Systemic ways to "leave no one behind": How intersectionality informs development initiatives programs

This case study introduces systematic ways to ‘leave no one behind.’ It highlights three
innovative methods Development Initiatives uses to operationalize an intersectional
approach to data. They are especially useful to consider for governments and organizations
that seek to support diverse communities and are not sure how to begin.

Key messages
• Promoting equity across the data value chain requires multiple methods to identify
individuals at greatest risk of marginalization and to design data ecosystems that
leave no one behind.
• DI has developed and collaborated on three different methods that can be used in
an intersectional approach to improve data ecosystems. Each method has its own
strengths and purpose.
• The P20 approach can be used to identify and monitor individuals at greatest risk of
marginalization or discrimination.
• A data landscape approach can be used to investigate data that currently exists, where
and how that data is produced, and how the ecosystem can be improved to support the
needs of different stakeholders using intersectional approaches.
• Community-driven data can be used to ‘center the voices’ of communities at greatest
risk of marginalization. It can complement government or organizational data
collection to fill data gaps and increase responsiveness.