Introduction
During a recent project, implementing a Finance dataset in Power BI helped reduce our client’s month-end closing time by more than three days. By automating data extraction and manual processing tasks, the controlling team was able to refocus on business analysis, moving away from time-consuming reporting and data consolidation tasks.

This time and energy savings, which allowed controllers to concentrate on high-value activities like forecasting and critical analysis, perfectly illustrates the benefits of a well-designed and tailored BI platform.

Today, many companies—regardless of size—have integrated Business Intelligence (BI) into their IT landscape. And by BI, we don’t mean makeshift pivot tables or internally cobbled-together modules, but rather a complete Business Intelligence ecosystem.

The three pillars of this ecosystem—integration, storage, and visualization—have become essential allies for analysts and financial controllers. They are at the heart of many digital transformation projects.

Once reserved for a few players with on-premise infrastructures and significant budgets, BI is now accessible and widespread thanks to cloud and SaaS offerings.

The market is vast: unified “all-in-one” platforms, on-premise tools, cloud solutions, scalable subscriptions, pay-as-you-go or monthly pricing… This is good news: it’s easier to find a suitable solution. But this abundance also makes it harder to navigate: what do I really need?

In this article, I’ll share insights from my experiences with clients across various sectors to highlight the key factors to consider when choosing a BI architecture.

Whether you’re in the process of creating, improving, or migrating your BI architecture, this multi-part guide will provide you with a practical methodology, grounded in best practices and enriched with real-world feedback—successes… and sometimes failures.