To shape effective policies and drive change in the remaining years of the 2030 Agenda, we must harness the potential of intersectionality in the production and use of development data.

Intersectionality is a framework that recognizes that a person’s sex, age, ethnicity, and other social identities interact to create unique experiences of advantage and disadvantage. Integrating an intersectional lens across the data value chain—from data collection and publication to uptake and use—goes beyond technical improvements. It aims to shift power dynamics in data governance for more equitable outcomes and inclusive data.

This brief provides a practical guide for planning to build better data systems by integrating intersectionality at each stage of the data value chain, improving the quality and value of data, and creating meaningful impact. This involves a spectrum of practical considerations to realize the full value of data investments. How do marginalized groups participate in data collection? How are the data made available? Who conducts the data analysis and acts on the results? And how will the data ultimately benefit the community?

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What is the data value chain?

The data value chain describes the processes of data production and use from the initial identification of data needs to the ultimate impact of that data. It shows how the value of data increases as it is gathered, processed, and placed in the hands of those who use it to inform decisions for a better world.

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Collection

Integrating intersectionality into data collection practices is not only an ethical imperative—it is a path to better data. When marginalized groups are engaged throughout the data value chain, their realities can be reflected in the production and use of data, improving quality and deepening data analysis, leading to better policies. By embracing intersectionality at the outset, we strengthen the data ecosystem from the ground up and create a foundation that policymakers and citizens alike can use to drive change that leaves no one behind.

	Meaningful engagement for better data collection Effective data collection starts with the identification of what needs to be collected. Direct engagement with civil society organizations (CSOs) and members of underrepresented groups makes it possible to understand the issues relevant to them. Consulting with policy analysts helps prioritize disaggregation by key attributes important to national development. This participatory approach validates questions and methodology and leads to tailored strategies for data collection.
	Building a better ecosystem of data sources Intersectional data gathered through administrative systems and alternative sources, such as citizen-generated data (CGD), are particularly important for creating a data ecosystem that represents the interests and needs of all people. Administrative sources hold a wealth of untapped potential that, coupled with citizen-generated data, can elevate the voices of marginalized communities. However, data sharing agreements and open data practices will be crucial to using such data sources.
	Shifting the balance of data ownership Intersectionality shifts the traditional power dynamic of data ownership from producers to the subjects themselves. The subjects must agree with the processes of data collection and accept the results as an accurate representation of their concerns and interests. This is crucial, as those represented in the data face the highest potential risks. Governments and civil society must respect community ownership of data while ensuring protective measures are in place to anonymize and secure data, supported by robust human rights frameworks and anti-discrimination laws.

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Engagement with civil society in Morocco strengthens data collection for violence against women and girls (VAWG)

In 2019, UN Women worked with Morocco’s High Commission for Planning (HCP) to design a survey to update VAWG prevalence figures. Consultation with CSOs expanded the scope of the survey to cover new areas such as estimating costs of violence for victims and relatives.

A CSO specializing in gender-based violence trained enumerators on sensitive data collection, including how to help interviewees recall violent events, ethical procedures, and referrals to services and support for survivors. Women’s networks and CSOs were also involved in the data collection as “listeners” to work alongside HCP teams collecting the data to provide services to victims of violence.

This approach improved data quality and accuracy as listeners were able to help enumerators introduce questions of violence in an indirect manner to get information with more subtlety.

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Publication

Data publication covers the processes of making raw data available for public access. An inclusive and intersectional approach to data production will result in more use and meaningful impacts, increasing the return on data investments. By actively involving those whom the data are about in the publication process, the analysis can reflect their voices and produce insights that address their needs. It is also important to publish data in open formats to make the data and analysis accessible and avoid the waste of data graveyards.

	Intersectional analysis to capture meaningful insights To realize the full potential of intersectional data collection, analysts continue to consult with community representatives and incorporate their inputs and feedback. An effective analysis asks the right questions and employs the appropriate methods to produce answers that will be most relevant to users and accurately reflect the lived experiences of marginalized groups. It is also important for analytical processes to respect the data ownership of respondents and ensure their privacy through the application of appropriate data security measures and anonymization methodologies.
	Publishing data in accessible formats for equitable use For intersectional data to benefit the communities they cover, they must be published in accessible and open formats. This goes beyond simply uploading a static table to a website. To be considered open, data should be available in machine-readable, non-proprietary formats, accompanied by comprehensive metadata to enable easy extraction, analysis, and reuse. It is also important to consider the language in which information is published and whether it is accessible to those with disabilities.
	Data stewardship to guide inclusive and equitable data production As governments and national statistical offices modernize their data systems, the emerging role of data stewardship has become increasingly relevant. Although the specific responsibilities of a data steward may vary by context, the overarching goal remains the same: to promote efficient, inclusive, and equitable data sharing and use. This involves setting appropriate standards to maintain data quality and integrity, ensuring that the control and use of data assets is fair, and respecting interests and concerns of marginalized groups. By championing these principles, data stewardship can help ensure that data production results in meaningful impact.

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The United Kingdom excels in publishing disaggregated data on sex and gender identity in open formats

According to the 2023 Gender Data Compass assessment of 53 important gender indicators in over 180 countries, the United Kingdom ranks first in the world in openness with a score of 74.1 percent. This assessment also found sex-disaggregated data available for 80 percent of the indicators, along with other disaggregations.

The Office for National Statistics of the UK has made data on gender identity available on its platform from a 2021 census. This reflects a strong commitment to collect and make available vital information related to sex and gender identity.

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Uptake

Uptake is the process of connecting users with data and making it possible for them to gain insights from the data and act. Effective intersectionality requires a system-wide commitment to ensure data are not produced just to record marginalized voices but are available and used for the benefit of vulnerable individuals and groups. Without prioritizing intersectionality, the data landscape remains incomplete, and opportunities to address deep-seated inequalities are missed.

	Connecting users to intersectional data Data intermediaries play a vital role in bridging the gap between published data and data use. Intermediaries come in many forms: a policymaker working with a statistician to understand issues and develop a data-driven proposal to address challenges facing marginalized communities; or a data journalist using statistics to tell a story on a pressing social issue; or a civil society organization creating a user-friendly app that can make information accessible to less technical users. Connecting data providers and data users creates positive feedback loops that incentivize engagement.
	Building capacity for intersectional data use Without adequate skills and resources, even the best-intentioned efforts to apply intersectional data will fall short. Investments in user training and data literacy are essential to turn data into actionable insights. Equipping both policymakers and community members with the skills they need not only empowers users but also bridges the gap between ambition and reality, driving tangible progress.
	Creating incentives to prioritize intersectionality Strong governance and funding incentives are needed to operationalize intersectionality. Donors who wish to advocate for and promote intersectionality can embed requirements for intersectionality in their funding criteria and explicitly invest in inclusive data practices. Governments can also establish laws to mandate intersectionality and guidelines to put it into practice. Meaningful engagement with marginalized communities should become a routine standard, not an afterthought. By making intersectional data a priority at every level, we can move beyond tokenistic efforts and build equitable, impactful data systems that truly serve all people.

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An action plan to finance and build capacity for the uptake of disaggregated disability data in Kenya

 The Inclusive Data Charter Action Plan (2021-2025) calls for the coordination of state and non-state actors in the production and use of disaggregated disability data. To facilitate the uptake of data, the plan recognizes the need for improved human and technical capacity to collect, analyze, and use disaggregated data as well as the critical importance of sufficient financing so that high-quality disability data can be collected and used by governments as well as by businesses, civil society, and citizens alike.

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Impact

Data are used throughout many stages of the data value chain, but impact is achieved when data inform a decision or alter a condition and improve well-being. An inclusive, intersectional approach maximizes the impact of data. A deeper understanding of disparities emerging from intersecting challenges guides policymakers towards more targeted and effective interventions empowers individuals and groups to use their own data to inform their decisions and work towards meaningful change. This lens helps address ethical concerns by reducing risks of unintended harm. Demonstrating positive impacts also creates positive feedback loops that strengthen the processes along the whole of the data value chain.

	The importance of who benefits Intersectional data collection begins with a promise that all voices will be heard and that data will be used for positive outcomes. In keeping that promise, partnerships cultivated as part of data collection are opportunities to identify ways that data can be directly used to create meaningful impacts. Likewise, policymakers should design their interventions through continuing engagement with relevant stakeholders. Respecting their interests and promoting their agency will help create inclusive and effective outcomes.
	Ethical considerations and monitoring Implementing intersectional data practices requires strong monitoring systems to track both positive impacts and potential risks. Intersectional data can potentially expose individuals to privacy risks and harm, particularly in hostile environments. Clear ethical guidelines and transparent data governance are essential for mitigating these issues. Ongoing monitoring allows for course corrections and ensures that interventions lead to positive, rather than unintended negative, outcomes. Collaboration among data producers, users, and affected communities is crucial to uphold accountability and adapt strategies as needed.
	Strengthening feedback loops and building trust and demand Intersectional data use leading to visible, real-world impacts reinforces feedback loops across the data value chain. Demonstrating policy changes and improved services driven by intersectional data builds demand for better-quality data and analysis. Respondents will have increased willingness to participate in future data collection as they see the value of it. And when data users and policymakers see the value of intersectional insights, it creates a virtuous cycle of investment, trust, and sustainable practices. By embedding habits of intersectional data use, we can foster inclusive development and empower marginalized communities, strengthening the entire ecosystem of data-driven decision-making.

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Using data to identify and address racial disparities in maternal health in Brazil

 The Brazilian Ministry of Health established the 2010 National Policy for Comprehensive Health of the Black Population to address racial inequalities prevalent in health statistics. However, ongoing monitoring that resulted in updated policies (in 2013 and 2017) continued to show maternal mortality rates almost twice as high among Black women compared to other races.

In 2024, the Brazilian government launched the Rede Alyne program with the goal of reducing maternal mortality by 25 percent overall and 50 percent for Black women by 2027. CSOs have also become involved in addressing the crisis with programs across public hospitals improving maternal mortality and achieving racial equity.

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Call to Action

Intersectionality is a vital approach for transforming data systems to truly reflect the diversity of lived experiences and the complexities of development challenges. By embedding intersectionality across each stage of the data value chain, we can build data systems that capture the realities of marginalized groups and drive targeted, effective interventions.

Operationalizing intersectionality requires both commitment and action from a wide range of stakeholders: national statistical offices, donors, policymakers, international donors, and civil society. It calls for an intentional shift towards inclusive data practices, investment in capacity building, and stronger accountability mechanisms to track and assess progress.

To move forward, stakeholders must prioritize building robust, inclusive data systems that center the needs and direct participation of those most often overlooked. By doing so, we unlock the power of data to uncover hidden inequalities, amplify marginalized voices, and enable more equitable policy solutions that lead to better development outcomes for all.

Many opportunities exist to promote an intersectional approach to development data:

• Standardize intersectional data practices: Establish standards and guidelines for the production and governance of intersectional data initiatives.
• Mobilize resources: Secure adequate funding and technical assistance as part of requirements to prioritize an intersectional approach to data,
• Develop case studies: Identify best practices and create opportunities to facilitate learning between practitioners to build and maintain momentum.
• Collaborate across other movements: Maintain intersectionality as a high priority within other initiatives such as citizen-generated data and data feminism.

 

This brief was prepared by Open Data Watch and Data2X. Join us in reimagining data systems that empower marginalized communities and facilitate inclusive development.

 

Contact us at: info@opendatawatch.com

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Glossary of terms

Agency: The ability of individuals or groups to participate in all steps of the data value chain, particularly when data reflect their intersecting identities.

Data intermediary: An individual or institution that facilitates access to or sharing of data.

Data stewardship: The management and oversight of an organization’s data assets to provide business users with high-quality data that are easily and consistently accessible.

Intersectionality: A concept for uncovering and understanding the experiences and challenges faced by individuals who occupy multiple and intersecting group identities.

Open data: Data that can be freely used, modified, and shared by anyone for any purpose.

 

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