Where you are stuck right now
The reports go out, then the meeting starts. Sales quotes one number for the month, finance quotes another, and the gap is not fraud. Each report was built by a different person, off a different export, with a quietly different idea of what counts. One excludes refunds. One counts the order date, the other the ship date. Nobody wrote the rule down, so nobody can settle it.
If you are on Microsoft 365 and Power BI, you have probably watched this get worse. Power BI made it easy for anyone to build a dashboard, and so they did. Now there are forty dashboards, half are stale, and your team trusts the spreadsheet they keep on the side more than any of them. You are weighing Microsoft Fabric to pull it back together, and rightly wondering whether it is the fix or just a bigger, pricier mess.
Why buying the licence does not settle the argument
Fabric is a capable platform. OneLake gives you a single store, Data Factory handles the loading, and Power BI is already where your people look. Switching it on changes none of the reasons your numbers disagree. The platform does not know that active customer should exclude trials, or that revenue is net of returns. Lift the same conflicting exports and habits into Fabric and you get the same disagreements on better infrastructure.
Two things actually decide whether Fabric earns its keep, and neither ships in the box. The first is a clean, modelled data layer, so reports stop drawing from competing copies. This is what a healthy data ecosystem gives you, unified data feeding the platform rather than ten private extracts. The second is one written, version-controlled definition for every metric. When the rule for revenue lives in source control and every report reads from the same semantic model, changing it once updates everything.
How we deliver it
We build in small, provable slices rather than one switch-on, so you see a trustworthy report early.
- Pick the report that hurts. We start with the one number people fight over and agree its exact definition in writing.
- Model the data once. We build a medallion lakehouse in OneLake, bronze to silver to gold, so curated tables feed reporting instead of raw or duplicated data.
- Write the metric down. Each definition goes into a versioned semantic model in source control, so the meaning of a number is recorded, reviewable and changed in one place.
- Build the golden path. We ship the self-serve report your team reads from, so people trust the shared one instead of building their own.
- Tune capacity and prove it. We size the F-SKU to your real workload, separate heavy engineering from interactive reporting, and reconcile against your old figures before go-live.
That first slice settles the patterns and capacity sizing before we widen scope. We provision in an Australian Azure region so data stays onshore, keep deployments in source control, and never edit in production. This is also how we build a quality internal platform, one with golden-path reports the whole organisation can self-serve from safely, instead of ten versions of the truth.
When to choose Fabric, and when not to
Honesty here saves you money. Fabric is a strong choice when you are already a Microsoft 365 and Power BI organisation and want pipelines, storage and reporting consolidated without running separate services. Your Power BI workspaces, datasets and Entra identities carry across, so there is less plumbing than bolting a standalone warehouse onto Power BI, and single-capacity billing is easier to reason about than a stack of separate meters.
It is the wrong call in a few clear cases. If you are not on the Microsoft stack, Snowflake or Databricks may suit you better, and we will say so rather than push Fabric. If your real problem is a handful of messy Power BI reports, you may not need Fabric yet. Tidied-up Power BI with proper semantic models often fixes the pain at a fraction of the cost. And because Fabric bills on capacity, both undersized and oversized capacity hurt you.
The honest summary is that Fabric rewards organisations with genuine data engineering needs on a Microsoft foundation, and punishes those that adopt it to look modern. We will give you a straight read on which one you are before you commit a dollar.
What good looks like
The point of this work is a number you can see, not a feeling, so we set the baseline and target before we build. Good looks like one revenue figure that finance and operations both quote without checking. It looks like the side spreadsheets going quiet because the shared report is finally trusted. It looks like a new question answered from an existing semantic model in minutes, not a week-long build, and a capacity bill you can predict with no surprise spike.
Services and industries we deliver with Fabric
Fabric is the platform under the work, not the work itself. See how we apply it in Data & Analytics, Power BI reporting and Data Engineering. For sector context, see Insurance, FinTech & Banking and Professional Services.
