Data Manager Program

Course Syllabus

Day 1: Introduction to Data Management and the CDO Role

• Morning: Introduction to the role and data strategy
  Objective: Understand the strategic importance of data for the company.
• Content:
  The CDO's role and responsibilities.
  The place of data in the company's digital transformation.
  The stakes of data for competitiveness.
• Afternoon: Data governance and digital transformation
  Objective: Identify key data governance practices.
• Content:
  Data governance: framework and key principles.
  Levers of data-driven digital transformation.
  Case study: Strategic impact of data in large enterprises.

• Morning: Data governance
  Objective: Master the fundamentals of data governance.
• Content:
  Define and implement a data governance policy.
  Data lineage and data catalogs.
  Metadata management.
• Afternoon: Data quality management
  Objective: Know how to assess and ensure data quality.
• Content:
  Principles of Data Quality Management.
  Tools and processes to improve data quality.
  Case study: Data quality challenges in an organization.

• Morning: Modern data architectures
  Objective: Understand data architectures suited to the company's strategic needs.
• Content:
  Data Lakes, Data Warehouses, and Cloud platforms.
  Choosing the right architecture for storing and analyzing data.
  Big Data solutions: Hadoop, Spark, etc.
• Afternoon: Data security and risk management
  Objective: Secure data and manage associated risks.
• Content:
  Data security in Cloud and on‑prem environments.
  Protection strategies against data leakage and loss.
  Compliance with data security standards (ISO 27001, etc.).
  Case study: Securing data in a multi‑cloud environment.

• Morning: Introduction to data analytics
  Objective: Understand analytics tools and their application for the business.
• Content:
  BI, Data Science, Machine Learning, and AI: technology landscape.
  Building a data culture to promote analytics.
  Use cases of advanced analytics for process optimization.
• Afternoon: Integrating AI and Machine Learning in the enterprise
  Objective: Integrate AI into decision‑making processes.
• Content:
  Deploying AI/ML algorithms in companies.
  AI/ML use cases for operational optimization.
  Case study: Implementing AI projects across sectors.

• Morning: Data regulations and compliance
  Objective: Apply compliance standards to sensitive data.
• Content:
  GDPR, CCPA, and other data‑management legislation.
  Data privacy principles and consent management.
  Company roles and responsibilities regarding personal data.
• Afternoon: Governance of sensitive data and risk management
  Objective: Master governance of sensitive data and guarantee its protection.
• Content:
  Methods for managing sensitive data (personal data, health data, etc.).
  Strategies for protecting sensitive data and securing access.
  Case study: Bringing practices into compliance in a multinational company.

• Morning: CDO leadership and change management
  Objective: Develop leadership skills to drive data transformation.
• Content:
  The CDO's leadership in a digital‑transformation context.
  How to lead a data‑centric cultural change in the company.
  Communicating the data strategy to fellow executives.
• Afternoon: Managing a high‑performing data team
  Objective: Build and manage an effective data team.
• Content:
  Identify, recruit, and manage talent in Data Science, Data Engineering, etc.
  Develop a multidisciplinary team (data scientists, analysts, engineers).
  Collaboration with other departments (IT, marketing, finance).
  Case study: Managing a data team in a complex organization.

• Morning: Data value strategy
  Objective: Develop a data value strategy to maximize ROI.
• Content:
  Define a data monetization strategy.
  Business models around data (sales, partnerships, derivative products).
  Governance and exploitation strategy to optimize performance.
• Afternoon: Process optimization with data
  Objective: Optimize internal processes through data use.
• Content:
  Use data to improve decision‑making and optimize processes.
  Create and track KPIs to measure the performance of data initiatives.
  Case study: How data improves process efficiency.

• Morning: Review and strategic plan
  Objective: Synthesize concepts learned and develop a data governance plan.
• Content:
  Review of key points and best practices in data management.
  Develop a strategic plan for managing and leveraging data in an organization.
  Discussion of implementation challenges.
• Afternoon: Project presentations and closing
  Objective: Put learning into practice and receive feedback.
• Content:
  Group project presentations on data governance and strategy.
  Feedback and discussion with instructors.
  Course conclusion and CDO career outlook.