One of the most requested mandates that I was asked to help with is how best to bring these functions together and create an effective operating model to deliver the organization’s goals.
To effectively promote the roles and operational model of the described teams, the following strategies can be implemented:
1. Interdepartmental Collaboration and Communication
Data Wrangling Team:
• Internal Workshops and Seminars: Organize regular workshops to educate other teams on the importance of data culture and how it supports the challenges.
• Joint Projects: Initiate projects that require collaboration between data wranglers and other teams to showcase the benefits of a robust data coordination framework.
Programme Management Unit:
• Progress Reviews: Hold monthly meetings with all departments to review the progress of scientific research and innovation areas, ensuring alignment with overall goals.
• Integrated Training Programs: Develop training programs that include modules on project management, scientific research delivery, and collaboration with data and engineering teams.
Research Engineering Group:
• Collaborative Development: Engage in joint development projects with the Data Wrangling and TPS teams to create research infrastructure that meets the needs of all parties.
• Skills Exchange Programs: Establish a skills exchange program where team members spend time working in other departments to understand different challenges and needs.
Tools, Practices, and Systems :
• Best Practices Documentation: Create comprehensive documentation and guidelines on open, ethical, reproducible, and collaborative practices in data science and AI.
• Open Source Community Engagement: Actively participate in open-source communities and encourage other teams to contribute to and leverage open-source tools and infrastructure.
2. Promotion of EDI (Equality, Diversity, and Inclusion) Priorities
Data Wrangling Team:
• Inclusive Data Practices: Ensure that data collection and usage practices are inclusive and represent diverse populations, particularly in health data management for early detection.
• EDI Training: Provide training to the data research team on embedding EDI principles in their work.
Programme Management Unit:
• Diverse Project Teams: Form diverse project teams to ensure a wide range of perspectives are considered in scientific research and innovation projects.
• Inclusive Funding Opportunities: Develop and promote funding opportunities that encourage diverse participation in research programs.
Research Engineering Group:
• Inclusive Infrastructure Design: Design research infrastructure that is accessible to researchers from diverse backgrounds and with different levels of expertise.
• Mentorship Programs: Implement mentorship programs to support underrepresented groups in research software engineering and computing.
Tools, Practices, and Systems :
• Global Collaboration Networks: Build and maintain networks that connect a diverse and global set of data experts with domain specialists.
• Ethical Use Policies: Develop and enforce policies that ensure ethical use of data and tools, with a focus on supporting marginalized communities.
3. Development and Implementation of Key Collaborations
Data Wrangling Team:
• Strategic Partnerships: Establish partnerships with key organizations and institutions that can provide valuable data and support for the Institute’s mission.
• Collaboration Platforms: Create platforms for seamless collaboration between the Data Wrangling team and external partners.
Programme Management Unit:
• Industry Collaborations: Develop collaborations with industry partners to leverage their resources and expertise in delivering high-quality research projects.
• Academic Partnerships: Partner with academic institutions to foster innovation and ensure cutting-edge research.
Research Engineering Group:
• Collaborative Infrastructure Projects: Lead collaborative projects that develop research infrastructure involving stakeholders from various sectors.
• Joint Research Initiatives: Initiate joint research initiatives that address the Grand Challenges and require engineering and computing expertise.
Tools, Practices, and Systems :
• Open Source Collaboration: Foster collaborations with open source projects to enhance and expand the available tools and infrastructure.
• Interdisciplinary Initiatives: Promote interdisciplinary initiatives that bring together domain experts and data specialists to address complex challenges.
4. Enhancement of Knowledge Exchange and Training Programs
Data Wrangling Team:
• Knowledge Sharing Sessions: Conduct regular sessions to share insights and developments in data wrangling and coordination.
• Online Learning Modules: Develop online modules focused on data culture and responsible data management practices.
Programme Management Unit:
• Training Workshops: Organize workshops that provide training in project management, research methodologies, and knowledge exchange strategies.
• Knowledge Exchange Platforms: Create digital platforms for knowledge exchange between researchers, program managers, and external partners.
Research Engineering Group:
• Technical Training: Offer training programs in research software engineering, data science, and computing to support team members and the wider research community.
• Hackathons and Code Sprints: Host hackathons and code sprints to encourage innovation and collaboration in research engineering.
Tools, Practices, and Systems :
• Open Access Resources: Provide open access to resources and tools that promote best practices in data science and AI.
• Collaborative Training Initiatives: Collaborate with educational institutions and industry partners to develop training initiatives that are accessible to a broad audience.
Implementing these strategies can effectively promote these teams’ roles and operational models, ensuring alignment with the Institute’s mission and the successful delivery of the grand challenges.
Alex.Worapol.Pongpech 
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