Africa Is Collecting More Data Than Ever. Nobody Is Talking About What Happens to It.

Every time you use a mobile money service in Africa, your transaction is recorded. Every time a smart meter reads your electricity consumption, that data is stored. Every time you interact with a government service online, apply for a loan, visit a hospital, or use a ride-hailing platform, data about you is collected, stored, and increasingly analysed by AI systems. This is not unique to Africa. But the regulatory and ethical frameworks that govern what happens to this data in Africa are dramatically less developed than in most other regions — and the gap is growing.

I want to make the case, as an AI practitioner who has spent fifteen years working with large datasets in complex organisations, that this is a problem that requires urgent attention from practitioners, not just policymakers.

The Scale of the Problem

Africa's digital transformation is accelerating the collection of personal data at unprecedented speed. Mobile money has given financial service providers detailed transaction histories for hundreds of millions of people who have never interacted with a formal bank. Utility companies operating smart metering infrastructure have granular consumption data that can reveal household occupancy patterns, equipment usage, and economic behaviour. Telecom operators have location data, communication patterns, and social network information for billions of interactions daily.

Most of this data is collected under terms and conditions that few users read and fewer understand. Most of it is stored under security standards that vary enormously. And increasingly, it is being analysed by AI systems whose outputs affect access to credit, pricing of insurance, allocation of government services, and even law enforcement — with almost no regulatory oversight of how those systems work or whether they are producing fair and accurate results.

What Is Missing

Meaningful Consent

The legal concept of informed consent for data collection exists in most African data protection frameworks. The practical reality of meaningful consent — where individuals genuinely understand what data is being collected, how it will be used, and what the consequences of sharing or withholding it are — is largely absent. When the choice is between consenting to data collection and not accessing a service, consent is not genuinely free. Frameworks that treat this as adequate consent are not protecting individuals — they are providing legal cover for collection without genuine agreement.

I have sat in product design meetings where the question of whether users would consent to a particular data use was answered by pointing to a terms and conditions clause that 0.3 percent of users had read. This is not ethics. It is legal risk management. The distinction matters enormously for the long-term trustworthiness of African digital services.

Algorithmic Accountability

When an AI system trained on African data makes a decision that affects an African individual — denying credit, increasing insurance premiums, flagging a transaction as fraudulent — there is typically no mechanism for that individual to understand the basis of the decision, challenge it, or seek redress. This is not acceptable in any context. It is particularly unacceptable in contexts where the historical data the model was trained on may reflect patterns of discrimination that the model will perpetuate and amplify.

What Practitioners Can Do

Regulatory frameworks will develop slowly. The standards practitioners set in their own work will shape what becomes acceptable much faster. Practitioners can design data collection that is genuinely minimal — collecting what is needed, not everything that is technically accessible. They can build explainability into AI systems from the start rather than retrofitting it. They can advocate within their organisations for data practices that they would be comfortable defending publicly. And they can participate in the policy conversations that are happening in every African jurisdiction about data governance — bringing operational knowledge that policymakers lack and need.

Dr. Sunny Okonkwo

AI Strategist · Head of Data Analytics at one of Africa's largest energy and utility companies. Author of 7 books including the #1 Bestseller The AI Alchemist. Keynote speaker at IIBA, Big Data Summit Canada, Global Summit, and UNICAF.