Data Science Champions Network Africa
Context and Purpose
1. The webinar convened twenty (21) members and partners with of the Data Science Champions Network Africa (DSCN Africa) under the UN Regional Hub for Africa to address operational challenges faced by NSOs in adopting data science, big data, and AI for official statistics. Specifically, the group: surfaced blockers in the production pipeline; shared practical experiences and lessons learned; defined minimum governance and scaling criteria; and proposed actionable next steps for innovation.
2. Annexes: List of participants; and Concept note and Agenda.
Key Discussion Points
3. The Opening remarks emphasized the importance of collaboration to avoid duplication and reduce costs. Participants were encouraged to draw on global frameworks such as the Paris21 AI Readiness initiative and to leverage upcoming partnerships, including a project with Google, to accelerate adoption of advanced technologies.
4. During the Pulse check, participants highlighted several pressing challenges. These included gaps in technical capacity and skills, resistance from both management and staff to change, outdated legal frameworks that restrict access to non-traditional data sources, and concerns about trust and transparency when using big data and AI for official statistics.
5. Insight Sparks provided real-world perspectives with Namibia sharing their experience, noting that successful integration of data science requires strong management buy-in, clear objectives, and dedicated research and development units within NSOs to allow experimentation. The importance of partnerships and international exposure were noted in driving progress for data innovation. It was also noted that Namibia has recently approved its IT and Data Science Strategy, which is now moving into implementation.
6. Breakout sessions explored two main themes. The first focused on integration and capacity, where participants noted the varying levels of understanding of integration (people, systems, data). They also discussed breaking down silos between statisticians and IT teams, introducing structured training programs, and conducting skills gap analyses. The second theme addressed governance, trust, and partnerships, highlighting the need to update legal frameworks, improve transparency through clear methodology documentation, and strengthen collaboration with regulators and private sector partners. Bias mitigation strategies, such as combining big data with traditional sources for validation, were also discussed. The need to develop tools to help NSOs capture nontraditional sources within legal framework was also recognized.
Recommendations
7. The group agreed on several key recommendations.
7.1. Legal frameworks must be updated to explicitly allow the use of non-traditional data sources, and model clauses for partnerships and data-sharing agreements should be developed.
7.2. Capacity building should be prioritized through cross-training programs for statisticians and IT professionals, and DSCN members should be included in all regional training opportunities including the UN regional hub webinar series and other training, whenever possible
Powering innovation in Africa’s official statistics, together.
7.3. Transparency and trust must be enhanced by publishing clear guidelines on the use of big data and AI and by communicating benefits and safeguards openly to stakeholders.
7.4. NSOs should create dedicated research and development units to provide space for innovation and experimentation.
7.5. Partnerships need to be strengthened by formalizing collaboration with regulators and private data providers and by sharing successful case studies and templates across the network.
7.6. Develop clear guidance on navigating novel and nontraditional data.
7.7. Encourage information sharing including successful case studies and templates across the network
Other matters
8. The webinar concluded with a discussion on additional topics critical for sustaining innovation efforts.
8.1. Participants were introduced to the NSO Maturity Survey, which aims to benchmark readiness levels for adopting data science and AI across national statistical offices. They concurred that they would respond to the survey to support the identification of capacity gaps and inform targeted support strategies.
8.2. The group also discussed the sustainability plan for DSCN webinars, emphasizing the need for regular sessions and structured follow-up mechanisms to ensure continuity and impact. For the next webinar, participants proposed to continue the interactive format with dedicated time for case studies, breakout discussions, and a segment on practical tools and templates. It was agreed that the next session would focus on either of the three priority topics identified in the webinar.
Next steps and conclusion
9. Looking ahead, the group proposed three priority topics for the next webinar:
9.1. Convincing Management and Securing Buy-in for data science initiatives, including advocacy strategies and examples of return on investment.
9.2. Building trust in non-traditional data through practical frameworks for transparency, bias mitigation, and validation.
9.3. Legal and governance readiness, focusing on how to update statistical acts and create enabling policies, as well as developing model agreements for partnerships and data access.
9.4. Next webinar. Volunteers step forward to lead and/or support the organization of the next webinar in February 2026 with Cynthia Chebet offering to be the lead coordinator. She will be supported by other DSCN members who will assist with agenda design, facilitation, and documentation. The ECA and NISR, as the UN Regional hub secretariat, will continue to provide advisory services and guidance to the DSCN webinars.
10. Business arising from the webinar for consideration of the DSCN and the UN Regional hub include:
10.1. Respond to the NSO Maturity Survey to identify capacity gaps (Members).
10.2. Design structured training programs for statisticians and IT professionals (PMU /All).
10.3. Review existing global frameworks and develop guidance and advocacy materials to increase trust in AI and nontraditional data that are more suited to the African context (PMU / All).
10.4. Support the updating of mandates and statistical acts for institutionalizing data access to novel data sources (PMU).
10.5. Organize webinars and invite private institutions to foster collaboration (PMU/ All).
10.6. Prepare proof-of-concept collaborations (e.g., South-South examples such as Indonesia (one-stop shop for data) and Georgia (migration statistics using MPD).