This blog is a preview of our session at Sirus Connects on October 9, 2026, where we share how to grow towards FAIR data step by step – with transparency as the starting point, powered by Microsoft Fabric, dbt and a data catalog.
Speaker:
FAIR data isn't a destination, but a growth path that starts with transparency.
Self-service analytics is high on the agenda in virtually every organisation. Everyone wants faster insights, less dependence on IT and more data-driven decisions. But in practice, I often see the same problem come up: expectations are high, while the foundations aren’t always in place yet.
What happens then? Teams build dashboards on top of data that isn’t fully understood, definitions differ from department to department, and trust in data goes down instead of up. Adding more tooling rarely solves that problem.
FAIR as a compass, not a checklist
What does help is looking at the journey differently. Instead of aiming straight for a perfect FAIR data platform, it pays off to think in terms of maturity. The FAIR principles – findable, accessible, interoperable and reusable – aren’t a checklist, but a direction. A compass.
In a recent project, we deliberately applied that mindset. Not everything had to be perfect right away. The initial focus was on transparency: making visible which data exists, where it comes from and how it’s used.
Building transparency with Fabric, dbt and a data catalog
We started by centralising data from various sources in Microsoft Fabric. This gave us one place where data comes together, but the real work only began after that. With dbt, we built a structured transformation layer. Not just to model data, but above all to create clarity: which steps take place, what logic is involved, and how do datasets relate to each other?
That transparency was further reinforced with a data catalog. There we capture definitions, relationships and ownership. An important detail: we make sure that metadata from dbt is used directly in the catalog. That way we avoid documentation and reality drifting apart.
Understanding comes before interoperability
The result isn’t a “perfect FAIR platform”, but it is an environment in which people better understand what’s happening. Data becomes easier to find, definitions become more explicit and discussions about numbers become more concrete.
And that’s exactly where the real win lies. Because before data can become fully interoperable or reusable, it first has to be understandable. Transparency isn’t a nice-to-have, but the foundational layer of data maturity.
Small steps, real progress
What I learned most from this project is that progress often lies in small, consistent steps. Not in big transformations, but in systematically improving structure, documentation and collaboration.
During my session at Sirus Connects, I’ll walk you through that approach. No theoretical model, but a practical story about how you get started, which choices you make and how you grow towards FAIR data step by step.
Curious how to take those first steps yourself? Be sure to drop by.
Want to follow this live?
Join this session at Sirus Connects on October 9, 2026, from 13h30 to 14h15. Register and secure your spot.
Can’t make it? Reach out to us, we are happy to share our experience and discuss how your organisation can grow towards FAIR data.
