Data governance course in avionics
About our
The Data Governance in Avionics course focuses on the effective management of data within the aeronautical field, covering everything from the collection and storage of information critical to aircraft operations to its analysis and security. It delves into the application of data policies, quality standards, and regulatory compliance (such as FAA and EASA regulations) to ensure the integrity and reliability of data in avionics systems. Tools and methodologies for master data management, privacy protection, and data-driven decision-making are studied within the aircraft lifecycle. The course trains professionals to manage data flow in areas such as predictive maintenance, route optimization, and operational safety, using data analytics and visualization platforms. It delves into the implementation of data management systems, data auditing, and the design of governance strategies aligned with the strategic objectives of aeronautical companies. The training prepares students for roles such as Data Governance Manager, Avionics Data Analyst, Data Systems Engineer, and Regulatory Compliance Specialist in the aerospace industry.
Data governance course in avionics
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
449 $
Competencies and results
What you will learn
1. Data Governance for Avionics: Fundamentals and Applications
1. Analyze the fundamental principles of Data Governance in the context of avionics.
2. Understand the importance of data quality and its impact on the safety and efficiency of air operations.
3. Identify the different types of data used in avionics and their sources.
4. Learn to establish policies and procedures for data management in avionics.
5. Explore best practices for data security and privacy protection.
6. Apply data analysis tools and techniques to improve decision-making and process optimization.
7. Study real-world use cases of Data Governance in the aviation industry.
8. Learn to implement Data Governance solutions to comply with aviation regulations and standards. 9. Develop skills for managing Data Governance projects in avionics.
10. Evaluate the impact of Data Governance on cost reduction, efficiency improvement, and increased safety in air operations.
2. Data Governance in Avionics: Domain and Control.
2. Data Governance in Avionics: Domain and Control.
- Understand the architecture of aeronautical data systems, including protocols and data buses (ARINC, CAN, Ethernet).
- Learn about data lifecycle management in avionics: acquisition, storage, processing, and analysis.
- Master the tools and techniques for data validation and verification in critical avionics systems.
- Analyze and apply aeronautical data security regulations (DO-178C, ARP4754A).
- Implement cybersecurity strategies for data protection in avionics systems.
- Use specialized software for avionics data analysis and visualization.
- Develop skills for resolving problems related to data integrity and quality.
- Explore the use of artificial intelligence and machine learning in avionics data analysis.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Data Governance in Avionics: Data Management for Aviation Safety
- Understand the fundamentals of **Data Governance** in the context of **Avionics**.
- Identify the different types of data critical to **aviation safety** and their sources.
- Learn the principles of **data management** and its application in avionics.
- Study the processes of **data quality**, including validation, cleansing, and verification.
- Analyze the roles and responsibilities related to **Data Governance** in aviation organizations.
- Become familiar with the **regulations and standards** relevant to data management in the aviation industry.
- Explore the technologies and tools for **Data Governance** in avionics (databases, software).
- Apply **Data Governance** for informed decision-making and safety improvement.
- Evaluate the risks associated with a lack of **Data Governance** and its consequences.
- Develop skills for the **implementation and maintenance of an effective Data Governance system** in avionics environments.
aviation.
5. Data Governance in Avionics: Ensuring Quality and Regulatory Compliance.
- Understand the fundamentals of **Data Governance** and its importance in the aeronautical sector.
- Identify and analyze the different types of data generated in **avionics**.
- Learn to establish policies and procedures for data management, including **collection, storage, and processing**.
- Understand the principles of **data quality** and how to ensure the accuracy, integrity, and consistency of information.
- Become familiar with the aeronautical industry regulations and standards relevant to **Data Governance**, such as those of the FAA and EASA.
- Implement measures to ensure **regulatory compliance** and avoid penalties.
- Use tools and technologies for data management and analysis in **avionics**.
- Learn to identify and mitigate the risks associated with data management, including **data security**. Information and privacy**.
Develop skills for data-driven decision-making and improve operational efficiency.
Apply data governance concepts to improve safety, reliability, and profitability in aeronautical operations.
6. Data Governance in Avionics: Strategies and Tools for Innovation
6. **Data Governance in Avionics: Strategies and Tools for Innovation**
Understand the fundamentals of data governance applied to avionics.
Identify the specific challenges of data management in the aeronautical environment.
Analyze strategies for creating data policies and quality standards.
Evaluate tools and technologies for managing, storing, and protecting avionics data.
Implement data security and privacy processes, complying with industry regulations.
Apply data governance to drive innovation in aircraft design, operation, and maintenance.
Use data analytics for continuous improvement and evidence-based decision-making.
Develop a data governance plan adaptable to the specific needs of an avionics project.
Who this program is for:
Data governance course in avionics
- Engineers with degrees in Aerospace, Mechanical, Industrial, Automation, or related fields.
- Technical experts from OEM rotorcraft/eVTOL, MRO personnel, industry consultants, and members of technology centers.
- Flight Testing, certification, avionics, control, and flight dynamics professionals seeking specialization.
- Personnel from regulatory bodies/authorities and professionals operating in the UAM/eVTOL sector, interested in acquiring compliance skills.
Recommended requirements: Knowledge of aerodynamics, control systems, and structures. A B2+/C1 level of Spanish is required. We offer bridging tracks.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
2.1 Introduction to Avionics and the Importance of Data Governance
2.2 Fundamental Principles of Data Governance
2.3 Data Lifecycle in Avionics
2.4 Key Legislation and Standards in Data Management
2.5 Data Architecture and Modeling for Avionics Systems
2.6 Security and Data Protection Concepts in Aviation
2.7 Roles and Responsibilities in Data Governance
2.8 Emerging Technologies Applied to Data Management in Avionics
2.9 Case Studies: Best Practices in Data Governance
2.10 Challenges and Future Trends in Data Governance for Avionics
3.1 Defining Data Governance Policies and Procedures
3.2 Establishing Data Standards and Governance
3.3 Managing Metadata and Data Dictionaries
3.4 Data Quality Control and Validation
3.5 Data Integrity and Change Management
3.6 Data Access and Permissions Management
3.7 Data Auditing and Compliance Monitoring
3.8 Conflict Resolution and Exception Management
3.9 Tools and Platforms for Data Governance
3.10 Implementing a Data Governance Framework
4.1 Importance of Aviation Safety in Data Governance
4.2 Identifying Risks and Threats in Avionics Data
4.3 Designing Security Controls and Data Protection
4.4 Security Incident Management and Response
4.5 Ensuring Data Confidentiality, Integrity, and Availability (CIA)
4.6 Security Monitoring and Threat Detection
4.7 Compliance with Aviation Security Standards
4.8 Protecting Sensitive and Personal Data
4.9 Analysis Vulnerabilities and Penetration Testing
4.10 Resilience and Disaster Recovery Strategies in Data Management
5.1 Importance of Data Quality in Aviation
5.2 Implementation of Data Quality Processes
5.3 Data Validation and Verification in Avionics
5.4 Regulatory Compliance in Data Management
5.5 Regulatory Compliance Management
5.6 Data Audits and Certifications
5.7 Nonconformity Management and Corrective Actions
5.8 Maintaining Long-Term Data Quality
5.9 Tools and Technologies for Quality and Compliance
5.10 Best Practices in Quality Assurance and Regulatory Compliance
6.1 Data Governance Strategies for Innovation
6.2 Data Analytics for Innovation in Avionics
6.3 Artificial Intelligence and Machine Learning in Avionics
6.4 Big Data and its Application in Aviation
6.5 Data Visualization and Analysis Strategies
6.6 Platforms and Tools for Data-Driven Innovation
6.7 Data Culture and Fostering Innovation
6.8 Protecting Intellectual Property in Data
6.9 Case Studies: Data-Driven Innovation in Avionics
6.10 The Future of Innovation and Data Governance in Aviation
7.1 Optimizing the Performance of Air Data Systems
7.2 Real-Time Data Management
7.3 Data Storage and Retrieval Strategies
7.4 Data Security in High-Availability Environments
7.5 Monitoring and Managing the Performance of Data Systems
7.6 Protecting Air Data Systems Against Cyber Threats
7.7 Implementing Cybersecurity in Avionics
7.8 Disaster Recovery and Business Continuity Business
7.9 Safety Audits and Risk Assessments
7.10 Future Trends in Air Data Optimization and Security
8.1 Introduction to the World of Air Data
8.2 Generation and Collection of Flight Data
8.3 Flight Data Analysis for Decision Making
8.4 Tools and Technologies for Air Data Analysis
8.5 Applications of Air Data in Accident Investigation
8.6 Use of Air Data for Maintenance Planning
8.7 Integration of Air Data with Management Systems
8.8 Ethics and Privacy in Air Data Handling
8.9 Case Studies: Deciphering the Flight of Data
8.10 Future Trends and Challenges in Air Data Analysis
2.2 Fundamentals of Data Management in Avionics: Conceptual Framework
2.2 Data Structures in Avionics: Types and Sources
2.3 Roles and Responsibilities: Data Governance Teams
2.4 Data Policies: Creation and Application
2.5 Data Access Control and Security
2.6 Data Auditing and Monitoring
2.7 Metadata and Data Dictionaries: Organization and Cataloging
2.8 Data Lifecycle Management
2.9 Data Governance and its Impact on Avionics
2.20 Case Study: Implementation of Data Domain and Control
3.3 Strategic Planning of Data Governance in Avionics
3.2 Aligning Data Governance with Business Objectives in Avionics
3.3 Designing the Data Governance Architecture for Avionics
3.4 Roles and Responsibilities in Avionics Data Governance
3.5 Establishing Data Policies and Standards in Avionics
3.6 Managing the Data Lifecycle in Aeronautical Environments
3.7 Data and Metadata Modeling in Avionics
3.8 Implementing Data Governance Tools and Technologies in Avionics
3.9 Evaluating and Measuring the Performance of Data Governance in Avionics
3.30 Continuous Improvement and Adaptation of the Data Governance Strategy in Avionics
4.4 Data Governance for Aviation Safety: Fundamentals of Data Protection
4.2 Identifying Risks and Vulnerabilities in Avionics
4.3 Implementing Access Controls and Data Security
4.4 Data Governance and the Integrity of Avionics Systems
4.5 Monitoring and Detecting Anomalies in Aviation Data
4.6 Incident Response and Data Recovery in Aviation
4.7 Regulatory Compliance and Data Protection in the Aviation Sector
4.8 Data Security Audits and Assessments in Avionics
4.9 Personal Data Protection and Privacy in Aviation
4.40 The Future of Data Security and Avionics: Trends and Challenges
5.5 Introduction to Data Governance in Avionics
5.5 Fundamentals of Aeronautical Data Management
5.3 Regulatory Framework and Industry Standards
5.4 Applications of Data Governance in Avionics Systems
5.5 Benefits of Data Governance: Security, Efficiency, and Compliance
5.6 Challenges in Implementing Data Governance in Aviation
5.7 Key Technologies: Databases, Analytics, and Visualization
5.8 Case Studies: Practical Examples of Data Governance in Avionics
5.9 Metrics and Key Performance Indicators (KPIs) for Data Governance
5.50 The Future of Data Governance in Avionics: Trends and Opportunities
5.5 Principles of Data Governance: Roles and Responsibilities
5.5 Data Architecture in Avionics Systems
5.3 Data Modeling and Design for Aviation
5.4 Data Access Control and Security
5.5 Data Quality Data cleansing, validation, and verification
5.6 Data lifecycle management
5.7 Metadata: managing and using information about data
5.8 Auditing and regulatory compliance in data governance
5.9 Data domain strategies: centralization, decentralization, and federation
5.50 Tools and technologies for data domain and control
3.5 Strategic planning for data governance implementation
3.5 Designing a data governance program in avionics
3.3 Selecting and implementing tools and platforms
3.4 Change management: training and adopting new practices
3.5 Integrating data governance with existing systems
3.6 Implementing data governance policies and procedures
3.7 Monitoring and evaluating the performance of the data governance program
3.8 Continuous improvement: optimization and adaptation to change
3.9 Risk management in data governance implementation
3.50 Case studies: successful implementation in different environments Avionics
4.5 Importance of Aviation Safety in Data Management
4.5 Identifying and Mitigating Data Security Risks
4.3 Cybersecurity in Avionics Systems: Protecting Sensitive Data
4.4 Safety Incident Management and Emergency Response
4.5 Data Analysis for Accident and Incident Prevention
4.6 Using Data Governance to Improve Safety
4.7 Integrating Data from Multiple Sources for Safety Analysis
4.8 Compliance with Aviation Safety Regulations
4.9 Strategies for Flight Information Management
4.50 Risk Simulation and Safety Scenarios
5.5 Importance of Data Quality for Regulatory Compliance
5.5 Data Quality Standards in Aviation (DO-578C, etc.)
5.3 Compliance with Data Privacy Regulations (GDPR, etc.)
5.4 Audits and Compliance Reviews in Avionics
5.5 Documentation Management and Data Recording
5.6 Implementing Data Quality Controls
5.7 Tools and Techniques for Data Verification and Validation
5.8 Impact of Data Quality on Safety and Efficiency
5.9 Examples of Non-Conformance and Their Impact
5.50 Strategies for Continuous Quality Improvement and Compliance
6.5 The Role of Data Governance in Aeronautical Innovation
6.5 Predictive Analytics and Big Data in Avionics
6.3 Artificial Intelligence and Machine Learning in Data Management
6.4 Developing New Data-Driven Products and Services
6.5 Strategies for Monetizing Aeronautical Data
6.6 Tools and Platforms for Data-Driven Innovation
6.7 Fostering a Data-Driven Innovation Culture
6.8 Protecting Intellectual Property in Innovation
6.9 Collaboration and Innovation Ecosystems
6.50 Case Studies of Successful Innovation in Avionics
7.5 Optimizing the Performance of Aeronautical Data Systems
7.5 Strategies for In-Flight Data Security
7.3 Encryption and Protection of Data in Transit and at Rest
7.4 Real-Time Data Security Monitoring and Analysis
7.5 Security Incident Detection and Response
7.6 Implementing a Business Continuity Plan
7.7 Data Recovery and Disaster Resilience
7.8 Security Audits and Vulnerability Assessments
7.9 Privacy Management and Personal Data Protection
7.50 Continuous Security Improvement and Data Optimization
8.5 The Aeronautical Data Lifecycle
8.5 Flight Data Collection and Processing
8.3 Aeronautical Data Storage and Management
8.4 Flight Data Analysis and Visualization
8.5 Using Data to Improve Operational Efficiency
8.6 Data Governance in Decision-Making
8.7 Trends in aviation data analytics
8.8 Challenges in aviation data management
8.9 The future of data governance in aviation
8.50 Conclusions and next steps
6.6. Trends in Aviation Innovation
6.2. Identifying Opportunities for Data-Driven Innovation
6.3. Implementing Cutting-Edge Technologies in Data Governance
6.4. Strategies for Creating Value from Data
6.5. Innovation in Aircraft Performance Data Management
6.6. Tools and Platforms for Data Innovation
6.7. Analyzing Industry Innovation Success Stories
6.8. Protecting Innovation and Intellectual Property
6.9. Future Trends in Data Governance Innovation
6.60. Integrating Data for Strategic Decision-Making
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).