Data disaggregation is the process of breaking down aggregated or compiled data into smaller, more detailed subgroups based on specific characteristics, such as gender, age, disability, and ethnicity. Disaggregating data allows for deeper analysis to reveal who is being left behind and is is foundational for inclusive policymaking.
It helps decision-makers to identify inequalities, target interventions more effectively, and monitor progress toward leaving no one behind. The tools in this section provide tested approaches for plugging gaps and aligning or updating approaches to match international best practice to produce high-quality disaggregated data. These resources should be used to design context-appropriate approaches across the data value chain and data systems that are ethical, accurate, and rights-based with the broader goals of equality and inclusion.
This section provides practical guidance on planning, implementing, and using disaggregated data. The first subsection covers general frameworks and guidance, followed by specific resources for key population groups, recognizing that each group faces unique measurement and inclusion challenges.
Users may explore the entire section for a holistic understanding or jump directly to population-specific areas relevant to their work. These resources can help practitioners to design more inclusive data collection, analysis, and reporting processes.
General
Effective data disaggregation begins with understanding its core purpose, methods, and ways of identifying who is represented in data and who is not. The resources in this subsection provide broad guidance on data disaggregation strategies, including both technical methodologies and institutional planning tools. Users can start here to gain an overview of what data disaggregation entails before delving into subsequent subsections on specific population groups.
Practical Guidebook on Disaggregation for the Sustainable Development Goals
Author: Asian Development Bank
Type: Guidance/toolkit
Location: Asian Development Bank website
Overview: This guidebook provides detailed, practical guidance to support the planning and implementation of data disaggregation strategies and methods to meet SDG reporting across population subgroups. It covers foundational concepts and practical tools, including planning and implementing data disaggregation strategies, working with data sources, applying estimation techniques, dissemination, and using data. It aims to help NSOs, statisticians, and analysts understand how to break down aggregated indicators in ways that are methodologically sound, feasible, and useful and addresses some of the common challenges to data disaggregation.
Key topics:
- Definition and rationale of data disaggregation and link to the SDGs
- Strategy and institutional planning for disaggregation
- Data sources, e.g., surveys, administrative data, small area estimation, big data
- Data analysis, addressing data gaps, trade-offs on sample size, bias, and estimation methods
- Data policy integration
- Communication, reporting, and visualization of data
- Enabling conditions, e.g., financing, capacity, partnerships, and governance
Stages in the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
Includes case studies/examples: Yes, features many examples, country experiences, and illustrative use cases, e.g., in Indonesia and the Philippines. There are also further suggested reading lists for many of the topics.
Additional information: Some of the content may require statistical expertise; available in PDF and EPUB formats.
Equalities Data Audit
Author: United Kingdom Office for National Statistics
Type: Audit tool/example
Location: Office for National Statistics website
Overview: The Equalities Data Audit is a structured inventory of existing United Kingdom data sources covering the protected characteristics in the Equality Act, i.e., the legally identified marginalized groups. It aims to map the gaps in data and identify which sources can support intersectional or disaggregated data analysis. Available as a downloadable spreadsheet, the audit lists official datasets (with links) and details on the disaggregated characteristics they include. It serves as a baseline against which improvements in inclusion, harmonization, and data strategy can be measured.
Key topics:
- Landscape mapping/stocktake of equality data sources
- Metadata attributes, e.g., frequency, geographical granularity, coverage across population groups
Stages in the data value chain:
- Identify
- Collect
- Process
- Release
- Disseminate
Includes case studies/examples: Yes, the audit is itself an example.
Additional information: Can be adapted to different contexts as a model for identifying gaps and assessing coverage or harmonization.
Gender
Gender disaggregated data is one of the most widely used forms of inclusive data, yet there continue to be gaps in data systems, especially when surveys, administrative systems, and sectoral data are not designed to capture gender-specific information comprehensively. This subsection provides critical insights into gender inequalities across sectors, including health, education, and economic opportunity, supporting evidence-based, gender-responsive, and participatory decision-making through a range of tools, such as guidance, maturity models, online repositories, and case studies. These resources will help users to plan, produce, and strengthen gender data systems with targeted tools to close the gaps. This subsection can also serve as an entry point to broader inclusion for other groups, deepening understanding of inequality.
Counted and Visible Toolkit to Better Utilize Existing Data from Household Surveys to Generate Disaggregated Gender Statistics
Authors: UN Women and Inter-secretariat Working Group on Household Surveys
Type: Guidance/toolkit
Location: UN Women website
Overview: This toolkit helps NSOs and gender data producers to use existing household surveys more fully to produce gender disaggregated statistics, especially where new data collection is constrained. It provides a compilation of tools and mechanisms used by several countries to produce evidence to inform gender-responsive policies.
Key topics:
- Institutional commitment for gender statistics
- Development of national priority gender equality indicators
- Data production, including data sources and estimation of gender equality indicators
- Use of microdata, metadata, and software tools for disaggregation
- Methods for assessing statistical quality, e.g., precision, reliability, bias
- Intersectional analysis
- Assessment and publication of results
- Dissemination, advocacy, and use
- Stakeholder engagement
Stages in the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
Includes case studies/examples: Yes, there are many country examples, such as Mongolia, Pakistan, and Georgia, across each of the core topics and chapters.
Additional information: Most relevant where survey microdata exists; focuses on maximizing existing data rather than new data collection. Some sections are highly technical, including statistical formulae and STATA commands.
Gender Data Solutions Inventory and Report—Suite of Tools
Authors: Data 2X and Open Data Watch
Type: Online repository/inventory and report/case studies/examples
Location: Data 2X website
Overview: This suite brings together two complementary resources to support efforts to close gender data gaps. They provide practical approaches, possible solutions, or methods to plug gaps, compare across sectors, inspire collaborations, or adapt or scale proven approaches. They are combined here because they cover similar sectors and focus areas and answer similar questions in gender data disaggregation.
Key topics:
- Addressing gaps in data disaggregation for gender equality
Resources
- Gender Data Solutions Inventory: A searchable collection of 140+ solutions addressing gender data gaps across six sectors, including health, education, economic opportunity, environment, human security, and public participation, across various stages of the data value chain. This tool helps data producers (NSOs, governments, non-governmental organizations, etc.) see what has been tried, especially innovations to supplement traditional data collection with new methods. The inventory may be filtered by areas of focus, data value chain classification, relevant actors, and transferability.
- Stages in the data value chain: Identify, Collect, Process, Analyze, Release, Disseminate, Connect, Use
- Solutions to Close Gender Data Gaps: This analytical report explores some of these solutions in greater detail, including solutions by sector and on the enabling environment, e.g., governance, financing, and capacity, as well as recommendations.
- Stages in the data value chain: Identify, Collect, Process, Analyze, Release, Disseminate, Connect, Incentivize, Influence, Use
Includes case studies/examples: Yes, each solution in the inventory typically serves as or provides examples or cases from countries or organizations, and the report provides many of the same examples with more detailed case write-ups.
Additional information: Guidance depth varies across tools, with some being sector- and/or country-specific and others more general. Some approaches can inform broader disaggregation efforts beyond gender.
Strengthening Administrative Data Systems to Close Gender Data Gaps—Suite of Tools
Author: UNICEF
Type: Guidance/checklists
Location: UNICEF website
Overview: These tools support governments and NSOs in strengthening administrative data systems so they can serve as a reliable source for gender-disaggregated data. Grouped together, these resources provide a package for evaluating and improving gender statistics.
Key topics:
- System readiness and institutional capacity
- Gender-specific data gaps and administrative data
- Data quality and improvement
- Governance, interoperability, and integration with other sources
- Standardization of gender indicators and metadata
- System readiness and institutional
- Emphasis on coordination across ministries and agencies for systemwide improvement
Resources:
- Improving Data for Women and Children: Guidance on Strengthening Administrative Data Systems for Gender Statistics: Provides insight on the role of administrative data in gender statistics, defines good practice following UNICEF’s Administrative Data Maturity Model, and addresses common challenges in the use of administrative data for gender statistics, including financing.
- Stages in the data value chain: Identify, Collect, Process, Analyze, Release, Disseminate, Connect, Use
- Assessing Maturity of the Cross-Sectoral Administrative Data Landscape for Gender Data Checklist: Including a downloadable Excel spreadsheet, this is a diagnostic tool to assess whether existing systems are equipped to collect and manage sex-disaggregated and gender-relevant data.
- Stages in the data value chain: Identify, Collect, Process, Connect
- Identifying Barriers to Using Administrative Data for Gender Statistics Checklist: This downloadable Excel spreadsheet assesses the reliability, completeness, and relevance of gender data within administrative sources.
- Stages in the data value chain: Process, Analyze, Release, Reuse
Includes case studies/examples: Yes—the guidance refers to many country-based examples, especially in the health and education sectors, and provides fuller country case study write-ups from Canada and Ghana in the annexes.
Additional information: Supports diagnosis and planning, especially if applied over time, and for countries transitioning from survey-based approaches to routine gender data generation.
The Development of a Gender Data System Maturity Model
Authors: Data 2X and Open Data Watch
Type: Maturity model/framework
Location: Data 2X website
Overview: This brief provides a detailed maturity model for gender data systems, intended to help countries assess their existing capabilities and chart a path toward more advanced, sustainable systems. It describes dimensions of gender data maturity, i.e., governance, financing, technical capacity, data instruments, and stakeholder coordination, and sets out four levels of maturity. It also provides guidance on how a maturity assessment can feed into a roadmap or investment plan.
Key topics:
- Dimensions of gender maturity model
- Criteria and indicators for each maturity level in each dimension
- Using maturity assessment outputs to inform strategies
- Methodological notes on scoring, weighting, and interpretation
Stages of the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
- Connect
- Incentivize
- Influence
- Use
Includes case studies/examples: Yes, country examples show how certain maturity features work in practice.
Additional information: Supports turning dialogue into concrete assessment and planning around gender data systems.
Supporting Girls’ Education in Sierra Leone through Inclusive Data Systems
Author: Global Partnership
Type: Case study/example
Location: Global Partnership website
Overview: This case study documents how Sierra Leone’s Education Ministry and partners have used the Inclusive Data Charter principles to improve data systems for girls’ education and marginalized groups. Key actions include strengthening coordination across government and civil society, improving survey and census instruments, disaggregating by gender and disability, digitizing education data collection, and embedding inclusion in policy. It demonstrates how NSOs and line ministries can integrate inclusive data approaches into education management information systems and link data to policy, informing concrete policy changes.
Key topics:
- Practical illustration of early- and mid-level data value chain steps
- Disaggregation by gender and disability
- Expansion of survey and census instruments
- Multi-stakeholder coordination across government and civil society
- Digital data collection and integrating systems
- Policy change/data-policy link
- Gaps concerning geographic considerations, e.g., district-level enrollment
Stages of the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
- Connect
- Use
Includes case studies/examples: Yes, this is a case study providing rich detail.
Children
Working with children’s data presents unique opportunities and responsibilities. Disaggregated, child-focused data is key to monitoring well-being, designing policies, and upholding children’s rights. However, collecting and managing children’s data involves ethical, privacy, and protection considerations. This subsection highlights resources that help users to produce child-related data responsibly and effectively. These tools show how to build strong administrative data systems and safeguard children’s rights, providing both conceptual and practical tools, balancing risks with active participation. These resources can complement tools in the subsections focusing on other populations, such as gender and disability, to reflect the diversity of children.
Responsible Data for Children—Suite of Tools
Authors: UNICEF and The GovLab
Type: Online repository/framework/diagnostic tools/case studies/trainings
Location: Responsible Data for Children website
Overview: This site promotes responsible data practices in the design, collection, use, and sharing of data about children. It sets out principles and practical tools to help diverse actors assess risks, protect children’s rights, and strengthen governance in child data systems. Case studies demonstrate good practices and are linked to the principles. The tools focus on embedding accountability, transparency, and ethical decision-making across the data lifecycle.
| Resource/Link | Type | Purpose | Data Value Chain |
| Data Ecosystem Mapping Tool | Mapping tool | Helps to visualize actors, data flows, and institutional roles in the child data ecosystem | Connect, Use, Reuse |
| 22 Questions to Assess Responsible Data for Children | Checklist | Self-assessment checklist to examine strengths and gaps in responsible child data work | Identify, Collect, Analyze, Use |
| Decision Provenance Mapping | Mapping tool | Traces how decisions about child data are made, including who makes them and under what conditions | Incentivize, Influence, Use |
| Opportunity and Risk Diagnostic Tool | Diagnostic tool | Identifies potential harms and benefits in child data initiatives to support risk-aware decision-making | Identify, Collect, Process, Analyze, Use |
| Studio Methodology | Participatory method/ facilitation process | Facilitates collaborative exploration of core principles through real-world child data projects | Cross-cutting |
Includes case studies/examples: Yes, there are country case studies on responsible child data practices and lessons for several countries, including Colombia, Kenya, and Uganda.
Additional information: Most relevant for planning, governance, and review phases of data concerning children, especially for privacy, consent, or cross-sectoral data considerations. Complements more technical statistical guidance.
Using Administrative Data for Children
Author: UNICEF
Type: Guidance
Location: UNICEF website
Overview: This resource introduces the rationale, challenges, and emerging practices for using administrative data to support child-focused monitoring and decision-making. It follows UNICEF’s Administrative Data Maturity Model, shares examples of using administrative data in low-literacy contexts and at the community level, and reflects on institutional and technical enablers. The aim is to help NSOs and child-focused agencies move from aspirational statements to practical steps in leveraging administrative systems for children’s data.
Key topics:
- Administrative Data Maturity Model for children’s data
- Community engagement and local-level monitoring using administrative data
- Digitization, data integration and triangulation, and linkage to other systems
- Challenges of data quality, coverage, and underreporting
- Enabling conditions, e.g., capacity, infrastructure, and standards
Stages of the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
- Connect
- Use
Includes case studies/examples: Yes, there are country and project examples, e.g., local monitoring in West and Central Africa.
Additional information: Because this is a highlights version, some of the deeper methodological or technical content is condensed or omitted, but it can be paired with the Administrative Data Maturity Model and other technical tools.
LGBTQI
Collecting data on sexual orientation, gender identity and expression, and sex characteristics (SOGIESC) is essential to understanding and addressing inequalities faced by these groups. However, this data is scarce and often contested, given the privacy, security, and stigma concerns of these groups. This subsection presents one comprehensive resource to help users design ethical, rights-based approaches within data systems for these populations, offering practical guidance on safe, inclusive approaches grounded in consent.
Guidance Note on the Collection and Use of Data for LGBTIQ Equality
Author: European Commission High Level Group on Non-Discrimination, Equality, and Diversity, Subgroup on Equality Data
Type: Guidance
Location: European Commission website
Overview: This guidance outlines principles, methodological considerations, ethical safeguards, and institutional approaches for gathering and using data disaggregated by SOGIESC. The aim is to help countries navigate privacy, legal, conceptual, and technical challenges, ensuring that data on these populations is collected in a rights-based, statistically robust, and comparable manner.
Key topics:
- Challenges in collecting SOGIESC data
- Ethical, privacy, consent, and data protection considerations, in compliance with the General Data Protection Regulation (GDPR)
- General principles for collecting SOGIESC data, e.g., self-identification, voluntary
- Guidance on a needs assessment for SOGIESC data users, identifying data sources, and aligning definitions and categorizations
- Methodological approaches to data collection, including surveys, administrative data, and proxies
- Intersectional approaches to SOGIESC data collection and use
- Enabling factors, such as budgeting for data collection
- Advice on using SODIESC data to improve LGBTQI equality
Stages of the data value chain:
- Identify
- Collect
- Process
- Analyze
- Release
- Disseminate
- Connect
- Use
Includes case studies/examples: Yes, there are examples of existing practices in European Union Member States.
Race, Ethnicity, and Indigenous People
Data disaggregated by race, ethnicity, or Indigenous group is essential to monitoring discrimination, ensuring equality, and recognizing collective rights. However, each national context presents challenges, and data can carry a history of misuse and mistrust. This subsection highlights frameworks and guidance to collect, use, and govern data responsibly, emphasizing self-identification, consent, participation, and sovereignty. They reinforce the shift from extractive to participatory approaches. Used in conjunction with resources on citizen data and intersectionality, users can adopt approaches that build trust.
Guidance Note on the Collection and Use of Equality Data Based on Racial or Ethnic Origin
Author: European Commission High Level Group on Non-Discrimination, Equality, and Diversity, Subgroup on Equality Data
Type: Guidance
Location: European Commission website
Overview: This guidance outlines how to collect and use equality data disaggregated by racial or ethnic origin in line with human rights and data protection standards. It provides legal, ethical, and practical considerations for improving data collection to inform non-discrimination policies and equality monitoring, with attention to public trust and stakeholder engagement.
Key topics:
- Guidance on mapping existing data and conducting a needs assessment
- Legal, privacy, and ethical standards, including alignment with GDPR
- Self-identification principles and when and how to use proxy information
- Collecting data on the experience of discrimination
- Community involvement and trust
- Policy-relevant equality indicators
- Enabling conditions, e.g., financing, capacity, political will
Stages of the data value chain:
- Identify
- Collect
- Process
- Analyze
- Disseminate
- Use
Includes case studies/examples: Yes, there are examples of applying good practices at the national level, e.g., Norway’s data hub on living conditions of Sami national minorities.
CARE Principles for Indigenous Data Governance
Author: Global Indigenous Data Alliance
Type: Framework/principles
Location: Global Indigenous Data Alliance website
Overview: This framework was designed to guide Indigenous data governance, emphasizing people and purpose in data systems. It complements existing data standards by ensuring that data about Indigenous Peoples is collected, stored, used, and shared in ways that respect their rights, cultures, views, self-determination, and priorities.
Key topics:
- Core principles, including collective benefit, authority to control, responsibility, and ethics
- Alignment with FAIR principles of open data, i.e., findable, accessible, interoperable, and reusable
- Respect for self-identification, consent, and data sovereignty
Stages of the data value chain:
- Identify
- Collect
- Process
- Use
Includes case studies/examples: Yes, links to examples of applying the framework in ecology and biodiversity and genomic research.
Additional information: Provides broad principles rather than detailed technical or operational guidance. Includes links to a practice paper, briefing/overview of the CARE Principles, slides, and translations of the CARE principles in Spanish, Vietnamese, Māori, German, and Khmer.
The First Nations Principles of OCAP®
Author: First Nations Information Governance Centre
Type: Framework/example
Location: First Nations Information Governance Centre website
Overview: This framework is designed to assert authority over all aspects of data collection, use, and governance relating to First Nations, i.e., Indigenous Peoples of Canada. It sets out four principles as foundational conditions for ethical data practice and is widely used across First Nations communities in Canada as a protective measure and proactive strategy for self-determined information governance.
Key topics:
- Core principles, i.e., ownership, control, access, possession
- Indigenous jurisdiction over data and information
- Community governance structures, e.g., privacy laws, data agreements, etc.
- Responses to historical harms in data and research
- Application of the framework in research, health, and policy contexts
Stages of the data value chain:
- Identify
- Collect
- Process
- Influence
- Use
Includes case studies/examples: Yes, this is an example of data governance of Indigenous Peoples in Canada.
Additional information: Specific to the Canadian context but may provide insights on comparable contexts or emerging issues that bear some similarity. An online workshop on the fundamentals is available.
Disability
Disability disaggregated data is essential for identifying barriers and advancing inclusion of persons with disabilities across services and sectors. However, collecting accurate, comparable, and rights-based data on disability presents challenges. This stems from outdated measurement tools, underreporting due to stigma, and inconsistent definitions of disability.
This subsection focuses on a suite of tools aimed at generating high-quality, comparable data on disability. It covers the full data lifecycle, featuring guidance, survey question sets and how to choose between them, case studies, and advice to tackle common challenges. These tools align with international best practice and may be used in conjunction with subsections covering other populations as well as the section on intersectionality.
Washington Group on Disability Statistics Resources—Suite of Tools
Author: Washington Group on Disability Statistics
Location: Washington Group website
Overview: The Washington Group developed a comprehensive set of tools and guidance resources to replace the standard yes/no census question on national censuses and surveys concerning disability. Aimed at improving accuracy and quality of disability disaggregated data, they focus on an individual’s functional aspects and align with human rights principles. These tools are designed to be modular, ensuring that the most appropriate question set is used for the right context and purpose.
| Resource | Type | Description | Data Value Chain |
| The Data Collection Tools Developed by the WG on Disability Statistics and Their Recommended Use | Guidance | Overview of the various question sets with advice on selection and use | Identify, Collect, Process |
| WG Short Set on Functioning (WG-SS) | Question set | Core set of 6 questions to identify persons with disabilities | Collect |
| WG Extended Set on Functioning (WG-ES) | Question set | Expanded version of the Short Set, including psychosocial disabilities (anxiety and depression), upper body functioning, pain, and fatigue | Collect |
| WG Short Set on Functioning— Enhanced (WG-SS Enhanced) | Question set | Broader tool covering more functional areas intended for population-based health surveys or disability-focused surveys | Collect |
| WG/UNICEF Child Functioning Module (CFM) | Question set | Designed for child/adolescent population with two submodules of children ages 5-17 and another for children ages 2-4 | Collect |
| Implementation Guidelines: How to use the WG Questions | Guidance | Instructions for each question set, including rationale, limitations, common pitfalls, instructions on administering questions, and interviewer guidelines | Identify, Collect, Process |
| Analysis Overview | Guidance | Technical guidance for tabulation, disaggregation, and interpretation of data | Process, Analyze, Connect, Influence, Use |
| The WG Blog | Blog/ guidance | Searchable repository of common challenges, reflections, and questions from users | Disseminate, Influence, Use |
| NSO Trainings | Training | Materials from WG trainings for NSOs | Identify, Collect, Process |
Case studies/examples: Yes, there are examples from several countries that have used WG questions in their censuses and surveys, including Malawi, South Africa, and Fiji. Examples are embedded across guidance documents and in the blog.
Additional information: The WG questions are extensively tested and widely accepted by civil society and NSOs, supporting internationally comparable disability disaggregated data. They should not be used to diagnose disability or determine eligibility for disability programs, services, or benefits. Guidance on translation is provided, and multiple language versions are available to ensure consistent implementation.