Business Intelligence (BI)
Decisions from data you can trust, at the speed you make them.
Business Intelligence is dashboards and reporting built on a governed data layer — one definition of every metric, refreshed continuously, and reachable by the people who need it without raising a ticket. When two reports disagree, it is because the business changed, not because the SQL did.
What it does
Every company has data. Fewer have a number for revenue that finance, sales, and the board all agree on. Reports are built by whoever was asked, against whichever table they found, with whichever definition seemed right at the time. Dashboards multiply, refresh on their own schedules, and quietly diverge. Eventually leadership stops trusting any of them, and decisions go back to instinct with a chart attached.
We build BI as a governed layer rather than a collection of reports. Source systems land in a warehouse. Models transform them into clean, tested tables. A semantic layer defines each metric once — revenue, churn, margin, utilisation — with its logic written down and versioned. Dashboards, self-service exploration, and analytics embedded in your own applications all read from that layer, so they agree by construction. Quality checks run on every refresh, lineage shows where any figure came from, and alerts fire when a metric moves in a way it should not.
Capabilities
Data warehouse and modelling
Ingestion from operational systems into a cloud warehouse, with tested, versioned transformation models that turn raw tables into clean, documented facts and dimensions.
Semantic layer with governed metrics
Each business metric defined once, with its formula, grain, and owner recorded. Every dashboard and query uses the same definition, so the number is the same wherever it appears.
Self-service dashboards and reporting
Curated dashboards for each function and an exploration surface where analysts and managers answer their own questions against governed data — without waiting on a data team ticket.
Embedded analytics
Charts, tables, and metrics rendered inside your own products and portals, with row-level security so each customer or user sees exactly their data and nothing else.
Alerting and anomaly detection
Thresholds and statistical baselines on key metrics, with notifications to the owner when a value moves outside its expected range — before someone notices it on a monthly report.
Data quality and lineage
Freshness, completeness, and consistency tests on every refresh, and lineage from any dashboard figure back through the models to the source system and field it came from.
Standards and technologies
Built on open, widely adopted tooling and formats so the platform stays portable, auditable, and understandable by any analyst you hire.
- dbt and the modern data stack
- Version-controlled, tested SQL transformations with documentation generated from the models themselves.
- Open table formats
- Parquet and Apache Iceberg for warehouse storage that any engine can read, avoiding lock-in to a single vendor's format.
- Row-level security
- Access policies enforced in the data layer so the same dashboard shows each user only the rows they are entitled to.
- GDPR data minimisation
- Personal data pseudonymised or excluded from analytical models unless a documented purpose requires it.
- DAMA-DMBOK
- Data governance roles, ownership, and stewardship structured on the Data Management Body of Knowledge framework.
- ISO/IEC 27001
- Information security controls for a platform that concentrates the company's most sensitive operational data in one place.
Why businesses bring us in
— One number for revenue, not five
The semantic layer means finance, sales, and the board all read the same definition. Meetings stop being about whose figure is right and start being about what to do.
— Self-service without the chaos
Analysts and managers explore freely, but against governed models with tested definitions. Freedom to ask questions, without the freedom to invent a new version of the truth.
— Lineage from dashboard back to source
Click any figure and see the model, the transformation, the source table, and the field it came from. Trust is built from traceability, not from a data team's assurance.
Part of the Enterprise suite
Enterprise Resource Planning (ERP)
Finance, inventory, and operations on one system of record.
Customer Relationship Management (CRM)
Pipeline, accounts, and service in a single view.
Supply Chain Management (SCM)
Sourcing to delivery with live visibility at every step.
Human Resource Management (HRM)
People, payroll, and performance from hire to retire.
Enterprise Asset Management (EAM)
Asset lifecycle, maintenance, and uptime tracking.
Bring us the report nobody trusts.
We will trace where its numbers come from, show you why they disagree with the other report, and rebuild the metric once — governed, tested, and the same everywhere it appears.