From SAP to Microsoft Fabric: accelerating analytics with a modern data architecture

The challenges

Our customer, a leading company in the biotech sector, faced significant hurdles in their data management and analytics journey. Their reliance on manual processes created inefficiencies and limited their ability to derive actionable insights in a timely manner.

1. Manual Data Extraction: Extracting data from SAP systems was time-consuming and repetitive, requiring manual efforts every month.

2. Fragmented Analytics: Analysis was mostly done in Excel, which could only handle a limited amount of data (about two months) due to file size limitations.

3. Siloed Operations: Teams worked in isolation, creating their own data mappings and analytics, resulting in redundancy and inconsistency.

4. Repetitive Workflows: Many tasks, such as monthly mapping corrections for SAP data, were repetitive and lacked automation.

5. AI Ambitions, but No Readiness: While the company was interested in exploring AI, they lacked the foundational architecture to integrate it effectively.

These challenges resulted in wasted time, with teams spending significant effort updating analyses manually in Excel after extracting data from SAP. Additionally, having to manually refresh dashboards with over 30 million of rows of data limited the timeliness of insights.

Our approach

To address these challenges, we initiated an 8-day pilot project focused on SAP invoicing data, leveraging Microsoft Fabric’s trial environment to eliminate upfront licensing costs. The pilot marked the beginning of a transformative journey.

How we approached it:

• Building a centralized workflow:
  We started with core data from sales and finance—SAP invoices, orders, shipments, budgets, opportunities, and the general ledger. By centralizing and harmonizing this information, we created a robust workflow that streamlined data access and reduced the need for manual intervention.

• Expanding the data landscape:
  With the foundation of sales and finance data in place, we expanded the system to include marketing and HR data. This included website orders, statistics, and leads/prospects from platforms like GA4 and HubSpot. This broader data set laid the groundwork for more comprehensive analysis and decision-making.

• Scaling adoption across teams:
  Initially, the system was used by just five users. However, as the solution proved its value, it quickly scaled to 90 users across the organization. This shift fostered collaboration and broke down data silos, enabling teams to work with consistent, accurate information.

By integrating diverse datasets and scaling user adoption, we demonstrated the power of a structured, phased approach. The transformation from manual processes to data-driven decision-making was a game-changer for the organization. Now, teams can act on insights daily rather than weeks later, drastically improving operational efficiency.

Unlocking new growth opportunities

With a strong, unified data architecture in place, the next logical step is to harness the potential of AI. Our roadmap includes exploring how AI can be layered on top of the current data infrastructure to deliver predictive insights, automate routine tasks, and ultimately unlock new growth opportunities for the business.