Core analytics stack
- Cloud and hybrid analytics platforms
- Databricks, Fabric, Power BI, and equivalent ecosystems
- Dimensional and star-schema engineering
- DAX engineering and model performance tuning
Star Schema & Dimensional Modeling
We design analytics-ready data models with clear fact/dimension boundaries to improve metric consistency and reduce model complexity.
- Conformed dimensions and grain definition
- Slowly changing dimension strategies
- Model standards for long-term maintainability
Semantic Model Architecture
Semantic layers with governance, reuse, and performance as first-class engineering concerns, including Power BI/Fabric where appropriate.
- Dataset/semantic model domain strategy
- Role-level security and governance controls
- Deployment pipelines and release discipline
Dashboard Engineering
Executive and operational dashboards designed for action, with clear narrative structure and trusted KPI interpretation.
- Decision-centric information architecture
- Visual clarity and interaction design
- Portfolio rationalisation for dashboard estates
DAX & Performance Optimisation
Advanced measure engineering and model tuning to improve refresh times and query responsiveness across enterprise-scale workloads.
- Measure design patterns and refactoring
- Storage/compute trade-off tuning
- Performance diagnostics and remediation
Analytics delivery process
- 1Audit: evaluate model quality, KPI logic, and performance constraints.
- 2Design: define target star schema and semantic architecture.
- 3Build: implement models, measures, and dashboards with governance built in.
- 4Enable: transfer standards and operating model to your internal team.
Analytics that leadership can trust
We focus on clarity, repeatability, and adoption so analytics becomes a dependable operating capability, not a collection of one-off reports.