Data Modeling & Semantic Layer
A shared semantic layer ensures 'revenue' means the same thing everywhere.
Without a shared data model, every department defines metrics differently, leading to conflicting reports. A semantic layer, built with dbt models or Power BI datasets, defines business logic once and reuses it consistently everywhere, building trust in numbers and ending debates over the 'right' figure.
- A common language for metrics across the company.
- Fewer arguments over the 'correct' number in meetings.
- Faster creation of new reports and dashboards.
conflicting metrics
faster report creation
to first live model
reconciliation effort
Calculated from the time business units currently spend clarifying conflicting metrics.
- 01
Metrics inventory
We collect existing definitions and calculation logic.
- 02
Model design
We develop a unified, documented data model.
- 03
Semantic layer implementation
We implement the model in dbt or Power BI.
- 04
Business alignment
We validate definitions together with the responsible teams.
- 05
Rollout & maintenance
We establish a process for changes and new metrics.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Financial metrics
Revenue, cost and margin data from finance.
Sales data
Order and pipeline data from the CRM.
Production metrics
OEE and throughput data from production.
Existing reports
Existing Excel and BI reports as reference.
Master data
Organizational, customer and product structures.
Business glossaries
Existing term definitions from individual teams.
Controlling
Binding, validated definitions for core metrics.
Business units
Self-service without back-and-forth with IT.
BI team
Fewer one-off solutions and duplicated logic.
Executive management
Trustworthy numbers as a basis for decisions.
A common language for metrics across the company.
Fewer arguments over the 'correct' number in meetings.
Faster creation of new reports and dashboards.
Traceable, documented calculation logic.
Easier onboarding of new staff into reporting.
With a solid data foundation this use case gets faster, cheaper and far more stable.
An existing platform provides the technical framework for the semantic layer.
Central master data simplifies consistent modeling.
Versioned pipelines make changing definitions easier.
An existing governance model clarifies ownership of metrics.
Business intelligence
Dashboards use the same validated metric definitions.
Data governance & permissions
Definitions are documented and managed centrally.
Data products & self-service
The semantic layer forms the basis for reusable data products.
Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.