Who is this
content for?
The content is a resource designed for three main groups involved in shaping data governance in African contexts: Policy Makers, Regulators, Data Practitioners and Technical Leads, Civil Society Representative/Advocate
This content matters because data systems increasingly determine access to services, finance, protection, and opportunity and applying an intersectional lens is important.
Policy Maker
Ignoring intersectionality in data systems creates systemic bias and regulatory risk, causing policies designed for a “standard” user to fail at the “last kilometer” and produce gaps between intent and real-world outcomes.
Data Practitioners
and Technical Leads
Ignoring overlapping identities in system design creates functional failures, undermining trust in digital systems and causing low program uptake because technical requirements mismatch users’ lived realities.
Civil Society Representative/
Advocate
By using an intersectional lens, Civil Society Representatives can translate the complex, overlapping realities of marginalized people into the technical proof needed for advocacy.
Policy effectiveness depends on access and uptake.
Data-related legal instruments increasingly determine eligibility for public services, social protection, identification, and finance. When these systems overlook how identities overlap, entire groups could be excluded, leading to :
- Low uptake of programs [e.g. A registration requirement for proof of home ownership, or providing a utility bill could inadvertently exclude women in rural areas. In some regions, land titles are only held in a man’s name (gender barrier) and the nearest government office is a day’s journey away (geographical barrier), making these women technically ineligible for the health services intended for them.]
- Uneven implementation across regions [e.g. Credit scoring guidelines that only recognize formal salary slips flag young people with disabilities as “unbankable.” Because they work flexible hours in the informal economy (economic status) and face physical mobility challenges (disability), they are automatically blocked from small business loans that their peers have better access to.]
- Gaps between policy intent and lived outcomes [e.g. A digital verification process that requires a smartphone and a permanent residency permit could fail elderly displaced persons. They may not be comfortable using smartphones (age) combined with the challenge of lacking the specific residency papers (migration status) required to create an account.]
As any political tool, data governance policies will encounter challenges when people begin interacting with the systems [e.g. when they try to register, apply, or comply.]
This is normal, as there will always be room for improvement. However, approaching data governance from a multi-dimensional, intersectionality lens can reduce the obstacles and/or negative impacts imposed upon people from the most vulnerable and/or marginalised groups in their daily lives.
Approaching data governance with an intersectional lens helps identify where and why this breakdown could happen.