Data & BI
Figures you can actually decide on
A dashboard is only worth having if you can explain where each figure comes from and why it differs from the one next to it.
The problems we address
What brings you here
Two departments report two different figures
The same indicator, two implicit definitions. The meeting turns into a debate about the data.
Monthly reporting takes a week
Manual extracts, rework in a spreadsheet, formatting. Repeated identically every month.
The data is dirty
Duplicate customers, inconsistent units, dates in the wrong format, free-text fields that were never normalised.
You look at the past, never ahead
The indicators record what happened. Nobody raises the alarm before the critical threshold is crossed.
Deliverables
What you receive
- Consolidation of data from several systems
- Cleaning, deduplication and normalisation
- Automated and monitored data pipelines
- An indicator dictionary with definitions and calculation methods
- Dashboards by department and by level of responsibility
- Periodic reports generated and distributed automatically
- Alerts on thresholds and on unusual deviations
- Predictive models where the history and the use case justify them
Benefits
What it concretely changes
One definition per indicator
The indicator dictionary puts an end to arguments about how a figure is calculated.
Time given back to the teams
What took a week of manual rework now happens on its own.
Data quality that is measured
We track completeness and anomaly rates: quality becomes an indicator like any other.
Anticipation rather than observation
Alerts on trends and deviations, before the problem shows up in the monthly result.
Features
What we can do in this area
- Connectors to databases, files, APIs and the IoT platform
- Historisation of data so that change can be analysed
- Pre-computed aggregates for fast dashboards
- Filters by period, site, product and team
- Export to spreadsheets for one-off analysis
- Automatic distribution by email at a set interval
- Read permissions by scope
- Anomaly detection and time-series forecasting where relevant
Our approach
How we go about it
Questions before dashboards
We first list the decisions to be made, then the indicators that inform them.
Audit of the source data
Volume, completeness, consistency, freshness. The limits are documented and owned.
Pipeline
Automated, logged loading, with an alert on failure or on an abnormal volume.
Dashboards
One page per use, with the definitions reachable from each indicator.
Adoption
User training and adjustment after a few cycles of real use.
Architecture
How it is built
We always separate operational data from analytical data: querying the production database for reporting always ends up slowing production down.
Sources
Application databases, deposited files, partner APIs, IoT telemetry.
Ingestion
Scheduled or event-driven extraction, with error recovery and idempotency.
Raw zone
The data kept exactly as received, so a transformation can be replayed.
Transformation
Cleaning, normalisation, joins, business calculations — versioned like code.
Analytical model
Fact and dimension tables, aggregates, historisation of changes.
Delivery
Dashboards, distributed reports, exports and APIs for your own tools.
Technologies
Relevant technologies
- Python
- Pandas
- SQL
- PostgreSQL
- MySQL
- TimescaleDB
- InfluxDB
- Elasticsearch
- Metabase
- Apache Superset
- Apache Kafka
- Docker
- Grafana
Industries covered
The industries where we apply it
Frequently asked questions
Frequently asked questions
Data and Business Intelligence
Do we need a data warehouse to start?
Can you do prediction?
Our data is in poor shape — is that a blocker?
Who will be able to create new dashboards?
Africa Tech Services
A project around “Data & BI”?
A first thirty-minute conversation, with no commitment. We will tell you plainly whether we are the right partner — and if not, we will point you elsewhere.