Building a Data Warehouse for a Leading UK Technology Provider

The organisation relied on multiple systems to manage customer relationships, finance, service delivery, marketing, and more. However, these platforms operated in silos. Extracting and reconciling data from each system individually was time-consuming and prone to inconsistency, resulting in manual workarounds and unreliable reporting.

Without a unified data view, the business struggled to meet reporting requirements and achieve complete, reliable insights across departments.

To overcome these challenges, DSP implemented Microsoft Fabric to unify data sources, streamline processes, and enhance reporting intelligence. With the customer’s existing expertise in Power BI, the transition to Fabric was smooth and unlocked a range of new capabilities.

The ELT (Extract, Load, Transform) pipeline in Fabric incorporated the following stages:

Ingestion – Bringing Data Together

Data from multiple business systems (CRM, finance, and delivery) was connected into OneLake, Microsoft Fabric’s centralised data lake. This established a single source of truth, eliminated data silos, and ensured all teams worked from consistent and accurate datasets.

Incremental data loads and management of slowly changing dimensions were implemented to maintain data accuracy and freshness without requiring full reloads.

Transformation – Consistent and Reusable Logic

All key transformation logic was handled within the Microsoft Fabric data warehouse layer using Dataflows Gen2 and Spark Notebooks. This centralised approach ensured that data transformations were consistent, reusable, and efficient across a variety of reporting and analytics scenarios.

By shifting the heavy lifting of transformation to Fabric, Power BI reports now require minimal or no additional processing, improving report performance and standardising business logic. This approach also provides a scalable foundation that supports growing data volumes and complexity.

Key transformation activities included:

• Standardising and cleansing raw data from diverse source systems
• Applying business rules and calculated metrics
• Creating conformed dimensions for consistent reporting across subject areas
• Managing historical data changes using Slowly Changing Dimensions (SCDs)

Modelling – A Central Lakehouse for Analytics

Fabric’s Lakehouse architecture was used to organise and store curated business data, enabling both BI reporting and advanced analytics to be delivered from the unified, governed platform.

Reporting – Faster and Smarter Insights

Fully integrated Power BI dashboards were deployed across the organisation, delivering real-time, dynamic insights. Reports now load faster, are easier to share, and consistently reflect standardised metrics across all departments.

AI & Forecasting – Future-Ready Platform

Forecasting was integrated using Prophet models in Fabric’s Data Science environment, with plans to expand AI-powered insights and Copilot-style assistants to further guide decision-making. In addition, LLM-based Retrieval Augmented Generation (RAG) solutions are being explored for internal data search and intelligent automation, leveraging Fabric data combined with Azure OpenAI.