The Problem
The organisation relied on two primary data sources — Microsoft Dynamics CRM for sales and customer data, and SharePoint for operational records and documents. Neither system was built with analytics in mind, and the gap between them was filled by a sprawling set of manual reports produced by hand each week.
These reports took significant time to produce, were inconsistent in how they defined key metrics, and were already out of date by the time they reached decision-makers. There was no single version of the truth — different teams often worked from different numbers for the same period.
What We Built
End-to-End Azure Data Pipeline
We designed and built a fully automated data pipeline on Azure, running from source systems through to Power BI dashboards. Azure Data Factory handles the orchestration and ingestion — pulling data from Dynamics CRM and SharePoint on a scheduled basis, with full error handling and alerting built in.
Centralised Data Lake and Warehouse
Data lands in Azure Data Lake Storage, where it is processed and transformed using Azure Data Factory pipelines into Azure SQL. The transformation layer applies consistent business logic — metric definitions, date hierarchies, CRM stage mappings — so every dashboard draws from the same conformed model.
- Azure Data Lake: Raw data storage — every extraction preserved for auditability and reprocessing.
- Azure SQL: Conformed, query-optimised data layer purpose-built for reporting performance.
- Analysis Services: Semantic model layer with pre-calculated measures and KPIs, allowing Power BI to run fast against large datasets.
Seven Governed Dashboards
We replaced every manual report with a purpose-built Power BI dashboard. Each dashboard was designed in close collaboration with the team that owned the report — so the new version answered the same questions, faster and more reliably, without requiring anyone to produce it manually.
Tech Stack
The Results
Every manual report has been retired. Seven governed Power BI dashboards now deliver accurate, consistent data to the teams that need it — automatically, every day. Metric definitions are consistent across the business for the first time. Decision-makers spend less time questioning the numbers and more time acting on them.
Key Takeaways
- Manual reporting is never just an inconvenience — it's a trust problem. When two people produce the same number differently, the business stops trusting either of them.
- Azure Data Factory is a reliable orchestration layer, but the real value is in the semantic model. Getting the business logic right in Analysis Services is what makes dashboards genuinely useful rather than just pretty.
- The best dashboard replacements happen when you involve the report's original owner in the design. They know what matters. Listen to them.