Solving the Data Poverty That Holds Everyone Back
Walk into any open-air market in Africa, and you’ll find more economic activity than in many boardrooms. But ask a simple question, “What’s the average margin on your best-selling item?” and you’ll get silence, guesses, or stories. Not data.
The truth is: we’re running billion-dollar economies on vibes.
In most African markets, people trade, produce, distribute, and lend with barely any structured data. The pharmacist in Minna doesn’t know if she’s overpricing her meds. The cassava miller in Akure doesn’t know if gari demand is going up or down. A credit officer in Nairobi has no reliable insight into the repayment history of the mechanic he’s assessing.
We don’t just have a data infrastructure problem; we have a data culture void. The systems don’t exist, the habits aren’t built, and the incentives don’t align. It’s not just that the data is messy. It’s that it never existed in the first place.
People should be building:
1. Tools that turn everyday paper into structured insight
From ledgers to market boards to WhatsApp orders scribbled in pidgin, this is where African business happens. Build tools that respect that reality and help digitize it with minimal friction. OCR for smudged notebooks. Photo-to-table tools for inventory. Voice dictation that understands code-switching between English and Hausa.
2. Community-powered data networks
Every local association has someone who “knows everything”, from market chairmen, union secretaries, to cooperative treasurers. These people already collect data informally. Build tools for them. Let them input daily price ranges, transaction volumes, or buyer sentiment, and in return, give them dashboards they can use to negotiate better.
3. Anonymous benchmarking platforms
Nobody wants to expose their books. But everyone wants to know how they’re doing. So give businesses a way to trade data for insight. Let a POS operator share anonymized daily sales and see how their numbers compare to peers in similar neighborhoods. A simple feedback loop can change how people think about growth.
4. Cleansing and reconciliation engines for local data
Dates in pidgin. Measurements in “paint buckets.” Addresses that say “beside the yellow gate after the big mosque.” African data is messy, but it’s real. Build engines that learn how to normalize this chaos: turn “two paint buckets of rice” into 40kg. Recognize “Mama Tolu” and “Mummy Tolu” as the same customer.
What does success look like?
- A roadside electronics dealer in Kano knows that Wednesdays are his slowest days, so he adjusts stock delivery and staffing.
- A grain seller in Enugu uses community price trends to price competitively and grow his customer base.
- A micro-lender in Kigali plugs into a reliable database of informal repayment patterns, not guesses, to underwrite smarter.
- An SME in Ibadan sees that their average transaction size is 20% lower than similar businesses across town, and starts asking why.
Data is not oil. It’s oxygen. Without it, our systems stay informal, our businesses stay small, and our risks stay unmeasurable.
We can’t afford to keep guessing. The informal economy is already too big to run without insight. It’s time to build the rails that turn stories into signals.
Build tools that help people see what they couldn’t see before, and act with the confidence they’ve never had.