Data Governance Fundamentals
Once the technical fundamentals are acquired, how do you manage this asset? This session focuses on Data Governance: a set of practices to enhance, secure, and make information reliable. Data governance is not limited to compliance: it is a powerful lever to "make data business-ready". This session demonstrates how good data asset management can break down silos, accelerate innovation, and make strategic decisions more reliable. 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 such as the Data Catalog (to find data) and Quality (to trust it), you will learn to transform raw data into a reliable and shared asset, essential to 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 attended the "N1 - Data Fundamentals" session is a plus.
- A computer with internet connection to access materials and collaboration tools.
Target audience
- Data Owners, Data Stewards, Product Owners, Business Managers and anyone looking to enhance their organization's data assets
Training Program
2 modules to master the fundamentals
Topics covered
- →Definition: Governance as an exercise in regulation and control (Business & IT).
- →The 4 pillars: Mapping, Organization, Tooling, Policies.
- →Risks and compliance: GDPR, reputational 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 Data Governance maturity of the organization.
Topics covered
- →Knowledge Management: The role of the Data Catalog, glossary, and dictionary to centralize knowledge.
- →Master Data Management (MDM): Identifying and managing "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