Overlapping and compounding forms of disadvantage can be addressed through intersectional data approaches. By analyzing multiple characteristics together, rather than in isolation, intersectional data can reveal hidden inequalities that conventional disaggregation may miss. 

In practice, intersectionality goes beyond disaggregation and involves examining how different characteristics interact, how data systems may reinforce exclusion, and how insights can drive systemic change. This often requires adjustments in methodologies, governance, and collaboration across sectors. 

The resources in this section provide conceptual foundations, practical entry points, and case studies from different perspectives. They will help users seeking to operationalize intersectional approaches across the data value chain. Since disaggregated data is used in intersectional analysis, users may use tools in the disaggregation section to plug gaps and enhance intersectional analysis.

ResourceTypeDescriptionData Value Chain
Unpacking Intersectional Approaches to Data: A White Paper

Conceptual document/

guidance

Sets out the definition, rationale, good practices, lessons, and common challenges on intersectional approaches to data. It focuses on 5 stages and provides key recommendations for each Identify, Connect, Incentivize, Influence, Use, Change
A Primer on an Intersectional Approach to Data

Summary/

guidance

A condensed companion to the white paper, it introduces key concepts, terminology, and entry points for intersectional approaches to dataIdentify, Connect, Influence, Use
Establishing an Intersectional Approach to Data at Your National Statistics InstituteNational case studyShowcases how Colombia and the United Kingdom piloted intersectional data strategies in their NSOs, including rationale, approaches, and challenges. It provides approaches and examples from both countries on developing NSO guidance on intersectional approachesIdentify, Collect, Process, Analyze, Influence, Use, Change
Systemic Ways to ‘Leave No One Behind’

Non-governmental organization/

program case study

Details how Development Initiatives used three intersectional approaches: (1) P20—a systematic way to establish and monitor a population at risk, (2) a landscape approach to explore available data sources, and (3) participatory methodsIdentify, Collect, Analyze, Use
How Intersectional Approaches to Data Can Be Used to Drive Whole Systems ChangeWhole systems case studyShows how intersectional approaches feed into wider systems to influence sectoral reforms from the Institute of Global Homelessness Identify, Collect, Process, Analyze, Influence, Change
Steps Taken by the Internal Displacement Monitoring Centre to Understand the Context for Preparing Its Intersectional Approach to DataHumanitarian case studyExamines how disaggregation of gender, age, and displacement status combined with intersectional analysis led to better targeted interventions and how humanitarian actors can incorporate it into monitoring frameworks Identify, Collect, Analyze, Use
When and How to Use Multivariable Analysis for Identifying Intersectional InequalitiesResearcher/ analyst case studyHighlights a field-level disability research project in Nigeria, examining how the research team designed survey modules, handled sample sizes, and analyzed intersectional outcomesCollect, Process, Analyze, Use

 

Case studies/examples: Yes, there are five published case studies covering different countries, sectors, and practitioner perspectives, with additional examples shared on the site’s blog and in the white paper.

Additional information: Provides conceptual and practical rather than technical guidance.