This goal means that people, especially women, girls and other marginalized groups, should not only be data subjects. They should have a real role in shaping data governance, including how data is designed, collected, analysed, shared and used. It represents a shift from consultation as an afterthought to participation as a normal part of data governance processes.

When this goal is met, data reflects diverse lived realities, revealing experiences from marginalized groups which are not usually considered. This improves policy outcomes by connecting local realities to national policymaking, and builds trust between citizens and governments.

Implementing this goal requires embedding citizen engagement in data-related processes. This could be done by requiring a Citizen Engagement Plan to involve women and girls’ movements related to data governance and/or involved across the data value chain on data governance initiatives (for example, in data monitoring approaches such as citizen scorecards, open data platforms, and dashboards, to drive accountability in processes). The plan should also include online and offline methods and tools which take into consideration local language accessibility, as well as budgeting for participation, and reports on feedback received and actions taken, to guarantee accountability and effectiveness.

Meaningful citizen engagement also requires investment in data literacy, which could include selecting and training community data stewards with data skills and contextual knowledge to act as bridges between communities and policymakers/technical actors.

• Require a simple Citizen Engagement Plan before data projects are approved.
• Set aside money for participation and require suppliers to work with women’s groups.
• Show evidence of meaningful engagement, comprehending layered approaches and oversight mechanisms.
• Budget for participation so women’s groups can engage consistently, including basic costs like transport, airtime, childcare, accessibility, and time for review work.

• Ministry responsible for Information and Communications Technology or Digital Transformation
• National Statistical Office
• Ministry responsible for Gender
• Data Protection Authority
• Local Governments
• Women and girls’ movements and community-based organisations

Short term measures:

• Issue a ministerial directive requiring Citizen Engagement Plans for all new data initiatives.
• Identify a local champion within the respective Ministries to champion and influence the process externally and internally.
• Update cabinet/project templates and introduce participation budget lines; establish simple feedback mechanisms.

Medium to long term measures:

• Establish a permanent multi-stakeholder data governance forum with formal representation of women and marginalized groups.
• Adopt national guidelines and institutionalize annual public reporting on citizen engagement.

• Percentage of new data initiatives approved through funded Citizen Engagement Plans.
• Number of girls’ and women’s movements involved across the data value chain.
• Percentage of inputs that led to changes in data governance policies and processes.
• Annual participation satisfaction score indicating whether citizens feel their input influences decisions.

This goal means that gender data must be governed in a way that protects rights, prevents harm and promotes gender equality. It represents the standards and safeguards that ensure women’s and girls’ data are collected for a clear purpose, limited to what is necessary, kept secure, and shared responsibly. The focus is on preventing misuse, ensuring inclusivity and avoiding discrimination, especially as data systems become more interconnected and data sharing increases.

Make ethics an intrinsic part of data governance both by integrating gender responsive provisions and implementing clear ethical guidelines for data governance processes.

Practical examples include establishing specific protections for high-risk contexts, such as explicit consent, privacy-by-design, specific limits on data access and sharing, safety measures to avoid data leaks and misuse, grievance and monitoring mechanisms, safeguards for politically sensitive data (e.g. gender-based violence and identity-related data), and algorithmic accountability when automated/AI systems are involved. Other measures include requiring ethical and gender risk checks before approvals, applying data minimisation and security standards in daily practice, and using standard data-sharing rules so women’s and girls’ data are protected from misuse and discrimination.

• Require an Ethics and Gender Risk Check and a Data Plan before any data project is approved.
• Use one standard data sharing agreement and put clear privacy and security rules in all supplier contracts.
• Budget for ethics and inclusion work, including the time and costs needed for women’s groups to review risks and give feedback on safeguards.

• Ministry responsible for Information and Communications Technology/Digital Transformation
• National Data Governance Lead
• National Statistical Office
• Data Protection Authority
• Ministry responsible for Gender
• Civil Society
• Private Sector Processors

Short term measures:

• Issue a government circular requiring ethical and gender risk reviews for new data initiatives, including cross-border data sharing.
• Create standard data-sharing agreements and incident reporting processes; train staff on consent and safe handling.

Medium to long term measures:

• Establish a national ethical oversight function (or strengthen the data protection authority’s mandate) with clear enforcement powers.
• Implement routine audits for high-risk datasets and invest in long-term security infrastructure and maintenance budgets.

• Percentage of new and updated data initiatives that completed an ethical and gender risk review before approval.
• Number of reported incidents (breaches, misuse, complaints) and percentage resolved within the required timeline.
• Number of high-risk datasets and projects reviewed for bias, discrimination, surveillance risk, and exclusion impacts.

This goal focuses on producing better quality gender and intersectional data, and ensuring it is used to inform and influence decisions. It intends to tackle insufficient, unavailable, non-transparent, weak, and/or potentially biased data systems by introducing data collection methods that reduce stereotypes and measurement errors, capture diverse realities, and support evidence based policy, budgeting and evaluation.

Improve gender data by revising and increasing investment in survey tools to reduce bias, collecting intersectional data beyond simple categories, training data collectors on gender-sensitive and bias aware methods, and ensuring gender data is used in planning, budgeting, and evaluation decisions.

Additional practical alternatives would be to interview women and other marginalized groups directly where safe, to ensure gender-matched interviewers for sensitive topics, to guarantee privacy during interviews, and to implement quality assurance protocols.

• Adoption of standards for mandatory nuanced gender-disaggregated and intersectional data.
• Data collection initiatives require proof of gender review before approval.
• Survey and data collection budgets include funding for gender-sensitive design, piloting and validation; data use is required in policy formulation.
• Resource safe and inclusive data collection and validation, including costs for women’s reference groups to test tools and confirm findings reflect lived realities.

• National Statistical Office
• Involved sector ministries (e.g. health, labour, education, agriculture, social protection)
• Ministry responsible for Gender
• Research institutions
• Women’s organisations
• Development partners

Short term measures:

• Conduct a rapid audit of existing survey and data collection tools to identify gender bias and gaps and revise questionnaires.
• Update data collectors and interviewers training manuals to include gender-sensitive and bias aware topics.
• Reduce proxy respondents for women’s data where possible and pilot women-only focus groups for sensitive topics.

Medium to long term measures:

• Institutionalize gender review as a mandatory step before survey approval.
• Develop national standards for intersectional classification.
• Invest in longitudinal and qualitative gender studies.
• Integrate gender data requirements into sector performance frameworks.

• Percentage of national surveys reviewed for gender bias before approval.
• Data collector and interviewer training completion rates.
• Number of policy or budget documents that explicitly reference gender data findings.
• Assess quality and completeness of gender-disaggregated data across sectors and review reduction in proxy respondent use.
• Evaluate how gender data influenced at least one major policy or budget decision and identify remaining gaps for the next cycle.

This goal means driving collaborative efforts by building shared ownership of gender transformative data governance through collaboration between individuals, groups and institutions across the data value chain, including government, civil society, especially women’s movements, academia and the private sector. It represents the idea that no single institution can deliver this agenda alone. Partnerships pool expertise and resources, improve accountability and oversight, and can shift power toward national, local and grassroots actors so gender data governance is sustainable and grounded in context.

For this goal to be put in practice, it is essential to institutionalise collaboration by setting up a multi-stakeholder gender data task force and formal partnerships with women’s organisations and other CSOs to co-design, co-fund, and oversee gender data priorities and accountability across the data value chain.

• Establish a multi-stakeholder gender data task force with clear Terms of Reference and a shared annual workplan.
• Use MOUs/partnership agreements and co-funding mechanisms to sustain joint initiatives and integrate outputs into plans and budgets.
• Resource partnership participation so women’s organisations are not expected to contribute unpaid labour, and can take part in task force workplans, reviews, and accountability.

• National Statistical Office
• Ministry responsible for Information and Communications Technology/Digital Transformation
• Ministry responsible for Gender
• Women’s movements and CSOs (including grassroots)
• Regulators
• Academia
• Private sector
• Development partners (supporting local ownership)

Short term measures:

• Map stakeholders and establish the task force.
• Sign MOUs with women’s organisations and CSOs for co-design, outreach and validation.
• Pilot one joint initiative (e.g., citizen-generated gender data, scorecards, or a gender data dashboard).

Medium to long term measures:

• Institutionalise the task force through policy or regulation so it remains active beyond projects and political cycles, providing mechanisms for continuity in case of government and/or leadership changes.
• Create pooled funding mechanisms and build long-term partnerships for continuous community engagement and validation.

• Existence of a national or regional gender data task force with approved Terms of Reference (Yes/No), with clear roles, routine meetings, and a shared workplan.
• Review of the workplan during budget hearings to ensure implementation and efficacy.
• Number of pilots/joint initiatives.
• Number of gender task force meetings held per year.
• Amount of co-funding mobilized for gender data initiatives and percentage allocated to local partners.
• Assess whether the partnership influenced at least one national standard, policy update or sector workplan.

This goal promotes women’s meaningful participation and leadership in data governance by strengthening women’s networks, improving representation in decision-making bodies, establishing mentorship pathways, and increasing the visibility of women role models in data and digital policy spaces.

To strengthen women’s meaningful participation and leadership in data governance, governments can introduce measures to improve representation, mentorship, visibility, and institutional support. This includes mandating gender-balanced participation in decision-making bodies, establishing mentorship schemes linking senior leaders with young women professionals, and designating a lead institution to coordinate women’s leadership initiatives.

Additional steps include supporting national women’s data governance networks, requiring annual reporting on women’s representation in digital leadership roles, increasing visibility of women experts through government media platforms, and further integrating leadership development into civil service and digital skills programmes.

• Issue a Cabinet directive requiring a gender quota system for all national data governance committees (e.g. data protection authority boards, digital strategy taskforces).
• Revise terms of reference to ensure fair and transparent selection of competent people.
• Introduce mandatory reporting on women’s participation in all core or major data governance initiatives.
• Allocate public funding to women-led data initiatives.

• Ministry responsible for Information and Communication Technology/Digital Transformation
• National Statistical Office
• Ministry responsible for Gender
• Local governments
• Data protection authority
• Women and girls’ movements and community-based organisations

Short term measures:

• Map existing women’s networks and leadership gaps.
• Issue directive on women’s leadership in data governance.
• Launch pilot mentorship programmes and reporting systems.

Medium to long term measures:

• Embed targets in national data and digital strategies.
• Scale mentorship nationally.
• Establish permanent funding and regional linkages.

• Percentage of women on data governance bodies.
• Existence of a recognized women’s data network.
• Number of women in leadership training and mentorship.
• Number of initiatives that women on data governance bodies have influenced.

This goal aims to build the skills, systems, and institutional practices needed for gender-transformative and intersectional data governance by investing in data literacy, ethical awareness, and gender-responsive data production across government, academia, and civil society.

To implement this goal and build national capacity for ethical, gender-responsive and intersectional data governance, governments can institutionalise sustained training and learning systems. This may include requiring all government-funded data training programmes (such as those for statisticians, ICT officers, and regulators) to incorporate modules on gender and intersectionality.

A lead institution could be designated to coordinate and standardise training efforts, while dedicated funding could support a national training programme focused on ethical and gender-transformative data governance for policymakers and regulators.

• Mandate gender and intersectionality modules in all public sector data training.
• Integrate competencies and include gender-responsive data collection and analysis into civil service curricula and performance systems, with regular reviews to ensure relevance with evolving data practices.
• Establish partnerships with academia, CSOs and regional institutions.
• Develop national curricula, toolkits and ethical standards.
• Establish a Community of Practice bringing together policymakers, statisticians, civil society organisations, researchers and data practitioners to exchange experiences, document good practices, and identify practical solutions for improving gender-responsive data systems.

• Ministry responsible for Information and Communication Technology/Digital Transformation
• National data governance lead
• National Statistical Office
• Data Protection Authority
• Ministry responsible for Gender
• Applicable sector ministries
• Government training authorities
• Civil society and private sector training institutions

Short term measures:

• Assess training gaps.
• Develop and pilot gender-responsive curricula.
• Issue guidelines on mandatory inclusion of gender modules.

Medium to long term measures:

• Institutionalize training in statistical systems and universities.
• Regularly update curricula.
• Harmonize data tools to remove bias and capture intersectionality.
• Collaborate with other African governments to coordinate a Continental Gender Data Standard and Glossary to harmonize definitions of gender-disaggregated and intersectional data, ensuring interoperability across borders.

• Percentage of data training programs including gender modules.
• Number of officials trained annually.
• Percentage of adoption of national curricula by institutions.

This goal seeks to ensure that gender-transformative data governance is supported by adequate, sustained, and transparent financial resources by integrating gender priorities into budgeting, planning, expenditure tracking, and accountability systems.

To ensure sustained, transparent and adequate financing for gender-transformative data governance, governments can adopt a set of institutional and budgetary measures. This may include allocating dedicated funds for gender-disaggregated surveys and upgrades to administrative data systems. Introducing gender budget tagging and reporting mechanisms would enable systematic tracking of spending on gender-related data initiatives, strengthening oversight and transparency.

Independent audits of expenditure on gender data programmes could further enhance accountability. Training budget and planning officers on gender-responsive budgeting would help embed gender considerations into financial decision-making processes.

• Integrate gender data priorities into national budgets and development plans, creating a budget line for “Gender and Data Governance” within the national budget to enable the implementation of the various activities within the applicable gender transformative strategy.
• Require each ministry to attach a gender budget statement to all data and digital projects.

• Ministry of Finance
• Ministry responsible for Gender
• National Statistical Office

Short term measures:

• Issue policy directive on gender budgeting for data.
• Pilot budget tagging and dedicated budget lines.
• Train financial and planning officers.

Long term measures:

• Institutionalize gender budgeting in MTEFs.
• Strengthen funding for gender-disaggregated data systems.
• Integrate tracking into audit and accountability frameworks.
• Contract independent audit from external consultants to determine if gender budget commitments are met.
• Add relevant requirements into sector performance frameworks.

• Existence of dedicated budget lines (Yes/No).
• Percentage of data budget allocated to gender initiatives.
• Publication of annual expenditure reports.

This goal focuses on strengthening research and innovation ecosystems that generate feminist, participatory, and policy-relevant evidence to address gender data gaps and inform inclusive, ethical, and effective data governance.

To strengthen research and innovation ecosystems that generate feminist, participatory and policy-relevant evidence for data governance, governments can introduce targeted funding and collaboration mechanisms. This may include launching competitive grant schemes to address key gender data gaps, such as unpaid care work, online violence, or rural women’s access to digital services.

Establishing a Gender Data Research Hub that connects government, universities, and women’s organisations could foster cross-sector collaboration and knowledge exchange. Publicly funded research could be required to produce policy briefs alongside academic outputs to ensure practical relevance. Additionally, supporting participatory research approaches — where communities help define what data is collected — would enhance legitimacy, inclusivity, and responsiveness.

• Integrate gender data priorities into national R&D and innovation policies.
• Create funding windows for participatory and intersectional research.
• Require funded research to produce policy briefs and accessible outputs.
• Use public procurement to support inclusive and ethical data technologies.

• National Statistical Office and the Ministry responsible for ICT/Digital Transformation
• Ministry responsible for Gender
• Women’s movements and CSOs (including grassroots)
• Regulators, academia, and private sector
• Development partners (supporting local ownership)

Short term measures:

• Issue directive prioritizing gender data research.
• Map institutions and establish pilot hubs.
• Launch small grants for participatory research.

Long term measures:

• Embed priorities in national research funding systems.
• Institutionalize participatory research methods.
• Align with regional and continental frameworks.

• Number of gender data research hubs established.
• Funding allocated to participatory research.
• Number of policy briefs and community outputs produced.