Overview
Brill needed a more reliable way to manage and use its data. We helped design and deliver a cloud-based platform that automated data flows, improved visibility and gave teams faster access to the insights they need to make decisions.
The Challenge
Brill’s data processes had evolved over time without a consistent structure. Data was collected from multiple sources and transformed manually, which made the process slow, resource-intensive and difficult to scale.
This created several practical challenges.
Data transformation was fragmented, with different workflows and tools used across the organisation. This made it harder to maintain consistency and trust in the data.
Reporting relied on legacy tools, limiting the ability to generate timely and relevant insights. Teams often spent more time preparing data than using it.
At the same time, the existing on-premise ERP system introduced constraints. Scaling required additional investment in infrastructure, and performance became harder to manage as data volumes increased.
There was a clear need for a more efficient approach – one that could automate data extraction, transformation and reporting, while providing a complete and reliable view of the business.
Brill also had a clear direction: move to the cloud and adopt a Microsoft-based ecosystem to support integration, scalability and long-term flexibility.
The challenge was to move from fragmented, manual processes to a structured and scalable data platform – without disrupting ongoing operations.
The Solution
To support Brill’s data transformation, emagine designed and delivered a cloud-based platform combining automation, scalability and clear ownership of delivery.
Phase 1: Assessing the data landscape
The programme began with a detailed assessment of existing data sources, formats and quality.
This provided a clear understanding of the current environment and defined a structured roadmap for transformation and migration.
Phase 2: Migrating to a scalable cloud foundation
The ERP system and Oracle database were migrated to Microsoft Azure using a phased approach.
This ensured continuity while reducing risk, and created a more scalable and cost-efficient infrastructure to support future growth.
Phase 3: Integrating data and automating pipelines
An automated data pipeline was implemented using Azure Data Factory, Databricks and Logic Apps.
This replaced manual data handling with consistent, repeatable workflows. At the same time, data from multiple sources was consolidated into a centralised data warehouse, establishing a single source of truth for reporting and analysis.
Phase 4: Enabling insights, quality and adoption
Power BI dashboards and reports were developed to provide clear, accessible insights across financial and product performance.
Data validation and QA processes were embedded throughout the pipeline to ensure accuracy and consistency.
Close collaboration with stakeholders ensured the solution worked in practice, supporting real business needs and day-to-day decision-making.
Transformation timeline
Phase 1
Assessing the data landscape
Phase 2
Migrating to a scalable cloud foundation
Phase 3
Integrating data and automating pipelines
Phase 4
Enabling insights, quality and adoption
The Results
The new platform improved both how data is managed and how it is used across the organisation.
Faster data processing and access to insights
Data transformation time was reduced by 40%, enabling faster access to accurate and up-to-date information.
Reduced infrastructure and operational costs
Cloud optimisation reduced infrastructure costs by 30%, improving efficiency while maintaining performance and scalability.
Improved reporting and user satisfaction
Stakeholder satisfaction with reporting increased by 70%, driven by clearer dashboards and easier access to reliable data. Teams can now focus on decision-making rather than data preparation.
Scalable foundation for future growth
A centralised, cloud-based platform provides a flexible foundation that can adapt to changing business needs. This makes it easier to integrate new data sources, expand capabilities and support long-term growth.