Follow our Master Data Officer course
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Experience an immersive and intensive training experience, designed to dive into practical workshops and real case studies.
Learn by doing, and develop concrete skills directly applicable to your future projects.

At the end of your career, we evaluate your acquired skills, issue certification attesting to your expertise, and accompany you to ensure your success in your professional projects.
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Description of the training

Training participants on the strategic and technical skills needed to lead data management within an organization, addressing key topics such as data governance, quality management, compliance (QM), data-drive strategy development, and data integration in decision-making, while leading data-based digital transformation teams and projects

Objectives of training

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

  • Understanding the strategic issues of data: how data can generate value for the company.
  • Mastering data governance: learning how to define and implement data management, quality, and compliance policies.
  • Developing data team leadership and management skills: building the skills needed to drive a team of data scientists, data analytics, and other data professionals.
  • Integrate data into the corporate strategy: know how to align the data strategy with business and organizational objectives.
  • Managing regulatory and ethical challenges: understanding legal obligations (GDPRs, etc.) and ethical issues around the use of data.


Who is this training for?

This training is aimed at:
  • Data managers (data managers, data scientists) who want to move to a strategic position.
  • IT managers (CIO, CTO) who want to integrate data management into the company's strategy.
  • Executives (CEO, transformation managers) interested in data governance.
  • Marketing and strategy professionals faced with the strategic use of data.
  • Digital transformation consultants interested in becoming experts in data management.

Prerequisites

No specific prerequisites are required.


Training programme

Jour 1 : Introduction à la gestion des données et rôle du CDO

  • Morning: Introduction to the role and strategy of data
    Objective: To understand the strategic importance of data to the company.
  • Content:
    The role and responsibilities of the LCO.
    The place of data in the digital transformation of the company.
    The challenges of the data for competitiveness.
  • Afternoon: Data governance and digital transformation
    Objective: To identify key data governance practices.
  • Content:
    Data governance: framework and key principles.
    The levers of digital transformation by data.
    Case study: Strategic impact of data in large companies.
Jour 2 : Gouvernance des données et gestion de la qualité des données
  • Morning: Data Governance
    Objective: To master the fundamentals of data governance.
  • Content:
    Develop and implement a data governance policy.
    Data Lineage and Data Catalogs.
    Metadata management.
  • Afternoon: Data quality management
    Objective: To assess and ensure data quality.
  • Content:
    The principles of Data Quality Management.
    Tools and processes to improve data quality.
    Case Study: Data quality challenges in an organization.
Day 3: Data Architecture and Infrastructure
  • Morning : Modern data architectures
    Objective: To understand the data architectures adapted to the strategic needs of the company.
  • Content:
    Data Lakes, Data Warehouses and Cloud Platforms.
    Choose the right architecture for data storage and analysis.
    Big Data solutions: Hadoop, Spark, etc.
  • Afternoon: Data security and risk management
    Objective: To know how to secure data and manage associated risks.
  • Content:
    Data security in cloud and on-premise environments.
    Strategies to protect against leakage and loss of data.
    Compliance with data security standards (ISO 27001, etc.).
    Case study: Data security in a multi-cloud environment.
Day 4: Data Analysis and Artificial Intelligence
  • Morning: Introduction to data analysis
    Objective: To understand the analytical tools and their application for the company.
  • Content:
    BI, Data Science, Machine Learning and IA: Overview of technologies.
    Build a data culture to promote analysis.
    Case of use of advanced analysis for process optimization.
  • Afternoon: Integration of AI and Machine Learning in the company
    Objective: To integrate AI into decision-making processes.
  • Content:
    Deployment of IA/ML algorithms in enterprises.
    IA/ML use cases for business optimization.
    Case study: Implementation of AI projects in various sectors.
Day 5: Sensitive Data Management and Regulatory Compliance
  • Morning: Regulation and data compliance
    Objective: To be able to apply the standards of compliance to sensitive data.
  • Content:
    GDPR, CCAA, and other legislation governing data management.
    Data confidentiality principles and consent management.
    Roles and responsibilities of companies with regard to personal data.
  • Afternoon: Sensitive data governance and risk management
    Objective: To master the governance of sensitive data and ensure their protection.
  • Content:
    Sensitive data management methods (personal data, health, etc.).
    Sensitive data protection strategies and secure access.
    Case study: Compliance of practices in a multinational company.
Day 6: Data team leadership and management
  • Morning: LCO Leadership and Change Management
    Objective: Develop leadership skills to drive data transformation.
  • Content:
    LCO leadership in a context of digital transformation.
    How to manage a data-based cultural change in the company.
    Communication about data strategy with other leaders.
  • Afternoon: Management of a high-performance data team
    Objective: To 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.
Day 7: Data-driven Business and value creation with data
  • Morning: Data recovery strategy
    Objective: To learn to develop a strategy for valuing data to maximize cost-effectiveness.
  • Content:
    Define a data monetization strategy.
    Economic models around data (sale, partnership, derivatives).
    Data governance and exploitation strategy to optimize performance.
  • Afternoon: Optimization of processes with data
    Objective: To learn how to optimize internal processes through data exploitation.
  • Content:
    Use of data to improve decision-making and optimize processes.
    Creation and monitoring of KPI to measure the performance of data initiatives.
    Case Study: How data improves process efficiency.
Day 8: Synthesis and Implementation
  • Morning: Revision and development of a strategic plan
    Objective: To synthesize the concepts learned and develop a data governance plan.
  • Content:
    Review of key points and good data management practices.
    Development of a strategic data management and valuation plan in an organization.
    Discussion on implementation challenges.
  • Afternoon: Presentation of the projects and closing
    Objective: To implement training outcomes and obtain feedback.
  • Content:
    Presentation of the group projects on governance and data strategy.
    Feedback and discussion with trainers.
    Conclusion of training and vocational prospects of the LCO.


Training assets

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 practical workshops allow participants to manipulate tools and models in concrete contexts.
  • Expertise in Pointe Tools: Using the most recent and relevant frameworks and 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 Challenges: Training designed to meet the current needs of companies for innovative and efficient 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 governance 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.

Modalities

Inter-company or remote
Intra-enterprise

Inter-company or remote

Duration:8 days

Price:€7000

More details Contact us

Intra-enterprise

Duration and program can be customized according to your company's specific needs

More details Contact us
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