Your Microsoft Fabric Migration Will Only Deliver If Your Data Is Ready First.
Microsoft Fabric promises company-wide insight. Pearstop makes sure the data feeding it is clean, structured, and reliable, so your migration delivers what leadership is expecting.
The hidden blocker in every Fabric migration
Microsoft Fabric promises a unified data platform, one source of truth across finance, operations, procurement, and assets. But Fabric assumes structured, consistent data that most organisations simply don't have yet. Migrating dirty data into Fabric doesn't fix the problem, it just moves it, at great cost and risk.
- Inconsistent naming conventions mean cross-site and cross-system comparisons fail
- Field errors and duplicates surface immediately in reporting, damaging trust in the new platform
- AI and Copilot features built on Fabric underdeliver when the underlying data is unreliable
How It Works
Three simple steps to clean, structured, decision-ready data.
Data Readiness Assessment
We audit your existing operational data against Fabric's structural requirements, identifying gaps, inconsistencies, and priorities before migration begins.
Automated Cleaning & Structuring
We clean, standardise, and structure your data to meet Fabric's requirements automatically, at scale, no manual rework.
Migration-Ready Data
You receive verified, structured data with automated quality control built in, ready for Fabric onboarding and AI model training from day one.
Key Benefits
What you gain when your data actually works for you.
Save Time
Reduce manual data processing by up to 95% — free your team for higher-value work.
Improve Accuracy
Eliminate inconsistencies and errors that undermine reporting and decision-making.
Protect Margins
Clear cost visibility before every bid or decision means no more margin surprises.
Scale Faster
Automated pipelines mean you can handle more volume without adding headcount.
Results That Speak for Themselves
Real outcomes from real clients in technical industries.
Forrester-modelled three-year ROI for enterprises that migrate to Microsoft Fabric on a clean data foundation (source: Forrester TEI Study, commissioned by Microsoft)
Without manual rework
Per Forrester composite enterprise model
"We were planning a Fabric migration but kept hitting the wall of inconsistent, unstructured data underneath. Pearstop cleaned and structured it first — what would have been a 12-month data preparation project became manageable."
Ready to Clean Your Data?
Book a 7-minute discovery call and see exactly how Pearstop fixes your specific data challenge.
Book a 7-Minute DiscoveryFrequently Asked Questions
Straight answers about Microsoft Fabric readiness and how Pearstop helps organisations build the data foundations Fabric actually needs.
What does AI readiness mean for construction, manufacturing, and hard services companies?
AI readiness means having operational data that is clean, structured, and consistently governed, so that AI tools, Copilot, and machine learning models can produce reliable outputs. For hard services, construction, and manufacturing companies, the most common AI readiness blockers are poor procurement data quality, inconsistent asset registers, and fragmented operational records. Pearstop automates the data preparation work that makes AI initiatives succeed, from UNSPSC procurement classification to asset data structuring, giving organisations a foundation that AI tools can actually learn from.
How do we know if our data is AI-ready?
The clearest signals are: your team spending time cleaning data before every report, AI tools producing unreliable or inconsistent outputs, and digital transformation projects stalling during the data preparation phase. Pearstop runs a 7-minute discovery call to assess your current data state and identify the specific gaps that would need to be closed before AI deployment.
We're planning a Microsoft Fabric or SAP migration — where does data readiness fit in?
Migrating dirty data into a new platform doesn't fix the problem, it just moves it. Pearstop prepares the data before migration: cleaning, deduplicating, classifying, and structuring it so the new system starts with a reliable foundation. This reduces migration risk, shortens implementation timelines, and means your team gets value from the new platform immediately rather than spending months cleaning up after go-live.
Does Pearstop build or deploy AI tools?
Pearstop focuses on the data layer, not the AI tools themselves. We clean, structure, and classify your operational data so that whatever AI platform or analytics tool your organisation chooses can actually perform. We work alongside your technology partners and internal teams, not in competition with them.
How long does it take to get data AI-ready?
For most initial datasets, Pearstop returns clean, classified data within a few business days. The timeline depends on volume, complexity, and how fragmented the source data is. Ongoing data pipelines, where new data flows in regularly, are set up once and run automatically, so AI tools always have fresh, reliable input without manual effort from your team.