Abstract

Conventional approaches to data collection, disaggregation and analysis often fail to capture how different dimensions of inequality intersect and shape people's lived experiences. Examining gender, age, disability or other characteristics in isolation obscures the distinct experiences of people facing multiple, overlapping forms of disadvantage. Making these inequalities visible requires moving beyond single-dimension disaggregation towards more systematic intersectional approaches to data.
This presentation will highlight Open Data Watch’s work to advance intersectionality in data, including efforts to strengthen understanding of how statistical systems can better identify and address the data needs of people at the intersections of multiple forms of inequality. It will explore how intersectional data can reveal patterns of exclusion that aggregate statistics and conventional measures may overlook, providing a stronger evidence base for inclusive policies and accountability.
Realizing this potential requires more than new analytical methods. National data systems need to strengthen their capacity to address intersectional and inclusive data needs. The presentation will argue that intersectionality needs to be considered throughout the data value chain, rather than treated as an additional analytical exercise.
This implies adopting an intersectional approach at all stages of data production and use -- from identifying data needs, designing data collection systems, defining population groups, to assessing data quality, and analysing and disseminating findings. This includes improving the availability of data for intersecting population groups, addressing challenges related to sample size, privacy and comparability, and strengthening collaboration among national statistical offices and other data producers and data users. Such an approach can help statistical systems better reflect the diversity of people's experiences, support more effective policies and accountability, and lay the foundations for AI systems that serve everyone, including those who have historically been left behind in the data.