Define the meaning.
Make analysis reusable.
We translate business concepts into durable analytical models and semantic layers that make measures consistent, discoverable and fast to use.
Data Modeling and Cubes
Value that can be observed—not implied.
Measures are agreed for the engagement and tied to the decision or operating change the work must support.
Metric consistency
Give teams one governed definition for critical measures.
Analysis speed
Reduce repeated joins, calculations and report-specific logic.
Change resilience
Localise business-rule changes in a maintainable semantic layer.
A controlled path from question to operating capability.
Define processes, events, entities, grains and decision vocabulary.
Design facts, dimensions, relationships, hierarchies and histories.
Specify calculations, filters, aggregation behaviour and ownership.
Test results against source evidence and real analytical questions.
Use this service when
- Different reports calculate the same KPI differently.
- Analysts repeatedly rebuild complex business logic.
- Users need governed self-service across shared measures.
Plan for these realities
- A semantic model requires sustained business ownership.
- Premature abstraction can hide important operational detail.
- Cube performance depends on workload, cardinality and refresh design.
A business-language-to-semantic-layer sprint.
Domain workshop · dimensional model · governed measures · user validation
Evidence before promotion.
A Data Modeling and Cubes engagement should leave a useful evidence trail: an agreed baseline, documented decisions, delivered artefacts and an outcome review against the measures defined at the start.
Which metric needs one shared meaning?
Bring the decision, the current friction and the evidence available. We’ll help define a practical first step.
Srebrenička bb · Bosanski Petrovac · office@companionscorp.com
