Datakafe Intelligence builds the transformation infrastructure that turns your scattered M-Pesa records, ERP exports, and spreadsheets into decisions your leadership team can trust — in hours, not weeks.
These are not separate problems. They are six faces of one root cause — the absence of a transformation layer between your raw data and your decisions. Most Kenyan medium enterprises have all six right now.
Finance and Sales report different revenue numbers. Nobody knows which is right. Trust collapses. Decisions revert to gut feel.
Your best analyst spends 80% of their week cleaning data. They're a bottleneck. When they leave, everything breaks.
Pipelines break quietly. Dashboards update with wrong numbers. You discover it in a board meeting, not from an alert.
One person knows every edge case in your pipeline. When they leave, months of data intelligence leaves with them.
A Google Sheet that started as a quick fix is now critical infrastructure — undocumented, untested, one mistake from disaster.
The CEO asks Monday. The answer arrives Thursday. Competitors who answer in hours are already acting.
We map every source — ERP, M-Pesa, CRM, spreadsheets — identify what's broken and missing. The blueprint for everything.
We design version-controlled data models encoding your business logic — one agreed definition of every core metric.
Every model ships with quality tests baked in. Every pipeline has alerts. Nothing is a black box. Bad data never reaches a report.
We train your team. Knowledge lives in documented code — not our heads. You own everything we build.
6–8 weeks. Takes you from raw warehouse to a clean, tested, documented transformation layer. The core of what we do.
Learn more →Deep diagnostic of your current infrastructure. We surface transformation debt and give you a clear, prioritised roadmap.
Learn more →Specialists in turning M-Pesa transaction data into structured models that reveal behaviour, patterns, and risk signals.
Learn more →A senior data engineering partner without the full-time hire. We extend your transformation layer as your business grows.
Learn more →"We don't just connect your data. We build the foundation that makes every business question answerable — in hours, not weeks — and that stays working long after we leave."
John Waweru · Founder, Datakafe Intelligence · Nairobi
Tell us what your data situation looks like. We'll tell you exactly what's holding you back — and what it would take to fix it.
Most businesses in Kenya are generating more data than ever. Almost none of them can actually use it.
We believe every growing business in this region deserves data infrastructure that works — tested, documented, and actually usable for decisions.
The modern data stack has transformed how companies in the US and Europe operate. We bring that same infrastructure — adapted for East African realities, M-Pesa data patterns, and local tech ecosystems.
We explain every decision. You should never wonder what's happening inside your own data infrastructure.
Infrastructure your team can operate and extend is worth more than something only we understand.
We adapt data engineering patterns for the data sources and business realities of this region.
Every decision we make prioritises one outcome: how fast your team can answer questions after we leave.
If it hasn't been tested, it hasn't been finished. We treat untested pipelines like unreviewed code.
Our job is to make ourselves unnecessary. We succeed when your team runs everything without us.
John founded Datakafe Intelligence after spending years watching growing East African companies make critical decisions on untrustworthy data — not because they lacked talent or intention, but because nobody had ever built the transformation layer between their raw systems and their reports. Datakafe is his answer to that gap: a specialist firm that builds what most companies skip, in the way it should have been built from the start.
Focused, high-impact engagements. Clear scope, clear outcomes, clear ownership.
Takes your business from raw, siloed data to a clean, tested, version-controlled transformation layer that the whole organisation can trust. By the end, your team has one agreed definition of every core metric. Every report traces back to documented, tested code.
Before spending on infrastructure, understand exactly what is broken. A deep diagnostic of your current data setup — pipelines, models, documentation, and trust levels across your team. You receive a clear, prioritised report of your transformation debt.
M-Pesa generates the richest transactional data in East Africa. Most companies that process it have no idea what to do with it beyond counting deposits. We take your raw Paybill, Till, B2C, and C2B data and build clean, queryable models that reveal customer behaviour, payment patterns, and risk indicators.
A senior data engineering partner without the cost or complexity of a full-time hire. New data sources get modeled. New business questions get answered. Pipeline health gets monitored every month.
The data exists. It's generated every day. But without a transformation layer, it stays raw, scattered, and unanswerable.
Growing businesses in East Africa are making critical decisions without reliable, timely, or trusted data — not because they lack data, but because nobody has built the infrastructure to connect, clean, and transform it into something answerable.
The result is a company where finance and sales report different revenue numbers, the best analyst spends most of their week cleaning spreadsheets, and answering a basic business question takes days or weeks. This is not a people problem. It is a data transformation problem — and it has a clear, buildable solution.
Every symptom below traces back to the same root cause: the absence of a clean, tested, documented data transformation layer.
When different departments pull data from different sources using different logic, they get different answers. Finance uses confirmed receipts. Sales uses closed deals. Neither is wrong — but because nobody has agreed on a single, version-controlled transformation of raw data into business metrics, the numbers will never match. Leadership stops trusting the data. Decisions revert to gut feel.
Impact: Loss of data trust across the organisationIn most Kenyan medium enterprises, the data analyst spends the majority of their time pulling exports, cleaning columns, joining tables by hand, and rebuilding the same base datasets from scratch for every new question. The analyst — hired to find insights — is trapped in a role that requires no analytical thinking at all. You're paying insight-level salaries for data-entry-level work.
Impact: Wasted talent, slow answers, single points of failureMost data pipelines were built to move data, not to verify it. When an upstream system changes — an API column name shifts, a timezone updates, a join key duplicates — the pipeline continues running silently with wrong data. The dashboard looks fine. The numbers are wrong. The first sign of the problem is a discrepancy in a leadership meeting, not an automated alert.
Impact: Bad decisions made on data that looked valid but wasn'tThe logic that powers your most critical reports often exists only in the mind of the person who built it. The edge cases, the filters, the workarounds — undocumented, unshared, unversioned. When that person leaves, the company loses months of accumulated data intelligence. The replacement spends weeks reverse-engineering work that should have been written down from day one.
Impact: Knowledge debt that costs months to rebuildAlmost every medium enterprise in Kenya has at least one Google Sheet or Excel file that started as a quick fix and became critical infrastructure. It has no version control, no testing, no documentation. Its logic is invisible unless you already understand it. Yet week after week, decisions worth millions of shillings are made based on what it outputs — and nobody has ever audited whether it's right.
Impact: Fragile, unauditable infrastructure hiding in plain sightEast Africa's business environment is moving quickly. Companies that can answer strategic questions in hours have a meaningful competitive advantage over those still waiting two weeks for a manually assembled report. Every month without clean data infrastructure is a month of slower decisions, missed signals, and compounding competitive disadvantage.
Impact: Strategic decisions made too late, or not at allFor a typical Kenyan medium enterprise, here is the honest gap between how fast business questions should be answered and how long they actually take.
| Business Question | Should Take | Actually Takes |
|---|---|---|
| What was our revenue last month? | Minutes | 1–3 days |
| Which product line is most profitable? | Hours | 1–2 weeks |
| What is our customer retention rate? | Hours | 2–4 weeks (if ever) |
| Which sales rep performs best, by region? | Hours | 1 week |
| What is our invoice collection period? | Minutes | 3–7 days |
| Where are we losing customers in the funnel? | Hours | Never answered properly |
| What does cash flow look like in 60 days? | Hours | Done manually, monthly only |
| Which branch is actually profitable after costs? | Hours | 2–3 weeks |
A clean, tested, documented, version-controlled set of transformation models that encode your business logic once — and make every question answerable from that point forward.
Practical writing on data infrastructure for East African businesses. No jargon. No vendor pitches.
This is not a people problem. It is not a communication problem. It is a data transformation problem — and it is silently happening inside your company right now.
Read Post →One email when we publish. No noise, no selling. Just practical writing on data infrastructure for East African businesses.
No long sales process. No pitch deck. Just an honest conversation about what's broken and what it would take to fix it.
Tell us what's going on with your data. We'll respond within 24 hours with either a direct answer, a clarifying question, or a proposal for a free data health conversation.
Nairobi, Kenya — serving businesses across East Africa
Within 24 hours on business days
Growing businesses with 20–500 staff and real data challenges across their systems