With the implementation of a new ERP system, Rf-t faced a new chapter in their data-driven operation. Their wish? A single streamlined, reliable reporting environment in Power BI. But the raw data from Dynamics was not enough to build meaningful dashboards right away.
Rf-t chose Jet Data Analytics as the data warehouse technology between Dynamics and Power BI. This tool was already known within their team, and provided enough flexibility to deal with complex business logic. Squadron was engaged to guide and set up the entire process - from data modelling to visualisation.
Working closely with Rf-t's internal BI team, we developed a modular architecture built around data cubes per domain: Sales, Inventory, Finance, Purchase, etc. Those cubes were fed from a four-layer architecture within Jet, with each layer providing an additional level of data transformation. Think combining tables, calculations, cleansing, filtering and logic implementations.
This approach ensured that Power BI reports did much more than just visualise: they offered real insights, tailored to the company. Think stock valuations, margin reports and exception handling via smart dashboards.
What started as an intensive project with several external partners has grown into a sustainable collaboration. Squadron's active role evolved towards maintenance & optimisation, with flexible support when needed. Among other things, we ensure that nightly data syncs remain running, perform updates in case of data contamination, and guide Rf-t in creating new insights - such as recent margin reporting or in-depth analysis.
We also collaborated on master data reports to increase the quality of input data - crucial for reliable output. Meanwhile, Rf-T has also built up internal expertise, so together we form a balanced team.
An important lesson during this project: Power BI is a great tool, but not the right tool for everything. Especially for financial reporting - such as balance sheet and P&L - you run into limits. We therefore recommended additional alternatives such as BrightAnalytics, which may or may not be implemented in the future.
However, the current solution remains performant and flexible. In addition, Rf-t is also looking ahead: they show interest in moving to a full Azure-based Medallion architecture, with which they want to operate even closer to the Microsoft ecosystem.
Conclusion: with an approach that combines technical depth with customer proximity, Squadron helped Rf-Technologies achieve a robust, scalable and insightful data platform. For four years now.