Intersectional approaches are vital in African data governance because overlapping identities (e.g., gender, age, location) create exacerbated barriers. Inclusivity in data systems is a human rights imperative, especially for Africa’s “under-sampled” communities. Policy must reflect the continent’s diverse realities, consider its colonial legacy, avoid importing non-African frameworks, and align with standards set by bodies like the African Commission on Human and Peoples’ Rights.
Policy Development in Data Governance without Intersectionality
While intersectionality is widely recognised as important for addressing social inequalities, translating it into policy practice remains difficult. Scholars note that intersectionality’s conceptual complexity and relatively recent adoption in policy settings present ongoing challenges for decision makers.
The following section outlines specific systemic barriers that have been cited as making adopting an intersectional approach difficult. This overview sets the scene for the subsequent section, which explores two case studies illustrating the real-world consequences of some of these failures.
Policy Development with Intersectionality
What if the true strength of public policy lay not in uniformity, but in its ability to navigate the complex realities of diverse lives? In Africa, where histories, cultures, and identities are deeply layered, the task of crafting effective data and technology policies requires more than ticking the boxes of inclusion. It calls for a fundamentally different approach.
Multi-Dimensional Approach
Taking into consideration different layers of intersectionality. Unlike approaches that focus on a single strand of identity (e.g., only gender or only race), the multi-dimensional perspective recognizes that inequality operates at different levels simultaneously. Drawing on the framework by Jubany, Güell, and Davis (2011), this approach requires analyzing data governance across three distinct levels:
- The Micro Level (Individual): How individuals subjectively experience their multiple identities and potential discrimination.
- The Meso Level (Institutional/Community): How institutions (schools, hospitals, local government) and administrative practices either accommodate or exclude specific groups.
- The Macro Level (Structural): How broader laws, cultural norms, and national policies create the conditions for inequality.
How to apply this approach
To implement a multi-dimensional strategy, data governance policies must connect these levels rather than treating them separately. Policymakers should ask:
- Micro: Does the data allow individuals to self-identify with multiple characteristics?
- Meso: Do administrative systems create barriers for specific combinations of identities?
- Macro: Do national privacy laws or data standards inadvertently harm specific subgroups identified at the micro level?
Space-Based Approach
Treating “place and context” as part of the analysis. Inequality is rarely geographically neutral. A Space-Based approach treats “place” not just as a backdrop, but as an active component of disadvantage.
This method pushes analysts to consider how geographic location (urban vs rural), physical accessibility, and local cultural contexts transform how data and technology are accessed. For example, a digital service that works perfectly in a well-connected capital city might be completely non-functional for a rural community without stable electricity or network coverage.
By centering “place,” policymakers can design solutions that are physically and culturally situated, ensuring that the benefits of data governance reach people where they actually live, rather than where infrastructure is most convenient.
Developed by Bishwakarma, Hunt, and Zajicek, this systematic approach integrates intersectionality throughout the typical policy cycle. It operates on the premise that both governmental and non-governmental organizations must incorporate intersectionality at every stage of policymaking, not just at the end.
Instead of treating inclusion as a retrospective checklist, this method demands asking intersectional questions at four phases: Agenda Setting, Formulation, Implementation, and Evaluation.
How to apply this approach
What it involves: Agenda setting is where intersectionality must first be embedded. This means broadening who defines the problem, which data is collected, and whose experiences are treated as evidence.
Key Actions: Map intersecting inequalities using disaggregated data; Centre testimony from multiply-marginalised communities; Challenge single-axis problem definitions.
What it involves: Requires designing measures that specifically address compound disadvantages, not just the “average” experience. It demands diverse expert panels and inclusive consultation.
Key Actions: Conduct intersectional impact assessments; Design layered, flexible policy responses; Include intersectional monitoring frameworks.
What it involves: Actively monitoring reach across intersecting groups. Frontline workers must have competency training to ensure services are geographically and digitally accessible.
Key Actions: Train frontline staff in intersectional competency; Remove bureaucratic access barriers; Establish community feedback loops.
What it involves: Asking who benefited, who was left out, and why. Completion of the cycle by feeding learning back into future agenda-setting.
Key Actions: Disaggregate outcome data by multiple identity markers; Use participatory evaluation involving affected communities; Identify unintended consequences.
Example in the context of Data Governance
Consider a government launching an “Open Data Portal.” In the Standard Approach, the portal is simply launched, and success is measured by “total hits.” While the aggregate numbers might appear high, they hide a critical failure: the data is mostly being accessed by those living in urban areas with high-speed internet, leaving the most vulnerable populations out.
In contrast, applying a Policy Process Approach transforms the initiative entirely. Before a single line of code is written, for instance, civil society groups representing rural women are consulted to define what data they actually need to hold local officials accountable. Recognising the intersection of poverty and geography, the team designs the interface specifically for low-bandwidth mobile devices rather than high-end desktops. Finally, success is no longer centered around total traffic; instead, metrics specifically track usage rates from marginalised regions. If usage in those areas is low, it is flagged as a system failure rather than a lack of interest, triggering future improvements.
From theory to practice
To move from theory to practice, it helps to see how intersectional thinking changes outcomes on the ground. The following two case studies illustrate how change makers across the continent of Africa have begun integrating intersectionality into real-world data and policy solutions.
Collection of Data through an Intersectional Lens
When Morocco set out to update national statistics on violence against women and girls (VAWG), intersectionality was woven directly into the survey process. Rather than limiting the data to prevalence rates, government actors worked hand-in-hand with civil society organizations to broaden the scope. They used new survey modules to look at how violence affects not only survivors but also their families, and the costs they bear.
To gather this nuanced data, CSOs specialising in gender-based violence played a leading role in training enumerators. These trainers equipped data collectors with methods for asking sensitive questions, like helping interviewees recall traumatic experiences in a safe and respectful manner, applying ethical protocols, and providing direct referrals to support services. During data collection itself, women’s networks and advocacy groups were present as “listeners,” working alongside official survey teams. Their involvement did not just safeguard the wellbeing of respondents, it improved the quality of responses, as enumerators could introduce questions about violence more carefully and pick up on subtle cues.
By involving local actors throughout the process, the survey design better reflected the lived realities of diverse groups of women in Morocco. The resulting dataset captured layers of experience that standard surveys often overlook, giving policymakers a more grounded foundation for designing support services and prevention programmes. This collaborative model shows how approaching data collection with an intersectional approach, linking gender, context, and social support systems, can produce data that is richer and more relevant for real-world decisions.
Example adapted from Open Data Watch & Data2X (2023)
Digital Identity and Intersectional Exclusion in East Africa
Across East Africa, national digital ID systems have been promoted as universal solutions to administrative exclusion. Yet the data tells a more complex story. Research comparing rollout results in Kenya and Uganda found that uptake rates mask enormous disparities when disaggregated by intersecting identities.
Women in rural areas with lower literacy rates, and particularly those from ethnic minority communities, were far less likely to be successfully registered. The common assumption that a “one-size-fits-all” digital identity process is inherently inclusive was shown to be false: a woman without a birth certificate, who does not speak the administrative language, and who cannot afford the cost of travel to a registration center, faces compounding barriers that no single-axis intervention can address.
This case demonstrates why intersectional data collection is essential before, during, and after rollout — not just to audit for failure, but to proactively design systems that account for layered disadvantage from the start.