Data Governance in Smart Cities Course
About our
The Data Governance in Smart Cities course explores the strategic management of data for the development of smart cities. It focuses on the implementation of policies, standards, and frameworks to ensure the quality, security, and ethical use of data generated in urban environments, considering privacy and transparency. It includes the analysis of data sources such as IoT sensors, mobility platforms, and urban management systems, and the application of data analysis tools for evidence-based decision-making, promoting sustainability and efficiency in the management of public services. The course prepares participants to design and implement data governance strategies, fostering collaboration among different stakeholders (government, businesses, citizens) and compliance with regulations such as the GDPR. Topics such as cybersecurity, data integration, and information visualization are addressed, enabling the optimization of urban services, the improvement of quality of life, and the promotion of innovation in the field of smart cities.
Data Governance in Smart Cities Course
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
780 $
Competencies and results
What you will learn
1. Data Governance: Key to Successful Smart Cities
- Understand the fundamentals of data governance and its importance in the context of Smart Cities.
- Identify the key challenges in data governance for smart cities, including privacy, security, and data quality.
- Explore the regulations and legal frameworks relevant to data governance in Smart Cities, such as GDPR and other local regulations.
- Analyze different data governance models and strategies, including roles and responsibilities within a Smart City.
- Evaluate the technologies and tools for data management in Smart Cities, such as open data platforms and data analytics solutions.
- Develop skills for planning and implementing an effective data governance system in a Smart City environment.
- Study success stories and lessons learned from Smart Cities that have implemented data governance strategies.
- Apply the acquired knowledge to address specific data governance problems in various scenarios. of Smart City, such as traffic management, energy efficiency, and public safety.
2. Deciphering Data Governance: Your Path to Smart Cities
- Understand the fundamentals of data governance and its importance in the context of smart cities.
- Identify and analyze relevant data sources for smart cities, including sensor data, geospatial data, and citizen data.
- Explore different data governance models and frameworks, such as decentralized data governance and centralized data governance.
- Learn to establish policies and procedures for data management, including data quality, privacy, security, and access.
- Evaluate available data governance tools and technologies, such as data management platforms and data analytics tools.
- Apply data governance principles to practical smart city case studies, such as traffic management, energy optimization, and public utility delivery.
- Understand the challenges and opportunities of data governance in the context of smart cities, including the Data interoperability, data ethics, and citizen participation.
Develop communication and collaboration skills in the field of data governance, including data presentation and interaction with different stakeholders.
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. Mastering Data Governance: The Future of Smart Cities
- Understand the fundamentals of data governance and its crucial importance in the context of Smart Cities.
- Identify and analyze the challenges of data governance, including privacy, security, quality, and ethics.
- Explore data governance frameworks and models applicable to Smart Cities, such as GDPR and other relevant standards.
- Learn to establish effective policies and procedures for data lifecycle management in a Smart City environment.
- Develop skills in data quality management, the integration of diverse data sources, and interoperability.
- Evaluate and select appropriate technologies and tools for data governance in Smart Cities, including data platforms, databases, and analytics tools.
- Analyze successful Smart City case studies and learn from best practices in data governance.
- Understand the
- The role of data governance in optimizing urban services, improving efficiency, and enabling data-driven decision-making.
- Addressing the legal and regulatory aspects related to data governance in Smart Cities, including the protection of personal data and transparency.
- Preparing for the future of Smart Cities by acquiring knowledge and skills in data governance that drive innovation and sustainable growth.
5. Data Governance: Driving Urban Intelligence and Success in Smart Cities
- Understand the fundamentals of data governance and its importance in the context of Smart Cities.
- Identify the challenges and opportunities in urban data management.
- Explore the policies and regulatory frameworks related to data privacy and security in smart urban environments.
- Analyze the data lifecycle, from collection to disposal, in the context of Smart Cities.
- Use tools and technologies for urban data management and analysis.
- Apply data governance to improve decision-making, operational efficiency, and quality of life in Smart Cities.
- Evaluate the impact of data governance on the sustainability and resilience of smart cities.
- Design and implement data governance strategies tailored to the specific needs of a Smart City.
- Understand the role of ethics and Responsibility in urban data governance.
Identify future trends and challenges in data governance for Smart Cities.
6. Implementing Data Governance: Successfully Transforming Smart Cities
6. **Implementing Data Governance: Successfully Transforming Smart Cities**
- Understand the fundamentals of data governance and its importance in the context of Smart Cities.
- Identify the key challenges in data management and governance in smart urban environments.
- Learn best practices for data collection, storage, processing, and analysis in Smart Cities.
- Explore the different data architectures and technologies used in data governance, including databases, data lakes, and data warehouses.
- Develop skills for implementing data governance policies and standards, such as data quality, security, and privacy.
- Analyze case studies of successful Smart Cities and how data governance has contributed to their success.
- Design strategies for managing the data lifecycle, from creation to disposal. ensuring the integrity and usefulness of data over time.
Evaluate the impact of data governance on decision-making, operational efficiency, and service delivery in Smart Cities.
Explore the use of data analytics tools and technologies, such as artificial intelligence and machine learning, to optimize data governance.
Develop a data governance plan for a Smart City, including policies, roles, responsibilities, and performance metrics.
Who this program is for:
Data Governance in Smart Cities Course
Here is the information for the Smart Cities Data Governance course:
- Professionals in technology, engineering, data science, or related fields in Smart Cities.
- Data analysts, data architects, Big Data and Business Intelligence managers.
- Project managers, consultants, and decision-makers involved in the planning, implementation, and management of Smart City projects.
- Government officials and public administration personnel interested in the management and strategic use of data for smart urban development.
Recommended requirements: Basic knowledge of databases, data analysis, and familiarity with the Smart Cities concept.
- 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.
1.1 Definition and Key Concepts of Data Governance
1.2 Importance of Data Governance in the Context of Smart Cities
1.3 Essential Components of Data Governance: Policies, Roles, and Responsibilities
1.4 Legal and Regulatory Framework for Data Governance in Urban Environments
1.5 Types of Data Relevant to Smart Cities and Their Management
1.6 Principles of Data Privacy and Security in Smart Cities
1.7 Benefits of Effective Data Governance: Efficiency, Transparency, and Citizen Participation
1.8 Common Challenges in Implementing Data Governance
1.9 Technologies and Tools for Data Management in Smart Cities
1.10 Case Studies: Examples of Success and Failure in Data Governance
2.2 Introduction to Data Governance: Definition and Key Concepts
2.2 Importance of Data Governance in the Context of Smart Cities
2.3 Framework for Data Governance: Fundamental Principles
2.4 Organizational Structure: Roles and Responsibilities in Data Governance
2.5 Data Policies: Creation and Management for Smart Cities
2.6 Data Quality: Measurement, Monitoring, and Continuous Improvement
2.7 Data Security: Protection and Privacy in the Urban Environment
2.8 Regulatory Compliance: Relevant Laws and Regulations
2.9 Ethics in Data Use: Transparency and Accountability
2.20 Case Studies: Examples of Successful Data Governance Implementation
3.3 Definition and Key Concepts of Data Governance
3.2 Importance of Data Governance in Smart Cities
3.3 Benefits of Data Governance: Efficiency, Transparency, Security
3.4 The Data Lifecycle and its Management
3.5 Roles and Responsibilities in Data Governance
3.6 Fundamental Principles of Data Governance
3.7 Common Challenges in Data Governance
3.8 The Future of Data Governance and Smart Cities
2.3 Legal and Regulatory Framework for Data Protection (GDPR, CCPA)
2.2 Privacy Laws and their Impact on Smart Cities
2.3 International Standards for Data Governance
2.4 Specific Regulatory Framework for Smart City Data
2.5 Regulatory Compliance and its Importance
2.6 Compliance Audits and Assessments
2.7 The Role of Data Protection Authorities
2.8 Future Trends in Data Regulation
3.3 Design 3. Data Governance Strategies
3.2 Defining Data Policies and Procedures
3.3 Data Quality Management
3.4 Decentralized vs. Centralized Data Governance
3.5 Data Security Strategies
3.6 Data Interoperability Strategies
3.7 Strategy Planning and Execution
3.8 Strategy Evaluation and Continuous Improvement
4.3 Applications of Data Governance in Various Areas
4.2 Data Governance in Intelligent Transportation
4.3 Data Governance in Waste Management
4.4 Data Governance in Public Safety
4.5 Data Governance in Energy and Environment
4.6 Data Governance in Health and Wellbeing
4.7 Successful Case Studies in Smart Cities
4.8 Challenges and Opportunities in Application Implementation
5.3 The Concept of Urban Intelligence
5.2 The Role of Data Governance in Urban Intelligence
5.3 Key Performance Indicators (KPIs) in Smart Cities
5.4 Data-Driven Decision Making
5.5 Predictive Analytics and Modeling in Smart Cities
5.6 The Impact of Data Governance on Quality of Life
5.7 Measuring the Success of Smart Cities
5.8 Future Trends in Urban Intelligence
6.3 Planning the Implementation of Data Governance
6.2 Selecting Tools and Technologies
6.3 Managing Organizational Change
6.4 Training and Development of Personnel
6.5 Integrating Systems and Data
6.6 Monitoring and Evaluating Performance
6.7 Risk Management in Implementation
6.8 Lessons Learned and Best Practices
7.3 Fundamentals of Data Management
7.2 Principles of Data Privacy and Security
7.3 Data Quality Management
7.4 Cataloging and Metadata
7.5 Data Modeling and Design
7.6 Data Integration and Interoperability
7.7 Best Practices Data Governance
7.8 Smart City Case Studies
8.3 The Value of Data Governance for Smart Cities
8.2 Data Governance as an Engine for Innovation
8.3 The Economic Impact of Data Governance
8.4 Data Governance and Citizen Participation
8.5 The Future of Data Governance in a Connected World
8.6 Ethics and Data Governance
8.7 Sustainability and Data Governance
8.8 Building a Smart and Sustainable Future
4.4 Introduction to Data Governance
4.2 What is data and why is it important?
4.3 Key Concepts: Quality, Privacy, Security
4.4 Conceptual Framework of Data Governance
4.5 Stakeholders and Responsibilities
4.6 Relevant Policies and Regulations
4.7 Data Lifecycle
4.8 Challenges and Opportunities in Data Governance
2.4 Designing Data Architectures for Smart Cities
2.2 Data Sources in Urban Environments
2.3 Storing and Managing Big Data
2.4 Databases for Smart Cities: Types and Uses
2.5 Data Integration: ETL and APIs
2.6 Security and Protection of the Data Architecture
2.7 Scalability and Performance
2.8 Examples of Successful Data Architectures
3.4 Establishing Effective Strategies
3.2 Defining Objectives and Metrics
3.3 Data Governance Models: Centralized, Decentralized, Hybrid
3.4 Developing Policies and Procedures
3.5 Managing Data Quality
3.6 Data Access and Security Management
3.7 Monitoring and Compliance
3.8 Evaluation and Continuous Improvement
4.4 Implementation Planning
4.2 Tools and Technologies for Data Governance
4.3 Implementing a Governance Framework
4.4 Roles and Responsibilities in Practice
4.5 Change Management and Adoption
4.6 Ensuring Data Quality During Implementation
4.7 Monitoring and Controlling Implementation
4.8 Measuring Success and Optimization
5.4 Data for Decision Making
5.2 Data Analysis for Urban Intelligence
5.3 Data Visualization and Dashboards
5.4 Data Applications in Key Areas: Transportation, Energy, Security
5.5 Predictive Modeling and Scenario Analysis
5.6 Artificial Intelligence and Machine Learning in Smart Cities
5.7 The Value of Data in Urban Planning
5.8 Ethics and Responsibility in Data Use Urban Cities
6.4 Global Case Studies
6.2 Analysis of Specific Examples in Different Cities
6.3 Identifying Lessons Learned and Best Practices
6.4 Key Success Factors
6.5 Innovative Applications of Data Governance
6.6 Common Challenges and How to Overcome Them
6.7 The Impact on Citizens’ Quality of Life
6.8 The Future of Data Governance in Smart Cities
7.4 Current Legal Framework and Regulations
7.2 Data Protection Regulations: GDPR, CCPA
7.3 Industry Standards and Best Practices
7.4 Compliance and Certification
7.5 Data Privacy and Security in Smart Cities
7.6 Citizens’ Rights and Transparency
7.7 The Role of Ethics in Data Governance
7.8 Future Regulatory Trends
8.4 Evolution of the Role of Data Governance
8.2 The Impact in the development of Smart Cities
8.3 The future of data governance
8.4 Necessary skills and competencies
8.5 The value of data in urban innovation
8.6 Emerging challenges and opportunities
8.7 The role of collaboration and the ecosystem
8.8 Conclusions and next steps
5.5 What are Smart Cities? Definition and evolution.
5.5 Key components of a Smart City: infrastructure, technologies, and data.
5.3 Importance of data in the development of Smart Cities.
5.4 The role of Data Governance: definition and scope.
5.5 Benefits of Data Governance in Smart Cities: efficiency, sustainability, quality of life.
5.6 Challenges and opportunities in the implementation of Smart Cities.
5.7 Legal and ethical framework for data management in smart urban environments.
5.8 Examples of successful Smart Cities and their approach to Data Governance.
5.9 Future trends in the development of Smart Cities and Data Governance.
5.50 Risk and opportunity assessment: SWOT matrix for data governance in Smart Cities.
6.6 Definition and objectives of data governance.
6.2 Importance of data governance in the context of Smart Cities.
6.3 Key benefits of effective data governance.
6.4 Common challenges in implementing data governance.
6.5 Introduction to the concepts of open data and privacy.
6.6 The role of data in urban digital transformation.
6.7 Data structure and organization in Smart Cities.
6.8 Case studies: examples of successful data governance.
2.6 Legal framework for the protection of personal data (GDPR, CCPA, etc.).
2.2 Specific laws and regulations for Smart Cities.
2.3 Legal considerations regarding open data and access to information.
2.4 Legal responsibilities of entities involved in data management.
2.5 Regulatory compliance in data governance. 2.6 Legal Aspects of Cybersecurity in Smart Cities
2.7 Citizens’ Rights and Transparency in Data Use
2.8 Updates and Changes in the Legal Framework for Smart Cities
3.6 Designing Data Governance Policies
3.2 Defining Roles and Responsibilities in Data Management
3.3 Developing Data Quality Management Strategies
3.4 Implementing Data Standards and Best Practices
3.5 Managing the Data Lifecycle
3.6 Strategies for Data Security and Privacy
3.7 Data Governance Maturity Models
3.8 Evaluating and Continuously Improving Governance Strategies
4.6 Applications of Data Governance in Urban Mobility
4.2 Use of Data in Traffic and Public Transportation Management
4.3 Applications of Data Governance in Public Safety 4.4 Implementation of data in the management of public services (water, energy, etc.).
4.5 Use of data in urban planning and sustainable development.
4.6 Data applications in waste and environmental management.
4.7 Case studies of success in different cities.
4.8 Challenges and opportunities in application implementation.
5.6 The role of data in urban intelligence.
5.2 Data collection and analysis for decision-making.
5.3 Use of artificial intelligence and machine learning in Smart Cities.
5.4 Key performance indicators (KPIs) for evaluating urban intelligence.
5.5 Data visualization and information analysis.
5.6 Platforms and tools for urban data management.
5.7 The impact of urban intelligence on improving quality of life.
5.8 The future of data and urban intelligence.
6.6 Planning and designing the implementation of data governance.
6.2 Selecting technologies and tools.
6.3 Developing a step-by-step implementation plan.
6.4 Change management and staff training.
6.5 Integrating data from different sources.
6.6 Monitoring and evaluating implementation performance.
6.7 Scalability and adaptation to future needs.
6.8 Best practices and lessons learned during implementation.
7.6 Fundamentals of data quality.
7.2 Principles of privacy and data protection.
7.3 Data standards and formats.
7.4 Metadata management.
7.5 Data integration and interoperability.
7.6 Data security and cybersecurity.
7.7 Best practices in open data management.
7.8 Data governance framework: roles and responsibilities.
8.6 The role of governance in the success of Smart Cities.
8.2 How data governance drives innovation.
8.3 Governance as a catalyst for citizen participation.
8.4 Governance and urban sustainability.
8.5 The importance of governance in urban resilience.
8.6 Case studies of successful cities.
8.7 The future of data governance in Smart Cities.
8.8 Challenges and opportunities for data governance.
- 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”).