7 August 2025
Analytical solution in Fabric to ensure Scalability, Single Source of Truth, and Autonomy
Analytical solution in Fabric to ensure Scalability, Single Source of Truth, and Autonomy
Challenge
In the highly regulated and competitive context of the pharmaceutical industry, the ability to make decisions based on reliable and accessible data is crucial for maintaining operational excellence and driving innovation. A leading pharmaceutical company embarked on a project to transform its data platform, aiming to enhance the quality of information, support daily operations more effectively, and foster a more innovative data culture. The initiative centred on the adoption of Microsoft Fabric as the foundation for a new analytical architecture.
Solution
The project’s mission was to completely overhaul the company’s Data Warehouse to ensure a modern, scalable, and secure foundation. The starting point of the project was complex, involving significant technical and organisational challenges. One of the major hurdles was the need to integrate a variety of complex data sources — including an Oracle-based ERP system (JD Edwards) — with the Microsoft Fabric environment. This demanded a high level of technical effort to ensure consistency and quality across all integrated data. Additionally, the platform itself was still evolving, with frequent updates that impacted stability and required ongoing adaptation. Finally, the company faced the issue of poor data quality in its source systems, which had historically been distorted by inaccurate reporting, further complicating the transformation process.
Benefits
The transformation delivered substantial improvements in how the organisation manages and uses its data. One of the most significant benefits was the creation of a centralised and reliable data platform — a single source of truth that replaced multiple fragmented and often contradictory data systems. This allowed for more consistent and confident reporting across the organisation. Another key benefit was the elimination of information silos. The new architecture enabled the efficient cross-referencing of data from different systems, promoting a holistic understanding of the business and making it easier to identify both opportunities and risks.
In addition, the adoption of self-service models with Power BI brought autonomy and agility to business users, allowing them to explore data independently and quickly generate insights relevant to their needs. Finally, the organisation gained a scalable, modern platform that not only supports current initiatives but is also designed to evolve with future analytical demands.
Statistics
70%
reduction
in the effort required for sales vs activity analysis
40%
reduction
in the effort needed to create new models
Practical applications
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Internal and external sales analysis
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Analysis of visits to doctors and pharmacies
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Monitoring of contracts carried out (level of achievement)
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Monitoring of new business opportunities and their realisation
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Monitoring of salespeople's targets
Examples
With the new solution in place, managers can take immediate action based on data insights. For instance, if a salesperson has ten open leads that have seen no progress for over 60 days, their manager can intervene directly to understand the underlying issues and unblock the process. In the case of a new product launch, it becomes possible to assess which countries are generating the strongest sales performance, helping inform market strategies. Similarly, if 50 leads remain unconverted, teams can investigate the root causes and adjust their client engagement tactics accordingly. Additionally, sales data by product and category allows for better portfolio management, enabling strategic decisions such as prioritising growing products, phasing out underperforming ones, or repositioning offerings that show declining trends.
