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Why Platforms Like Microsoft Fabric Don’t Fix Broken Data CultureAfter dashboards fail, organisations often reach for a bigger answer.

• A new platform.
• A more integrated stack.
• A promise that this time things will be different.

That’s where platforms like Microsoft Fabric come into play.

• Fabric is powerful.
• Modern.
• Well-architected.

But it’s also one of the most misunderstood investments organisations make. Because platforms don’t fail organisations. Organisations fail to change how they work around them.

The platform myth

There’s a comforting belief that goes something like this:

“Once we’re on the right platform, everything will fall into place.”

• Data will be trusted
• Reporting will be faster
• Teams will align
• Decisions will improve

The platform becomes a proxy for leadership, strategy, and culture. That belief is understandable, but it’s wrong.

What platforms are actually good at

Let’s be clear: platforms like Microsoft Fabric are not the problem.

They are exceptionally good at:

• Centralising data
• Standardising tooling
• Reducing architectural sprawl
• Enabling scale and performance
• Supporting modern analytics patterns

Fabric can remove technical friction. What it cannot remove is organisational friction.

Broken data culture looks like this.

Before blaming tools, it’s worth recognising the symptoms of a broken data culture:

• Metrics are debated more than decisions
• Reports exist, but trust is low
• Teams optimise locally, not collectively
• Data ownership is unclear or political
• Leadership asks for insight, but rewards speed over rigour

In these environments, a new platform doesn’t create clarity; it amplifies confusion.

Why platforms don’t fix culture

Here are a few reasons explaining why data platforms don’t fix and organisations’ data culture

1. Platforms don’t define purpose

A  data platform can answer:

“Where does the data live?”

It cannot answer:

“Why does this data matter?”

Without a shared understanding of:

• Business priorities
• Critical decisions
• Success measures

Even the best platform becomes an expensive filing cabinet.

2. Platforms don’t align with leadership

Data culture is set at the top of a business or organisation.

If leaders:

• Ask for different numbers in different meetings
• Override data with instinct when it’s inconvenient
• Reward delivery over quality

Then no platform will create trust. Culture is reinforced by behaviour, not architecture.

3. Platforms don’t resolve ownership

Modern platforms centralise data, but they don’t magically assign accountability.

Without clear ownership:

• Data quality issues persist
• Definitions drift
• “Someone else owns that” becomes the default

Fabric can host your data estate. It cannot tell you who is responsible for it.

4. Platforms don’t simplify decision-making

A common failure mode is more capability, less clarity.

With powerful platforms:

• More data becomes accessible
• More metrics get surfaced
• More dashboards get built

But without decision discipline, this leads to:

• Cognitive overload
• Slower meetings
• Analysis paralysis

Better tools don’t automatically mean better decisions.

5. Platforms don’t change incentives

People respond to what they are measured on. If teams are incentivised to:

• Deliver quickly rather than accurately
• Protect their numbers rather than challenge them
• Avoid uncomfortable insights

Then culture won’t shift, regardless of platform.

Technology follows incentives, not the other way around.

When platforms do work

Organisations that succeed with platforms like Microsoft Fabric tend to do a few things differently:

• They establish clarity before migration
• They define decision ownership early
• They align leaders on what “good” looks like
• They treat the platform as an enabler, not a saviour

In these environments, Fabric accelerates progress rather than exposing cracks.

The uncomfortable truth

If dashboards are already struggling…
If trust in data is fragile…
If reporting feels slower every year…

A new platform will not fix those problems. It will surface them faster.

Why this matters

Many organisations invest heavily in platforms expecting transformation. What actually  they get instead is:

• Better plumbing
• The same arguments
• New tooling layered on old habits

The gap between capability and impact grows wider. That’s not a platform failure. It’s a leadership and culture challenge.

Where this fits in the bigger picture

This article builds on Why Dashboards Fail and leads into the next questions many leaders face:

• If platforms don’t fix culture, what does?
• How do we know whether we’re observing the right things?
• Why does reporting slow down as complexity grows?

Those are the questions explored in the next parts of this series:

They’re also the questions organisations bring into our Data & Analytics Accelerator often after investing in the platform first.

A better starting question

Instead of asking:

“Is Fabric the right platform for us?”

A more useful question is:

“Are we ready to get value from it?”

That answer has very little to do with technology, and everything to do with clarity, ownership, and culture.

Useful Links

Building a Data-Driven Story: From Reports to Impact

Introduction to the Microsoft Data Platform – Data Platform Roles

What is Microsoft Fabric and How Does It Relate to Power BI?

Dataflows Gen2 vs Data Factory in Microsoft Fabric: What’s the Difference? I have been asked this question several times in recent training sessions on Microsoft Fabric, so I jotted some notes down here.

Microsoft Fabric brings together the best of Microsoft’s data engineering, data integration, analytics, and AI capabilities into a single unified platform. For many teams adopting Fabric, one of the first questions that arises is:

“What’s the difference between Dataflows Gen2 and Data Factory Pipelines?”

Both can move, transform, and prepare data. Both live inside the Fabric experience. And both can be scheduled, monitored, and orchestrated. However, they serve different purposes, offer different strengths, and work best in different parts of the modern data lifecycle.

This post explains the key differences and provides practical examples to help you choose the right tool for your scenario.

What Are Dataflows Gen2?

Dataflows Gen2 are Fabric’s low-code data preparation and transformation solution. They are built on Power Query, the same engine used in Power BI and Excel, giving analysts and citizen developers a familiar, friendly interface.

Key Characteristics

• Low-code / no-code: Drag-and-drop transformation steps rather than writing SQL or Python.
• Power Query based: Ideal for data wrangling, cleansing, merging, shaping, and enrichment.
• Works well for mid-volume data: Excellent for business data preparation and M-code transformations.
• Outputs straight into Fabric: Can load data into Lakehouses, Warehouses, and KQL databases.
• Accessible to analysts: You don’t need a data engineering background to use it effectively.

When to Use Dataflows Gen2

Dataflows Gen2 shine in scenarios such as:

• Self-service data preparation for analysts building semantic models.
• Ingesting business application data (Excel files, SharePoint lists, Dataverse, SQL).
• Quick transformations such as splitting columns, merging tables, cleaning text, or deduplication.
• Prototyping datasets before handing them over to engineering teams.

If you know Power Query, you’ll feel at home immediately.

What Is Data Factory (in Fabric)?

Fabric’s version of Data Factory combines two things:

1. Pipelines – orchestration and data movement.
2. Dataflows (Power Query) and Notebooks (Spark) – heavy-duty transformation for engineers.

It is Microsoft’s full data integration and ETL/ELT platform, now tightly integrated into Fabric.

Key Characteristics

• Enterprise-grade orchestration with pipelines, triggers, and dependency management.
• Powerful connectors for large-scale ingestion, especially from cloud and on-premises systems.
• Supports Spark notebooks and data engineering workloads.
• Handles high-volume, complex pipelines.
• CI/CD friendly and suited for production data engineering.

When to Use Data Factory

Data Factory is designed for more complex engineering tasks, such as:

• High-volume ingestion from operational systems, APIs, or files landing in cloud storage.
• ETL/ELT using Spark notebooks, SQL scripts, and pipeline activities.
• Orchestrating multi-step workflows, including branching, loops, and conditional logic.
• Copying terabyte-scale datasets from Azure SQL Database, Synapse, ADLS, AWS S3, Oracle, and more.
• Building production-ready pipelines with monitoring, retries, and error handling.

If you are familiar with Azure Data Factory, this will feel like its next evolution within Fabric.

Dataflows Gen2 vs Data Factory: How to Choose?

Here is a simple way to think about it:

Choose Dataflows Gen2 when:

• You want low-code data shaping.
• Business analysts are preparing their own datasets.
• You need simple ingestion or transformation.
• The data volumes are small to medium.
• The source systems are Excel, SharePoint, Dataverse, or SQL.

Choose Data Factory when:

• You are building enterprise pipelines.
• You need orchestration, scheduling, and dependencies.
• You are working with large or complex datasets.
• You require Spark, notebooks, Data Engineering, or SQL pipeline logic.
• Data movement needs to be integrated into CI/CD or operated at production scale.

A Combined Approach

In many organisations the best approach is both, working together:

• Data Factory pipelines handle ingestion from source systems into the Bronze layer.
• Dataflows Gen2 then apply transformations to shape and enrich the data for the Silver layer or the semantic model.

This layered approach provides scalability, governance, and flexibility while still enabling self-service analytics.

Need help applying this in practice?

If your organisation is using Power BI or Microsoft Fabric and needs clarity around architecture, governance, or next steps,
The Data Platform Accelerator is designed to help.

It’s a focused engagement that assesses your current setup and delivers a practical roadmap you can execute.

👉
Learn more about The Data Platform Accelerator

Summary

While Dataflows Gen2 and Data Factory sit side-by-side in Microsoft Fabric, they target very different users and workloads:

• Dataflows Gen2 → Best for analysts, low-code transformations, quick data preparation.
• Data Factory → Best for engineers, enterprise data pipelines, complex ingestion, and orchestration.

Understanding these differences ensures your team uses the right tool for the right job, helping you build efficient, scalable, and well-governed data solutions in Microsoft Fabric.

If you’re teaching or adopting Fabric, this distinction is one of the most important concepts to get right early.

Power BI & Microsoft Fabric Consulting and Training | 2025 Review

As the final working day of 2025 draws to a close here at GRE Towers, it feels like the right time to reflect on what has been another highly successful year for data consulting, Power BI delivery, Microsoft Fabric projects, and technical training.

Throughout 2025, we’ve worked with organisations across the public sector, private sector, fintech, insurance, retail, and not-for-profit space, helping them make better decisions using data.

Data consulting highlights from 2025

Over the past year, our data consulting and advisory services have included:

• Embedding Power BI dashboards directly into a fuel delivery company’s application
• Providing a Virtual DBA service to a large software licensing company, managing and optimising their SQL Server estate
• Delivering ongoing Virtual DBA services for a fast-moving retail organisation
• Designing and architecting a Microsoft Fabric deployment for a local authority
• Providing fractional CEO and CTO services to a healthcare startup (more on this in 2026)
• Delivering fractional CIO / CTO support to NICS Wellbeing
• Supporting a large insurer with data strategy, business alignment, and BI modernisation, including a roadmap to migrate legacy BI infrastructure to Microsoft Fabric
• Optimising Power BI and Microsoft Fabric capacity for an insurer, delivering significant cost savings on their F512 capacity
• Implementing a data engineering solution for risk reporting with a fintech operating in the insurance broker space

These projects reflect a growing demand for modern analytics platforms, cost-effective Fabric capacity planning, and strategic data leadership.

Power BI, Microsoft Fabric, and SQL training delivered in 2025

Training remains a core part of what we do, and in 2025 we delivered a wide range of bespoke and commercial training programmes, including:

• Bespoke Power BI training for a leading food manufacturer
• Bespoke Power BI and Paginated Reports training for two leading UK police forces
• Bespoke Microsoft Fabric training for a large local authority
• SQL training for a large local authority
• Authoring a commercial PostgreSQL DBA training course for a major training provider
• Delivering multiple Microsoft-certified courses for large Microsoft training partners

Our training focuses on real-world use cases, ensuring teams can apply what they learn immediately.

Data consulting and training plans for 2026

Looking ahead, 2026 is already shaping up to be another strong year.

Consulting in 2026

• Continuing data engineering and risk reporting work with a fintech in the insurance broker space
• Ongoing Virtual DBA services for a large software licensing company
• Continued SQL Server and platform support for fast-moving retail
• Supporting a not-for-profit organisation with Power BI development, focusing on KPI reporting and insight delivery

Training in 2026

You can view our public training schedule on the website, with new dates added regularly.

Looking for Power BI, Microsoft Fabric, or data consulting support in 2026?

If you’re planning to improve your data analytics, reporting, or platform strategy in 2026, now is the ideal time to start the conversation.

Whether you need:

• Power BI development or optimisation
• Microsoft Fabric architecture and cost optimisation
• SQL Server or PostgreSQL DBA support
• Fractional CIO / CTO or data leadership
• Tailored training for your team

We can help.