What we do
About our project
Data Management and Data-Driven Decision Making
Within the Department of Clinical Genetics, diverse clinical, (epi-)genomic, biochemical, and proteomics studies generate substantial datasets for both diagnostic and research purposes. When properly structured and integrated, these data contribute to the discovery of new biomarkers, early disease detection, personalized treatment selection, and a deeper understanding of disease mechanisms. Multi-disciplinarity and the use of both structured and unstructured multimodal data are increasingly crucial in healthcare. The rise of digitalization, robust infrastructure, seamless health data integration, and effective data management are essential for compliance and lay the foundation for future innovations such as AI-driven decision-making. Our mission is to ensure optimal management and utilization of structured and unstructured, multimodal clinical and experimental data. By storing, processing, and combining data in a smart way, we foster collaboration, stimulate innovation, and contribute to health gains and personalized patient care.
Within the Department of Clinical Genetics, diverse clinical, (epi-)genomic, biochemical, and proteomics studies generate substantial datasets for both diagnostic and research purposes. When properly structured and integrated, these data contribute to the discovery of new biomarkers, early disease detection, personalized treatment selection, and a deeper understanding of disease mechanisms. Multi-disciplinarity and the use of both structured and unstructured multimodal data are increasingly crucial in healthcare. The rise of digitalization, robust infrastructure, seamless health data integration, and effective data management are essential for compliance and lay the foundation for future innovations such as AI-driven decision-making. Our mission is to ensure optimal management and utilization of structured and unstructured, multimodal clinical and experimental data. By storing, processing, and combining data in a smart way, we foster collaboration, stimulate innovation, and contribute to health gains and personalized patient care.
Our research focus
Our group specializes in program and project management, data stewardship, the implementation of FAIR principles, and ethical research practices. We have established a department-wide data management framework that aligns with institutional, national, and European standards. This framework supports the integration of genomics into routine care and ensures that innovations in hereditary cancer research benefit both patients and society. Our team is guiding the transition towards a data-centric diagnostic and research environment, ensuring alignment with institutional initiatives. Our responsibilities include overseeing scientific integrity, GDPR compliance, biobanking, and data governance. In collaboration with internal and external partners, we have implemented a FAIR-aligned data management framework. Our team includes Certified Data Management Professionals (CDMP), and we actively develop tools for data cataloguing, metadata tracking, and monitoring. We also contribute to institutional initiatives in AI, digitalization, and research ethics.
We aim to be an example of innovation and excellence in data management within clinical genetics by promoting accessibility, synergy, and innovation in the collection, use, and sharing of data, our data practices will form the foundation for advanced genetic care, research, and a healthy future for our patients and society. First step toward a more data-driven department is the development of a comprehensive strategy, outlining the choices, decisions, and actions needed to achieve our goals. This strategy forms the basis of a data management program, consisting of interrelated projects designed to elevate the department. Our approach includes a clear vision, solid planning, and promotes awareness and readiness for change. Our mission and vision are closely aligned with those of the Department of Clinical Genetics and Erasmus MC, leveraging institutional tools and infrastructure.
Understanding the current organizational landscape—including staff, processes, and culture—is crucial for successful change. We utilize the DAMA-DMBOK data maturity scan to analyze data use, identify areas for improvement, and assess strengths and weaknesses. This process results in a roadmap for strengthening data governance and management, which is periodically reviewed to ensure continuous improvement.
Our data strategy is based on a thorough understanding of departmental data requirements, stakeholders, and processes. It includes measures for data quality, integrity, access, and security, while mitigating risks. The strategy is developed by the Data Governance Team, supported by a Data Governance Council, and is detailed in a Data Project Charter and business plan, covering objectives, KPIs, risks, roles, and a prioritized implementation roadmap. All relevant DAMA Data Management Framework knowledge areas are addressed to ensure comprehensive and sustainable data management.
We aim to be an example of innovation and excellence in data management within clinical genetics by promoting accessibility, synergy, and innovation in the collection, use, and sharing of data, our data practices will form the foundation for advanced genetic care, research, and a healthy future for our patients and society. First step toward a more data-driven department is the development of a comprehensive strategy, outlining the choices, decisions, and actions needed to achieve our goals. This strategy forms the basis of a data management program, consisting of interrelated projects designed to elevate the department. Our approach includes a clear vision, solid planning, and promotes awareness and readiness for change. Our mission and vision are closely aligned with those of the Department of Clinical Genetics and Erasmus MC, leveraging institutional tools and infrastructure.
Understanding the current organizational landscape—including staff, processes, and culture—is crucial for successful change. We utilize the DAMA-DMBOK data maturity scan to analyze data use, identify areas for improvement, and assess strengths and weaknesses. This process results in a roadmap for strengthening data governance and management, which is periodically reviewed to ensure continuous improvement.
Our data strategy is based on a thorough understanding of departmental data requirements, stakeholders, and processes. It includes measures for data quality, integrity, access, and security, while mitigating risks. The strategy is developed by the Data Governance Team, supported by a Data Governance Council, and is detailed in a Data Project Charter and business plan, covering objectives, KPIs, risks, roles, and a prioritized implementation roadmap. All relevant DAMA Data Management Framework knowledge areas are addressed to ensure comprehensive and sustainable data management.
Our team
Erwin Brosens, PhD, Molecular geneticist, program manager
Geert Geeven, PhD, Bioinformatics Specialist
Daniel Bink, MBA, Sector Manager
Mario Redeker, Ing, ICT Specialist
Daniel de Jong, Ing, ICT Specialist
Debora Lont, Ing, Senior Quality Advisor at Erasmus MC
Geert Geeven, PhD, Bioinformatics Specialist
Daniel Bink, MBA, Sector Manager
Mario Redeker, Ing, ICT Specialist
Daniel de Jong, Ing, ICT Specialist
Debora Lont, Ing, Senior Quality Advisor at Erasmus MC
