Data and AI
Data Analytics
Decisions backed by numbers you trust
What this involves
How we approach data analytics at Oasis Hive.
Most organisations are not short of data; they are short of agreement about it. We consolidate the sources, define the metrics properly and build reporting people open.
Data modelling, pipelines and dashboards that give leaders one agreed version of the truth.
What we do
Data modelling and warehousing
A single governed store with consistent definitions for the metrics that matter.
Pipelines and integration
Scheduled, monitored extraction from operational systems with quality checks.
Dashboards and reporting
Executive and operational views designed around decisions, not decoration.
Data quality
Validation, deduplication and reconciliation so the numbers hold up.
What you end up with
- One agreed definition of every headline metric
- Reporting that refreshes without manual effort
- Visible data quality problems instead of silent ones
- Faster answers to routine business questions
Tools and technologies
We choose per project rather than by habit. These are the ones we most often reach for here.
Questions about this service
Delivered properly
One team from scoping through to support
The people who design the system are the people who answer for it in production, so nothing falls between suppliers.
Proof
Where we have done this
Healthcare
Nile Health Group · 2023
Patient records that work when the connection does not
An offline-first records system for a clinic network, so clinicians keep working through outages and files reconcile when the network returns.
Logistics
Kampala Logistics · 2024
Seeing every truck, every kilometre, in real time
Telemetry, fuel monitoring and route analytics across a 140-vehicle fleet, with alerts that fire before a problem becomes a breakdown.
How the work runs
Six steps, no surprises
Step 01
Listen and map
We interview the people doing the work, review the systems in play and write down how things actually flow.
Step 02
Design the solution
We propose what to build first, what to leave alone, what it will cost and what we are assuming. Options come with trade-offs.
Step 03
Build in increments
Working software every two weeks, demonstrated against real data. You can redirect early while changes are still cheap.
Step 04
Verify and secure
Automated tests, security review and load checks run throughout, not at the end, so findings are fixed while the context is fresh.
Step 05
Launch with support
We plan the cutover with you, migrate data carefully, train your team and stay close through the first weeks in production.
Step 06
Run and improve
Monitoring, support and a regular review of what to improve next. Maintained systems stay valuable.
Often paired with
Services that work well alongside this one
Machine Learning
Models that earn their place in production
Forecasting, classification and anomaly detection built to run reliably in production.
Big Data Engineering
Handle the volume without the meltdown
Streaming and batch platforms that ingest at scale and stay affordable and queryable.
Custom Software Development
Systems shaped around your operation, not a template
Internal tools, portals and platforms built around how your team actually works.
Next step
Let us scope data analytics properly
A short discovery gives you a written specification, a schedule and a fixed price. No obligation to continue afterwards.