Policymaker's Edition - Edited Version

I. Acknowledgements

II. Introduction

III. The Purpose

IV. From Goals to Actions

V. Glossary of Concepts

Feminist Approach
Gender Data
Gender-Transformative Approach
Intersectionality
Gender Equality and Equity
Gender Norms
Empowerment

VI. Conclusion

1. ACKNOWLEDGEMENTS

This Policymakers’ Edition is the result of a collaborative and co-authored effort. It reflects the time, expertise, and shared commitment of policymakers and data ecosystem stakeholders who contributed their insights throughout the process.

We would like to sincerely thank all participants for their valuable contributions and the thoughtful work they brought to this collective endeavour.

Co-Authors
[List of co-authors]

Contributors
[List of contributors]

Thank you again for your commitment and collaboration. This edition would not have been possible without your engagement and dedication.

2. INTRODUCTION

At a time when governments across Africa are increasingly relying on data to guide decisions and deliver public services, this Policymakers’ Edition is the result of a collaborative effort to make gender-transformative data governance more practical and actionable, building on Gender Data Futures: A Handbook on Transformative Data Governance, developed by Pollicy. While the original Handbook provides a comprehensive foundation, its structure and language were designed for a broad audience. Building on this strong basis, i4Policy led a co-creation process to adapt the Handbook into a version tailored specifically to policymakers working to strengthen gender-responsive data governance across the region.

The objective was to translate the original content into a format that speaks directly to policy realities, institutional constraints, and decision-making environments. The process brought together policymakers, data and gender ecosystem stakeholders, with particular emphasis on engaging women. Participants reviewed the original Handbook from a policymaker’s perspective, identifying where concepts required clarification, simplification, or reframing to support action. Through virtual co-creation workshops and a co-authoring workgroup, they collectively reshaped the structure, learning journey, and key messages of this edition to ensure relevance and usability. This collaborative work was complemented by structured consultations that gathered targeted feedback on draft sections and invited a broader group of policymakers to review and validate the near-final version.

The result is a more accessible, context-responsive, and practice-oriented edition that preserves the ambition and substance of the original Handbook while making it directly usable for policy development, implementation, and reform.

3. THE PURPOSE

The importance of good data governance

Data is central to modern governance. Policymakers rely on data to define problems, allocate resources, monitor implementation and demonstrate accountability. Decisions about how data is collected, governed, and interpreted shape which populations are visible in policy processes and whose needs are prioritised. Data governance is therefore inherently not neutral. It reflects institutional choices and underlying power dynamics. But what is good data governance? A great starting point is using a gender-transformative lens which recognises that data systems can either reinforce entrenched inequalities or help dismantle them. In line with Agenda 2063’s commitment to inclusive development and the AU Data Policy Framework’s call for inclusive and ethical data systems, gender responsive data governance is essential for fair, transparent, and accountable service delivery.

When gender perspectives are absent, data systems often rely on gender-blind or sex-neutral data frameworks that assume policies affect everyone equally. In practice, this masks differences in access, participation, and outcomes, particularly for women, girls, and other marginalised groups. Without accurate, intersectional gender data, policymakers risk designing policies based on incomplete evidence and assumptions, overlooking structural barriers that shape lived realities. These gaps are shaped by historical and institutional power imbalances that determine whose knowledge counts, whose experiences are counted and whose voices are heard, a concern central to feminist approaches and reflected in the AU Gender Policy and the Maputo Protocol’s commitments to equality and non-discrimination. When gender is invisible in data systems, policy precision and effectiveness are weakened from the outset.

Mitigating policy risks

Weak integration of gender perspectives in data governance creates tangible risks for policymakers. Policies informed by incomplete data are more likely to underperform, fail during implementation, or generate unintended consequences. Budget allocations may not reach those most affected, and monitoring systems may be unable to demonstrate equitable outcomes. Over time, this undermines public trust, weakens accountability, and exposes decision-makers to reputational and political risk. Many policy failures can be traced to data that did not adequately capture differentiated impacts across populations.

Abiding by continental mandates

Policymakers already operate within continental and international commitments that require inclusive, accountable governance. Agenda 2063 calls on Member States to ensure that no one is left behind. The AU Data Policy Framework emphasises inclusive, ethical and accountable data systems. The AU Gender Policy mandates gender mainstreaming across sectors, and the Maputo Protocol requires the elimination of discrimination and the protection of women's rights. Meeting these obligations depends on data systems that can identify who benefits from policies and where gaps persist. Gender-responsive data governance supports core responsibilities related to planning, budgeting, monitoring, evaluation, and compliance.

There is no time to waste

Growing inequality, rapid digitalisation, and rising expectations for accountability make it urgent to address exclusion embedded in data systems. Data systems designed without attention to intersectionality risk reproducing exclusion in digital platforms, algorithms and public service delivery systems. Considering overlapping identities such as gender, age, disability, geography, and socioeconomic status improves policy accuracy, effectiveness and sustainability. A gender-transformative approach addresses the structural conditions that produce inequalities. Gender-responsive data governance is thus core to governance infrastructure to better enable decisions and stronger accountability, leading to fairer outcomes and enhanced public trust.

What are we waiting for? Let’s dive straight into some strategies to get you started…

4. FROM GOALS TO ACTIONS

To get you started quickly, we have identified 8 strategic goals to work towards a more gender transformative approach to data governance. Given its broad impact, these goals can be broken down into short, medium and long term goals. We explain how this could be done in practice, as well as the types of stakeholders who could be involved, and what success might look like in practice.

The goals are not in any particular order, so feel free to pick and choose the ones you feel you could champion easily whilst building a longer term strategy, resource mobilisation and stakeholder engagement for the more complex goals.

Finally, good data governance requires a whole of society approach. Don’t let this overwhelm you, change takes time and this resource is your first step on the road of transformation.

Strategic Goal 1:	Citizen Engagement in Data Governance Practices
Summary	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.
How could this goal be implemented?	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 budgeting for participation, and reports on feedback received and actions taken, to guarantee accountability and effectiveness..
What administrative mechanisms can drive implementation?	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 and show evidence of engagement. Budget for participation so women’s groups can engage consistently, including basic costs like transport, airtime, childcare, accessibility, and time for review work.
Who should be involved?	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
What could this goal look like in practice?	Short-term measures: Issue a ministerial directive requiring Citizen Engagement Plans for all new data initiatives. 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.
How could this goal's success be measured?	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.

Strategic Goal 2:	Ethical Gender Data Governance
Summary	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.
How could this goal be implemented?	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 of how to implement this goal include establishing specific protections for high-risk contexts, such as explicit consent, privacy-by-design (preventing privacy problems before they happen, rather than fixing them afterwards), specific limits on data access and sharing, safety measures to avoid data leaks and misuse, grievance and monitoring mechanisms, and safeguards for politically sensitive data (e.g. gender-based violence and identity-related data). Other measures would be to require 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.
What administrative mechanisms can drive implementation?	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.
Who should be involved?	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
What could this goal look like in practice?	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.
How could this goal's success be measured?	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 for bias, discrimination, surveillance risk, and exclusion impacts.

Strategic Goal 3:	Strengthen Production and Use of Gender Data
Summary	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.
How could this goal be implemented?	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 and mitigation measures on how to implement this goal 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.
What administrative mechanisms can drive implementation?	Adoption of standards for nuanced gender-disaggregated and intersectional data Data collection initiatives require proof of gender review (i.e. documented evidence that the initiative has been examined through a gender lens.) 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.
Who should be involved?	National Statistical Office Involved sector ministries (eg. health, labour, education, agriculture, social protection, etc) Ministry responsible for Gender Research institutions Women’s organisations Development partners
What could this goal look like in practice?	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.
How could this goal's success be measured?	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.

Strategic Goal 4:	Multi-stakeholder Partnerships for Gender Transformative Data Governance
Summary	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.
How could this goal be implemented?	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.
What administrative mechanisms can drive implementation?	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.
Who should be involved?	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)
What could this goal look like in practice?	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. Create pooled or funding mechanisms and build long-term partnerships for continuous community engagement and validation.
How could this goal's success be measured?	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 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.

Strategic Goal 5:	Women’s Leadership in Data Governance
Summary	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.
How could this goal be implemented?	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.
What administrative mechanisms can drive implementation?	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.
Who should be involved?	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
What could this goal look like in practice?	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.
How could this goal's success be measured?	% of women on data governance bodies. Existence of a recognized women’s data network. Number of women in leadership training and mentorship.

Strategic Goal 6:	Capacity Building for Gender-Transformative Data Governance
Summary of the Strategic Goals	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.
How could this goal be implemented?	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.
What administrative mechanisms can drive implementation?	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. Establish partnerships with academia, CSOs and regional institutions. Develop national curricula, toolkits and ethical standards.
Who should be involved?	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.
What could this goal look like in practice?	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.
How could this goal's success be measured?	Percentage of data training programs including gender modules. Number of officials trained annually. Percentage of adoption of national curricula by institutions.

Strategic Goal 7:	Budgeting for Gender-Transformative Data Governance
Summary	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.
How could this goal be implemented?	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.
What administrative mechanisms can drive implementation?	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.
Who should be involved?	Ministry of Finance Ministry responsible for Gender National Statistical Office
What could this goal look like in practice?	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. Requirements into sector performance frameworks.
How could this goal's success be measured?	Existence of dedicated budget lines (Yes/No). Percentage of data budget allocated to gender initiatives. Publication of annual expenditure reports.

Strategic Goal 8:	Research and Innovation for Gender-Transformative Data
Summary	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.
How could this goal be implemented?	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.
What administrative mechanisms can drive implementation?	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.
Who should be involved?	National Statistical Office and the ministry responsible for ICT/digital transformation; Ministry responsible for Gender. Women’s movements and CSOs (including grassroots); regulators; academia; private sector; development partners (supporting local ownership).
What could this goal look like in practice?	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.
How could this goal's success be measured?	Number of gender data research hubs. Funding allocated to participatory research. Number of policy briefs and community outputs produced.

5. GLOSSARY OF CONCEPTS

Feminist Approach

Feminist approaches aim to transform entrenched power structures and address the systemic exclusion of women and girls whose realities are marginalised by dominant systems, including governance and data systems. They provide the conceptual foundation for gender-transformative strategies by shifting attention from surface-level representation to the systems, policies, and institutions that determine whose knowledge counts and whose voices are heard. Feminist approaches, supported by gender data and intersectional analysis, provide the tools to make these distinctions and to dismantle, rather than reinforce, systemic inequalities within data ecosystems.

Gender Data

Gender Data refers to the collection, analysis, and use of information that reveals how gender differences, inequalities, and power imbalances shape outcomes across all areas of life, including education, employment, health, political participation, and digital access. Meaningful gender data goes beyond simple counts to capture how social and cultural norms influence access, control, and use of resources.

When data collection methods fail to account for these deeper dynamics, policies risk being ineffective or reinforcing existing inequalities. Missing or incomplete gender data has real consequences, leading to decisions that overlook the needs of women and other marginalised groups and limit their access to services and opportunities.

Gender-Transformative Approach

A gender-transformative approach seeks to change the structural and power dynamics that produce gender-based inequality, discrimination, and exclusion, rather than addressing only their symptoms. In the context of data governance, this means identifying and tackling the structural drivers of inequality. It includes strengthening the collective and individual agency of women and girls by enabling their meaningful participation in and influence over decision-making processes, recognising intersecting forms of inequality (see the definition of ‘Intersectionality’ below), and embedding gender considerations into policies, budgeting processes, and data governance institutions.

Intersectionality

Intersectionality recognises that a person's experience of unfairness depends on all their identities (attributes) combined, not just one aspect of it. It acknowledges that gender inequalities intersect with other factors such as race, religion, disability, sexual orientation, and geography, and that overlooking these intersections undermines sustainable progress. In practice, that means that a woman experiences things differently based on whether she is young or old, urban or rural, able-bodied or disabled, wealthy or poor, and that all these factors might overlap. Good policy identifies these overlapping barriers and targets them specifically, rather than assuming all women (of different races, ages, disability status, living situations, and economic status) face the same challenges.

For example, consider data being collected on women's access to credit. If that data is not broken down by age, location, and education level, it overlooks the fact that older rural women with less education face specific barriers that aggregate (wider) data would never reveal. These blind spots lead to policies that sound inclusive on the surface but leave people behind (especially those targeted by the policy). Intersectionality prevents wasted resources on one-size-fits-all interventions that do not work for everyone.

Gender Equality and Equity

Gender equality is the destination: equal outcomes and equal representation across all areas (same number of women and men in leadership, equal pay, equal access to services without discrimination). Gender equity is how you get to that destination: the fairness in the process, the deliberate measures you take to level the playing field and remove the barriers that have prevented equal outcomes in the first place.

A government could place 50% women in leadership roles (equality on paper) but if those women lack the training required to serve, face cultural pressure to stay silent, can't attend meetings due to childcare responsibilities, or have no real decision-making power. In that case, there is an appearance of equality without the equity that makes it meaningful (and that makes it work). True equality comes from first doing the equity work (ie. by removing the obstacles that will prevent those women serving efficiently).

Gender Norms

Gender norms are the informal social expectations that shape how women and men are perceived, what roles they are expected to perform, and whose voices are valued in families, communities, workplaces, and institutions. These norms influence how opportunities, responsibilities, and authority are distributed and often operate invisibly within systems, including data and governance processes.

In data governance, gender norms affect whose experiences are considered important enough to measure, whose participation is expected in decision-making, and how information is interpreted. For example, norms that associate men with leadership or technology may lead to data systems that overlook women’s digital access, unpaid care work, or safety concerns. When these patterns go unexamined, policies risk reinforcing existing inequalities rather than addressing them.

Addressing gender norms does not mean regulating culture; it means identifying and correcting the systemic biases that shape how data is collected, categorized, and used. By questioning these embedded assumptions, policymakers can ensure that governance systems reflect the realities of the whole population, not only those groups whose roles have traditionally been prioritized.

Empowerment

Empowerment refers to the process through which individuals and groups gain the knowledge, skills, confidence, and opportunity to participate meaningfully in decisions that affect their lives. It involves strengthening people’s ability to understand their rights, use available resources, and influence public and institutional processes.

In the context of data governance, empowerment means enabling women and other historically excluded groups to engage not only as beneficiaries of services, but as contributors, users, and shapers of data systems. This includes access to digital tools, data literacy, leadership opportunities, and platforms where their perspectives can inform policy design and accountability processes.

When people are empowered to engage with data, policies become more responsive, better targeted, and more legitimate. Empowerment therefore supports more effective governance by broadening who can participate in shaping evidence, questioning decisions, and improving public outcomes. Without this inclusion, data systems risk reflecting only the perspectives of those already in positions of influence.

6. CONCLUSION

Gender-transformative data governance is an ongoing process. Each institutional reform, policy adjustment, and inclusive consultation contributes to more equitable and accountable data systems.

Advancing such an approach requires leadership, coordination, and persistence. It involves translating commitments into concrete measures, embedding gender considerations into policies and budgeting processes, strengthening accountability mechanisms, and ensuring meaningful participation in decision-making spaces.

This resource is part of a broader ecosystem of knowledge and practice. For deeper conceptual grounding and case studies, we invite you to consult Pollicy's Gender Data Futures: A Handbook on Transformative Data Governance. To further explore how overlapping forms of inequality shape data and governance outcomes, we recommend engaging with our website on Intersectionality. To strengthen inclusive and meaningful engagement processes, the Participation Handbook offers practical guidance on designing participatory approaches within policy and data governance contexts.

We hope this Policymakers’ Edition and related resources support you in taking informed, confident, and context-responsive steps forward. We encourage you to use this edition actively. Share it within your institution. Use it to guide internal discussions. Draw on it when reviewing strategies, drafting policies, designing data systems, or commissioning new initiatives. Identify entry points that are feasible within your mandate, and build alliances across departments and sectors to sustain progress over time.

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