“Everything technically works. But nothing actually works.”
That is what an IT Director told us last year about his Power BI estate. Reports ran. Dashboards loaded; but four-hour refresh times, numbers that never quite matched across systems, and six months of ‘we are looking into AI’ told a different story.
He was not describing a Power BI failure, he was describing an organisation that had outgrown the platform it built its data practice on.
This guide is for IT Directors, Heads of Data, and technology leaders who are asking the same question he was: is it time to migrate from Power BI to Microsoft Fabric; and if so, how do we do it without disruption?
The honest answer, which we will give you upfront; not every organisation should migrate. Power BI is an exceptional tool that is right for a large number of organisations today. Microsoft Fabric is built for a different set of problems. Understanding which category, you are in is the most important decision you will make in this process.
Power BI vs Microsoft Fabric: Understanding the Difference
Before any migration conversation is useful, this distinction needs to be clear; Microsoft Fabric is not an upgraded version of Power BI. It is a fundamentally different kind of platform.
Power BI is a business intelligence and reporting tool. It connects to data sources, transforms data through Power Query, models it, and presents it through interactive reports and dashboards. For organisations that need reliable, self-service analytics, it does this as well as any product on the market. Power BI remains inside Microsoft Fabric as the reporting layer, it does not disappear.
Microsoft Fabric is a unified data platform. Launched in 2023 and now the centrepiece of Microsoft’s data strategy, Fabric handles the entire data lifecycle in a single, integrated environment: data ingestion, transformation, storage, real-time analytics, data science, and business intelligence. It is built on a Lakehouse architecture with OneLake as its unified storage layer, meaning all data in a Fabric environment lives in one place, governed centrally, accessible to every workload.
The practical implication of this difference is significant. Power BI was designed for organisations that have data and need to report on it. Fabric was designed for organisations that need to build, govern, and operationalise a data platform, one that can support not just reporting but AI, real-time analytics, and data products across the enterprise.
| Power BI — Built For | Microsoft Fabric — Built For |
| Self-service BI and interactive reporting | Full data lifecycle: ingestion to AI |
| Connecting to existing data sources | Centralised data storage via OneLake |
| Business users consuming dashboards | Data engineers, analysts, and data scientists |
| Departmental or team-level analytics | Enterprise-wide data platform |
| Organisations with moderate data complexity | Organisations with high data volume and complexity |
| Reporting on data that exists elsewhere | Building the data foundation itself |
Neither is better than the other. They are designed for different organisations at different stages of data maturity. The migration question is not ‘which is the superior product?’ It is ‘which one is right for where we are now and where we are going?’
The Four Signals That Tell You It Is Time to Migrate
In our work running data assessments and Microsoft Fabric migrations across financial services, construction, energy, and professional services organisations, the decision to move from Power BI to Fabric is almost always driven by one or more of four consistent signals. The more of these that apply, the stronger the case for migration.
Signal 1: Data Reconciliation Has Become Someone’s Job
If anyone in your organisation spends significant time each week ensuring that the numbers in one system match the numbers in another, if there is a standing Friday afternoon task called ‘reconciling the board pack’, you have an architecture problem presenting itself as a process problem.
This happens when Power BI is doing transformation work that should be happening upstream. Data lives in multiple disconnected source systems. Each system tells a slightly different version of the same story. Power BI connects to each one and tries to make them agree, but the connections are fragile, the logic is buried in Power Query that only one person fully understands, and the result is reports that analysts do not trust enough to present without checking first.
Fabric’s Lakehouse architecture resolves this at the source. A Medallion architecture, bronze layer for raw ingestion, silver for cleansed and conformed data, gold for business-ready datasets, means that by the time data reaches a report, it has been through a governed transformation process that produces a single, trusted version of every key metric. Reconciliation becomes unnecessary because the foundation does not allow contradictions to exist.
The Question to Ask Your Team
How many hours per week does your team spend reconciling data between systems or validating numbers before they are presented? If the answer is more than two hours, you have a Fabric-shaped problem.
Signal 2: Your Microsoft Licensing Costs Are Climbing Without a Clear Return
Power BI Premium Capacity was designed for a specific scale and a specific set of use cases. As organisations grow, more users, more workspaces, more data volumes, more complex requirements, the licensing model can start to work against them rather than for them.
The pattern we see regularly: organisations that started with Power BI Pro, graduated to Premium Per User as their needs grew, and are now looking at a Premium Capacity SKU to handle their reporting volumes, all while also paying separately for Azure data services, potentially Azure Synapse or Azure Data Factory, and various other components of a data stack that was assembled incrementally rather than designed holistically.
Microsoft Fabric’s capacity-based licensing model consolidates this. A single Fabric capacity SKU covers the full range of Fabric workloads, data engineering, data warehouse, real-time intelligence, data science, and Power BI. For organisations with a mature data estate spanning multiple Azure services and Power BI licensing tiers, the consolidation opportunity is material.
| Feature | The Old Way (Fragmented) | The Fabric Way (Consolidated) |
| Licensing | Separate bills for Power BI, Synapse, and ADF. | One Capacity (F-SKU) covers everything. |
| Data Storage | Multiple copies of data (Data Lake, SQL DB, Power BI Cache). | OneLake: A single copy of data used by all services. |
| Compute | You pay for idle time on multiple different services. | Pooled Compute: Your F-SKU “credits” shift between ETL, SQL, and BI as needed. |
Comparison: Traditional Azure/Power BI vs. Microsoft Fabric (2026)
| Feature | The “Incremental” Model (Old) | The Microsoft Fabric Model (New) |
| Licensing Logic | Seat-Based + Service-Based: You pay per user (Pro/PPU) and per Azure service (ADF/Synapse). | Capacity-Based: One monthly fee (F-SKU) covers all tools and unlimited “viewers” (at F64+). |
| Power BI Costs | £10.80 per user/month. Costs scale linearly as your company grows. | Fixed Monthly Cost. Scale your user base without increasing your license bill. |
| Data Integration | Azure Data Factory: Charged per pipeline run and activity hour. | Fabric Pipelines: Included in your capacity; no “per-run” fees. |
| Warehousing | Azure Synapse: Separate compute pools; often pay for idle time. | Fabric Warehouse: Uses pooled compute; shifts power to BI during the day and ETL at night. |
| Data Storage | Triple-Pay: Separate costs for Data Lake, SQL Warehouse, and Power BI Cache. | OneLake: “Single Copy” architecture. Store it once, use it everywhere via Delta Parquet. |
| Complexity Tax | High: Multiple portals, security models, and “glue” code to connect services. | Low: A single unified SaaS experience with one security layer (OneSecurity). |
The Financial “Switch” Point
If your organisation matches any of the criteria below, the “Return” on switching to Fabric is usually immediate:
- User Count: You have more than 350 Power BI users.
- Azure Spend: You spend more than £1,200/month combined on Synapse and Data Factory.
- Data Duplication: You are struggling with “Import” size limits in Power BI and need Direct Lake performance.
The Question to Ask Your Finance Director
Can you clearly explain what your organisation pays for its Microsoft data and analytics platform today, and what each component delivers? If the answer is ‘not really’, a licensing audit should happen before your next renewal.
Signal 3: The AI Question Has Arrived and the Honest Answer Is Not Yet
Every leadership team in 2026 is asking some version of the same question: what is our AI roadmap? For IT Directors and Heads of Data, the honest answer to that question depends almost entirely on the state of the underlying data estate.
AI does not fix poor data quality. It amplifies it. A Copilot Studio agent or an AI model that draws on inconsistent, ungoverned, poorly structured data produces outputs that are inconsistent, ungoverned, and poorly structured, faster and at greater scale than any human process could manage. The organisations getting genuine value from AI are not the ones who moved fastest to deploy it. They are the ones who built a clean, governed, centralised data foundation first and then built AI on top of it.
Microsoft Fabric is not an AI product. But it is the platform on which AI on the Microsoft stack functions properly. OneLake provides the centralised, governed data store that Copilot and AI models require. The Fabric Lakehouse provides the data quality layer that makes AI outputs trustworthy. Without it, AI projects in Microsoft environments either underperform or fail entirely.
If your organisation’s current answer to the AI question is ‘we are looking into it’ or ‘we are not ready yet’, the most valuable thing you can do is audit your data foundation. In most cases, the gaps that prevent AI readiness are exactly the gaps that a Fabric migration addresses.
The Question to Ask Your Leadership Team
If we deployed Copilot Studio or an AI model on top of our current data estate today, would we trust the outputs? If the answer is no, or ‘it depends’, the data foundation needs work before the AI investment does.
Signal 4: Your Data Team Is Maintaining the Estate Rather Than Using It
This is perhaps the most telling signal, and the one most commonly accepted as normal when it should not be.
In a mature Power BI estate that has grown organically over several years, a significant proportion of the data team’s time is often absorbed by operational overhead: managing gateway refreshes, resolving capacity alerts, fixing broken pipelines, investigating failed report refreshes, and handling the accumulating technical debt of a data architecture that was built incrementally rather than designed intentionally.
When data analysts are spending two or three days per week on infrastructure maintenance rather than analysis, the platform is working against the organisation rather than for it. The insight that data teams were hired to generate, the competitive intelligence, the operational efficiency improvements, the financial modelling, is being delayed or foregone because the plumbing demands constant attention.
A properly implemented Fabric environment, Lakehouse at its core, automated data pipelines, clear governance, monitored capacity, significantly reduces this operational overhead. Data teams working on a well-architected Fabric estate typically report spending 60 to 70 percent more time on analysis and value-generating work within the first six months of go-live.
The Question to Ask Your Data Team
What percentage of your week is spent keeping the existing estate running versus generating insight from it? If maintenance is consuming more than 30 percent of team capacity, the architecture is costing you more than the infrastructure.
When to Stay on Power BI
This section matters as much as the one above, and it is worth reading even if you recognise your organisation in several of the signals described.
Power BI is the right platform for organisations whose data needs are met by what a reporting tool can deliver, organisations with a manageable number of data sources, a team that trusts the numbers it produces, and data ambitions that are well served by self-service analytics and interactive dashboards. If that describes your organisation today and for the foreseeable future, there is no compelling case for migration.
There is also a category of organisation that should not migrate yet even if they plan to eventually: those whose data quality and governance problems are significant. No platform migration fixes a data quality problem. Organisations that move to Fabric with an ungoverned, poorly structured data estate do not get a clean Lakehouse, they get an ungoverned, poorly structured Lakehouse. The discipline of data governance must precede the architecture change, not follow it.
The question we ask every organisation before recommending a migration is this: if you fixed your data quality, your governance, and your team structure tomorrow — would Power BI still be a constraint? If the honest answer is no, fix those things first. If the honest answer is yes, the migration conversation is worth having.
Stay on Power BI If:
- Your reports are trusted and your data is reasonably centralised
- Your team has capacity to work on insight rather than infrastructure
- Your organisation’s data ambitions are met by reporting and self-service analytics
- You have significant data quality or governance issues that are unresolved — fix those first
- You are within 12 months of a major organisational change that might alter your requirements
Consider Migrating to Fabric If:
- Data reconciliation is consuming team time every week
- Your licensing costs are growing without a corresponding growth in capability
- AI is on the leadership agenda and your current estate cannot support it
- Your data team is spending more time on maintenance than on analysis
- You need a single platform for data engineering, analytics, and AI — not separate tools
What a Power BI to Microsoft Fabric Migration Actually Involves
The anxiety most IT leaders feel about migration is understandable. Moving a mature data estate is not a minor undertaking, and the consequences of doing it poorly are real. Reports break. Analysts lose confidence. Projects overrun. The business disruption of a failed migration is considerable.
What separates successful Fabric migrations from unsuccessful ones is rarely the technology. It is the approach.
Phase 1: Assessment and Architecture Design (Weeks 1–3)
The first phase of any Fabric migration we run is a comprehensive assessment of the current estate: the number and nature of Power BI workspaces, the data sources and transformation logic, the governance model (or absence of one), the licensing structure, and the team’s current capabilities.
From this assessment, we design the target Fabric architecture before a single line of code is written or a single report is moved. The Medallion architecture — bronze, silver, and gold layers — is the standard approach for most mid-market organisations, but the specific design depends on data volumes, source system complexity, and the organisation’s intended use of Fabric workloads beyond Power BI.
Phase 2: Foundation Build (Weeks 4–10)
The Fabric Lakehouse is built and the data pipelines are established before any Power BI migration begins. This is the stage most organisations want to skip, it feels like nothing visible is happening — but it is the stage that determines whether the migration delivers lasting value or simply recreates the same problems in a new environment.
Bronze layer pipelines ingest raw data from source systems into OneLake. Silver layer transformations cleanse, conform, and enrich the data according to documented business rules. Gold layer datasets are built for specific business use cases, with clear ownership and governance. This process takes longer than most organisations expect, and it should, the quality of the foundation determines the quality of everything built on top of it.
Phase 3: Power BI Migration (Weeks 8–14)
Once the Fabric foundation is stable and tested, Power BI reports and semantic models are migrated to connect to the new data layers rather than the original source systems. This phase is typically the least technically complex, Power BI in Fabric is still Power BI, but it requires careful management of analyst expectations and a clear communications plan for business users who will notice changes in report performance and behaviour.
Reports are migrated in priority order, validated against agreed KPIs, and signed off by business stakeholders before the legacy connections are deprecated. A parallel-running period, where both old and new reports are accessible for validation, is standard practice and significantly reduces migration risk.
Phase 4: Optimisation and Handover (Weeks 12–16)
The final phase focuses on performance optimisation, governance configuration, capacity monitoring setup, and knowledge transfer to the internal team. A well-executed Fabric migration should leave the organisation fully capable of operating and evolving the platform independently, not dependent on the implementation partner for day-to-day operations.
| Migration Phase | Typical Duration |
| Assessment and architecture design | 2–3 weeks |
| Foundation build (Lakehouse + pipelines) | 4–8 weeks depending on source complexity |
| Power BI migration and validation | 3–6 weeks depending on report estate size |
| Optimisation and handover | 2–3 weeks |
| Total (mid-market organisation) | 10–18 weeks end to end |
The Migration Mistakes That Slow Organisations Down
Having run a significant number of these migrations, the same mistakes appear consistently. They are worth naming directly.
Starting with the reports rather than the foundation. The instinct is to migrate the most visible outputs first, the board pack, the CEO dashboard, because that is what the business notices. Organisations that do this spend months migrating reports onto a foundation that is not yet ready, and then spend further months fixing the foundation while trying not to break the reports they have already moved. Foundation first, always.
Underestimating data quality debt. Every data estate contains inconsistencies, undocumented transformation logic, and fields that mean different things in different reports. A migration surfaces all of it. Organisations that do not allow time for data quality remediation in the project plan consistently overrun on the foundation phase.
Treating it as an IT project rather than a business programme. Fabric migrations that are run as purely technical exercises, without business stakeholder involvement in KPI validation, without clear ownership of the gold layer datasets, without a communications plan for analysts, produce technically correct Lakehouses that nobody trusts. Business involvement from day one is not optional.
Ignoring licensing until after go-live. The Fabric capacity model is different from Power BI Premium. Organisations that do not model their Fabric capacity requirements before go-live regularly find themselves either over-provisioned (paying for capacity they are not using) or under-provisioned (experiencing throttling at peak usage). Capacity planning belongs in the assessment phase, not the optimisation phase.
The Next Step: An Honest Assessment of Where You Stand
The IT Director who called us last year, the one whose estate technically worked but practically did not, had one thing in common with most of the organisations we see at this stage: he knew something needed to change, but he did not have a clear enough picture of his own estate to know what the right change was.
The most valuable thing we can offer any organisation asking the Power BI to Fabric question is exactly that: a clear-eyed, honest assessment of where you are, what your current estate can support, and what a migration would realistically involve for your specific situation.
We run a complimentary 30 minute Fabric Discovery Workshop with a Synapx Microsoft MVP, with no obligation and no pitch. You walk away with a one-page Fabric Opportunity Summary that maps your current state against Fabric readiness indicators, identifies the gaps that matter, and gives you an honest view of timeline, complexity, and indicative cost.
It is the conversation worth having before any other conversation about your data platform.
If you would like to book a 30-minute Fabric Discovery Workshop with one of our Microsoft Fabric MVPs, contact us.
