) and understand Master Data Management (MDM)
Prerequisites
- Basic knowledge of IT vocabulary (database, application)
- Having completed the "Data Fundamentals" training is a plus
Target audience
- Data Owners, Data Stewards, Product Owners, Business Managers and anyone wishing to enhance the company's data assets.
Training Program
2 modules to master the fundamentals
Topics covered
- →Definition: Governance as a regulation and control exercise (Business & IT)
- →The 4 pillars: Mapping, Organization, Tooling, Policies
- →Risks and compliance: GDPR, reputation and operational risks
- →Data Governance vs Data Management: understanding the complementarity
Activities
Data Governance Quiz: Test your knowledge on roles and definitions
Experience sharing: Discussion on the organization's Data Governance maturity
Topics covered
- →Knowledge Management: The role of Data Catalog, glossary and dictionary to centralize knowledge
- →Master Data Management (MDM): Identify and manage "Master Data" (Golden Record)
- →Data Quality: The 6 dimensions of quality (Uniqueness, Freshness, Completeness, etc.) and the continuous improvement cycle
- →Mapping methodologies: Top-down vs Bottom-up approaches
Activities
Quality analysis: Identify human and system factors impacting data in a business process
Prioritization: Exercise on data criticality (Value/Risk Matrix)
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote