Inclusion Matters: Rethinking Data Governance Through an Accessibility Lens

Inclusion is not a peripheral concern—it is a core requirement for building data systems that serve the public good. Yet, the second edition of the Global Data Barometer (GDB) reveals that most data governance frameworks remain far from inclusive, particularly when it comes to accessibility for persons with disabilities and linguistic diversity. This blog post explores the inclusion cross-cutting theme, which brings together evidence from across indicators to examine whether current data systems are designed with all users in mind.

Cross-cutting themes in the GDB go beyond individual indicators or sectors. They reassemble components from multiple parts of the survey, weaving together sub-questions, elements, and supporting responses to surface patterns that traditional sectoral analysis might miss. The inclusion theme does just that—providing a multidimensional view of how well data systems reflect, represent, and serve diverse populations.

The findings are sobering. Only a handful of countries have comprehensive legal or regulatory frameworks that explicitly require disability-inclusive data practices. Even where such requirements exist, they rarely span the full data lifecycle—from collection and consent to publication and correction. Promising examples, such as efforts in Peru and Brazil, remain the exception rather than the rule—and even there, implementation is uneven and institutional oversight is limited.

In practice, this means that persons with disabilities are often excluded not just from the benefits of data, but also from the processes through which data is generated, governed, and used. The lack of inclusive standards results in gaps in representation, reduced accountability, and missed opportunities for policy responsiveness.

Another overlooked but equally critical dimension is linguistic inclusion. While governments frequently publish data in official languages, the exclusion of indigenous or other local languages remains a major barrier to access. This is especially acute in multilingual countries where significant portions of the population may be unable to meaningfully engage with public data due to language mismatches. The absence of Indigenous languages from data systems not only limits accessibility—it reinforces existing structural inequities, preventing marginalized communities from participating fully in civic life or holding institutions accountable.

Taken together, these findings point to a deeper issue: data governance systems are too often designed without the diversity of end users in mind. Inclusion is not just about ticking a box or adding a translation—it’s about embedding equity into the architecture of data ecosystems. This requires proactive design choices, legal safeguards, and participatory processes that bring affected communities into the center of decision-making.

To build truly inclusive data systems, countries must expand the scope of accessibility beyond technical standards and think critically about who is represented, who is excluded, and what systemic changes are needed to close these gaps. The GDB’s cross-cutting analysis on inclusion makes clear that advancing data justice begins with reimagining accessibility—not as an afterthought, but as a foundational design principle.