Data Architecture & Modelling

The Problem

A poorly designed data model is behind most Power BI headaches: slow reports, tangled DAX, numbers that don't add up. Many-to-many relationships, flat wide tables, calculated columns where measures should be, and inconsistent grain — these are the silent killers of BI projects.

Fix the model and everything downstream gets easier.

How We Tackle It

We follow a Kimball-inspired dimensional modelling methodology, adapted for modern cloud platforms. The goal is a model that is:

• Fast — aggregated at the right grain, with clean relationships
• Accurate — single source of truth, consistent definitions across every report
• Maintainable — easy to extend without breaking what's already live
• Self-documenting — clear naming conventions, a data dictionary, and a relationship map

We always start from the business questions backwards. What decisions do your stakeholders need to make? What metrics matter? The model follows from the answers — not the other way around.

What You Get

• Entity-relationship diagrams (current state and target state)
• Dimensional model design (star or snowflake schema)
• Semantic layer specification (Power BI or Microsoft Fabric)
• Data dictionary — tables, columns, and measures fully documented
• Naming convention guide
• Relationship strategy and cardinality documentation
• Physical implementation (tables, views, stored procedures)
• Power BI dataset / semantic model build-out

Tools & Technologies

• Power BI Desktop (semantic model development)
• Microsoft Fabric (lakehouse / warehouse layer)
• SQL Server / Azure SQL Database (T-SQL)
• DAX (measures, calculated tables)
• Power Query M (data transformation)
• LucidChart / Draw.io (architecture diagrams)

Who This Is For

• Organisations building their first data warehouse
• Teams whose Power BI reports are slow, unreliable, or hard to maintain
• Companies migrating from legacy reporting tools to a modern BI platform
• Architects who want a second opinion on an existing model design