You’re not afraid of Fabric because it’s new. You’re afraid of Fabric because you’ve seen this movie before.

You inherited an environment with ADF, Synapse, Power BI, Databricks, and a graveyard of “temporary” jobs. Every shortcut taken to hit a deadline became tomorrow’s problem. Now the organisation wants to “move to Fabric” and calls it modernisation.

Your real fear isn’t the migration itself. It’s waking up 18 months from now with Technical Debt 2.0: a shiny new platform layered over the same sprawl, the same unclear ownership, the same permission chaos, just running faster.

This article is about how to avoid that outcome.

Why lift‑and‑shift into Fabric is so tempting (and so dangerous)

On paper, lift‑and‑shift looks like the pragmatic option. You can show progress to stakeholders, simplify licensing conversations, and tick the “modern platform” box.

But if your underlying estate is already a patchwork of ad‑hoc pipelines, copy‑pasted models, and workspaces no one fully owns, Fabric doesn’t clean that up for you. It just makes it easier to scale the complexity.

You get more speed, more reach, and, if you’re not careful, more ways to break things.

The business pressure is real: “Can we be on Fabric by Q3?” Vendors frame the move as unification, not redesign. It’s easy to slip into thinking that migration alone equals modernisation. In reality, all of the domain confusion, workspace sprawl, and permission chaos you already live with simply gets new clothes and a new URL.

The three flavours of Technical Debt 2.0 in Fabric

In Fabric, technical debt doesn’t just show up as bad code. It shows up as architecture decisions you never formally made, but still have to live with.

Three patterns show up again and again:

• Domain confusion. Boundaries between business domains are fuzzy, so everything ends up in a couple of “catch‑all” workspaces. Marketing tables sit next to Finance models and operational logs, not because that’s a good design, but because no one had time to decide where they truly belong.

• Workspace sprawl. New workspaces are created for every project, experiment, and request. There’s no naming convention, no clear ownership, and no lifecycle. Some were meant to be temporary, but they quietly become production because stakeholders start relying on them.

• Permission entropy. Reader, Contributor, and Owner roles are granted in a hurry to “unblock” someone. Those permissions are rarely reviewed. Over time, you end up with a tangle of access paths that are hard to explain and even harder to audit.

Individually, none of these feels existential. Together, they make every change harder, every audit more painful, and every new initiative riskier than it needs to be.

Treat Fabric like a new city, not a new tool

The cleanest mental model for Fabric isn’t “new data tool.” It’s “new city.”

If you built a new business district, you wouldn’t start by dropping buildings at random. You’d define zones, plan main roads, and agree on basic building codes. Only then would you let people build.

Fabric is the same. Domains are your zones. Workspaces are your plots of land. Governance is your building code. If you don’t set those up intentionally, the city fills itself, and you inherit the fire hazards.

Thinking this way changes the questions you ask:

• Instead of “Where should we create a workspace?” you ask “Which domain does this really belong to?”

• Instead of “Who needs access?” you ask “What level of access is appropriate for this kind of street, in this part of town?”

• Instead of “Can we spin this up quickly?” you ask “Are we okay living with this pattern for the next three years?”

You’re not just deploying tech; you’re defining how people are allowed to build on it.

The first 90 days: what good looks like

The first 90 days in Fabric are where you either pay down old debt or lock in new debt.

A practical playbook looks something like this:

Weeks 0–2: Inventory your reality.
Map your business domains and the key analytical products you already have. Don’t start with tools; start with how the business actually thinks about itself: by product, region, segment, or function. Capture where things are already breaking: duplicate data, ambiguous ownership, or recurring complaints from stakeholders.

Weeks 2–4: Design your domain map and workspace strategy.
For each domain, define the data products and key use cases that matter most. Decide what belongs where. Then design a workspace model that matches those domains, with a clear distinction between experimental and production workloads. This is where you get explicit about which teams are allowed to build where.

Weeks 4–8: Create opinionated standards.
This is where you write the “building code”:

• Naming conventions that tell you at a glance what a workspace or object is for.

• Workspace types: for example, “lab” for experiments, “staging” for integration, “core” for shared, production‑grade assets.

• Baseline permission patterns that make the secure thing the easy thing and the risky thing obviously out of bounds.

Weeks 8–12: Prove it with one high‑value use case.
Migrate or build one end‑to‑end use case on the new structure. Not a vanity dashboard, but something the business actually feels in its day‑to‑day decisions. This becomes your reference implementation, your internal case study, and your political capital when you say “This is why we’re doing it this way.”

The goal in those 90 days is not perfection. It’s to lock in patterns you won’t hate in 18 months.

Governance as a service, not a gate

Data leaders know governance is non‑negotiable. What teams push back on is governance that feels arbitrary, slow, and disconnected from delivery.

The alternative is to treat governance as a service.

That looks like:

• Pre‑built workspace templates for common scenarios so teams don’t start from a blank page.

• Default permission sets that are safe by design, so people get the access they need without over‑exposure.

• Clear guidance on when to involve Governance or Risk, and what “good” looks like for different types of projects.

If someone has to book a meeting every time they want to do the right thing, they won’t. If the right thing is one click away, they will.

When governance is framed as “we’re here to make sure you don’t get burned six months from now,” not “we’re here to slow you down,” it becomes much easier to get buy‑in from engineering and analytics teams.

Where an Architecture Audit actually helps (and where it doesn’t)

An Architecture Audit is not about handing you a pretty diagram and walking away. Done properly, it gives you three things:

• A clear domain and workspace model you can explain to both engineers and executives. Everyone should be able to answer “Where does this live, and why?” without guessing.

• A permission and governance pattern that reduces your attack surface instead of expanding it. That includes a strategy for handling sensitive data, sandbox environments, and cross‑domain access.

• A realistic migration sequencing plan from where you are to where you want to be, without trying to “big bang” the entire estate and blowing up trust along the way.

What it shouldn’t be is:

• A 200‑page PowerPoint full of generic best practices that ignore your constraints.

• A theoretical reference architecture that looks beautiful but would take five years and ten extra headcount to implement.

It won’t write every line of code for you. But it will stop you from spending the next three years refactoring decisions you were forced to make in three weeks.

A closing note for Data Leaders

You already know Fabric can be powerful. The question keeping you up at night isn’t “Can we get it running?” It’s “Will we still be proud of what we built two years from now?”

That answer has less to do with the features you turn on and more to do with the decisions you make in the first 90 days—about domains, workspaces, and governance. If you treat Fabric like just another tool, it will quietly multiply your existing problems. If you treat it like a new city, you get the chance to design something you can actually live in.

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