Practice 07 · Data engineering, BI, embedded analytics

Your data is already valuable. Most of it is just unread.

East African enterprises generate enormous volumes of transaction data, customer data, and operational data — M-Pesa receipts, EMR entries, ERP journals, IoT sensor readings. The firms that use it to make faster, better decisions have a structural advantage over the firms that don't. We build the infrastructure and the interfaces that close that gap.

Discuss a data programme See case studies
Data warehousing & lakehouses BI dashboards & reporting Real-time analytics pipelines Embedded analytics in products
Data analytics team reviewing dashboards and business intelligence — East Africa
What we do

Seven capabilities inside the Data & Analytics practice.

Engage one or combine several — most programmes start with a data audit and a prioritised roadmap.

Data warehouse & lakehouse

A single source of truth for your business data — structured and unstructured, transactional and historical, on-cloud or on-prem.

  • Warehouse architecture (BigQuery, Redshift, Synapse)
  • Lakehouse design (Delta Lake, Apache Iceberg)
  • ETL / ELT pipeline engineering
  • Master data management

Business Intelligence & Reporting

Executive dashboards, operational reports and self-serve analytics — built on your data, designed for the questions your leadership actually asks.

  • Power BI, Looker, Metabase
  • KPI framework design
  • Automated financial & regulatory reports
  • Board-level dashboards

Real-time analytics pipelines

Stream processing for data that can't wait until batch night — M-Pesa transaction monitoring, fraud detection, IoT sensor analytics, live operations dashboards.

  • Apache Kafka / Flink streaming
  • Real-time fraud and anomaly detection
  • M-Pesa & payment stream processing
  • IoT telemetry ingestion

Embedded analytics

Analytics built into your product — so your customers see their own data inside your application, not in a separate report they have to request.

  • In-app dashboards and charts
  • Customer-facing analytics portals
  • White-labelled BI components
  • API-first analytics delivery

Predictive analytics & ML

Credit scoring, churn prediction, demand forecasting, fraud propensity — models trained on your data, deployed in your systems, monitored in production.

  • SME and retail credit scoring
  • Customer churn and lifetime value
  • Demand and inventory forecasting
  • MLOps and model monitoring

Data governance & quality

A model is only as good as the data it runs on. We build data quality frameworks, governance policies and lineage catalogues that make your data trustworthy.

  • Data quality rules and monitoring
  • Data catalogue and lineage
  • GDPR and data-privacy compliance
  • Data stewardship operating model

Regulatory & compliance reporting

Automated CBK, CMA, KRA, NHIF and sectoral reporting — removing the manual reconciliation burden and reducing the risk of a late or incorrect return.

  • CBK prudential reporting
  • KRA eTIMS data validation
  • NHIF/SHIF claims reconciliation
  • ESG & sustainability reporting
The difference

What well-designed data infrastructure actually changes.

Without it

  • ✕ Month-end close takes two weeks because finance reconciles data manually from six systems
  • ✕ The CEO asks "what's our NPS this week?" and the answer takes three days to produce
  • ✕ Regulatory returns are prepared in Excel by one overloaded analyst the night before the deadline
  • ✕ You know a segment of customers is churning but not which one or why until they've already left

With it

  • ✓ Finance closes in two days because the warehouse does the reconciliation automatically overnight
  • ✓ The CEO opens a dashboard on their phone on Monday morning and the number is there
  • ✓ Regulatory returns are generated from the warehouse on demand, reviewed and submitted in a morning
  • ✓ A churn model flags at-risk customers 30 days before their behaviour changes — retention acts first
Outcomes

What our data clients have achieved.

2 days
Month-end close (from 14)
Real-time
Executive dashboards
−80%
Manual reconciliation effort
10+
Sectors with active data programmes

Ready to make your data work harder?

We start with a two-week data audit — mapping your sources, identifying the highest-value use cases and sizing the effort. Fixed price, no commitment beyond that.