Services
Pipelines that run without supervision
Ingestion, transformation and orchestration built as engineered software — version controlled, parameterised, monitored and repeatable.
One definition of the business
Dimensional warehouses and data marts that hold history, resolve conflicting definitions and stay fast as volumes grow.
Reporting built around decisions
Power BI semantic models, governed measures and dashboards designed around the questions the business actually asks.
Numbers people stop arguing about
Validation, reconciliation and governance built into the pipeline, so quality is measured continuously instead of discovered in a meeting.
Azure data platforms, built properly
Lake, warehouse and orchestration layers designed on Azure with environment separation, cost control and deployment automation.
Move the data, keep the meaning
Legacy, ERP and database migrations executed with mapping, reconciliation and parallel-run validation before anything is switched off.
In detail
The capability lists below are the work itself — not a menu of tools. Where a service has its own page, it goes considerably deeper.
Pipelines that run without supervision
Ingestion, transformation and orchestration built as engineered software — version controlled, parameterised, monitored and repeatable.
Data Engineering in depthIncluded
One definition of the business
Dimensional warehouses and data marts that hold history, resolve conflicting definitions and stay fast as volumes grow.
Data Warehousing in depthIncluded
Reporting built around decisions
Power BI semantic models, governed measures and dashboards designed around the questions the business actually asks.
Business Intelligence in depthIncluded
Numbers people stop arguing about
Validation, reconciliation and governance built into the pipeline, so quality is measured continuously instead of discovered in a meeting.
Discuss data qualityIncluded
Azure data platforms, built properly
Lake, warehouse and orchestration layers designed on Azure with environment separation, cost control and deployment automation.
Discuss cloud dataIncluded
Move the data, keep the meaning
Legacy, ERP and database migrations executed with mapping, reconciliation and parallel-run validation before anything is switched off.
Discuss data migrationIncluded
Process
Seven stages, run in order. Each one produces something the next stage depends on, so decisions are made before code is written.
Understand business requirements and data sources.
Who needs which answer, how often, and which systems hold the data behind it.
Analyze existing systems, data quality and architecture.
Profile the data, find the gaps and identify what is safe to build on.
Design the target data architecture.
Layers, storage, modelling approach and orchestration, agreed before build starts.
Build ingestion and transformation pipelines.
Parameterised, source-controlled pipelines with logging and alerting from day one.
Create the data warehouse and semantic model.
Conformed dimensions, facts at a defined grain and governed measure definitions.
Build Power BI dashboards and reports.
Reporting designed per audience, tested against the model and documented.
Monitor, improve and scale the solution.
Tune load windows and query performance, and extend the platform as needs change.
Most engagements start with a conversation about where the data currently breaks down.
