Data Infrastructure & Engineering
One trustworthy set of numbers, updated automatically, that everyone in the organisation reads the same way.
Situations you will recognise.
The monthly meeting starts with an argument about whose number is right instead of what to do about it.
If that person is on leave, the board pack is late. That is not a reporting process, it is a single point of failure.
Field offices submit on paper or by email, and someone retypes it before anyone can analyse it.
You cannot model what you cannot trust, and nobody currently owns the definition of a clean record.
You can't run AI on bad data. Most African organisations have data scattered across spreadsheets, PDFs, and disconnected systems, and no one is accountable for its quality.
Full inventory of every data source, its owner, its quality, and its trustworthiness.
Warehouse design, ETL/ELT pipeline blueprints, and governance model.
Production-grade pipelines with monitoring, alerting, and lineage.
Data ownership matrix, access policies, retention rules, and quality SLAs.
- Data warehouse design
- Production ETL pipelines
- Data governance policy
- Dashboard layer
- Team enablement & training
Data Infrastructure & Engineering: common questions
- What does Kwish's Data Infrastructure & Engineering service deliver?
- One trustworthy set of numbers, updated automatically, that everyone in the organisation reads the same way.
- What problems does Data Infrastructure & Engineering solve?
- It is built for situations like these: two departments bring two versions of the same figure; your reporting depends on one person's spreadsheet; data lives in pdfs and printed returns; every ai proposal stalls on data quality. You can't run AI on bad data. Most African organisations have data scattered across spreadsheets, PDFs, and disconnected systems, and no one is accountable for its quality.
- What do we receive at the end of the engagement?
- Data warehouse design, Production ETL pipelines, Data governance policy, Dashboard layer, Team enablement & training. Your team owns the system after handover, with documentation, training sessions and 90 days of support.
- How does Data Infrastructure & Engineering affect revenue and cost?
- On the revenue side: Live pipelines let commercial teams adjust pricing, stock and routes on current data instead of last quarter's summary. On the cost side: Automating recurring reports targets a 50 to 70 percent reduction in the hours spent producing them each month. Any percentage ranges we quote are targets we work towards and measure with you, not guarantees.
- How does Kwish run a Data Infrastructure & Engineering project?
- Five stages: discovery (one week inside your workflow. we map the process, the data and the people who touch it.); scoped quotation (a fixed scope, milestones and a price. no open-ended retainers to start.); build (we build in two-week increments against your real data, with you reviewing each one.); go live (integration with the systems already in use, load testing, and a phased switch-on.); team training (your staff run the system. documentation, sessions and 90 days of support after handover.).
- Which countries does Kwish work in?
- Kwish Technologies operates from offices in Uganda, Kenya, Sweden and Canada, and delivers across Africa remotely and on site.
Keep exploring
Where Data Infrastructure & Engineering fits across Kwish
The sectors we deliver this in, the research behind our approach, and case studies where we have shipped it.
Sectors where this applies
Research and analysis
Two sides of the same investment.
The ranges below are targets Kwish works towards on this kind of engagement, based on our own deployments. They are targets, not guarantees, and we agree the measurement method with you before we build.
How this expands revenue
Live pipelines let commercial teams adjust pricing, stock and routes on current data instead of last quarter's summary.
A unified customer view lets marketing spend concentrate on the segments that convert, raising return on the same budget.
Clean, governed data can be packaged into reporting or benchmarking services that your customers will pay for.
How this cuts cost
Automating recurring reports targets a 50 to 70 percent reduction in the hours spent producing them each month.
Consolidating duplicated databases and unused tooling targets a 15 to 25 percent cut in infrastructure spend.
The costliest waste is stock, staffing or spend committed against numbers that were already out of date. Live pipelines remove that class of error.
Discovery to live system to a team that can run it.
One week inside your workflow. We map the process, the data and the people who touch it.
A fixed scope, milestones and a price. No open-ended retainers to start.
We build in two-week increments against your real data, with you reviewing each one.
Integration with the systems already in use, load testing, and a phased switch-on.
Your staff run the system. Documentation, sessions and 90 days of support after handover.
Work you can inspect.
Ready to see what this saves you?
Send us the workflow you want fixed. We reply with a scoped quotation, not a brochure.
Most quotations answered within one business day.