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 do not have yet. Migrating dirty data into Fabric does not fix the problem, it just moves it, at greater 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
- During a data lake transition there is a natural inflection point to clean the data before it gets more expensive
Fabric readiness in three stages
Data Readiness Assessment
We audit your existing operational data against Fabric's structural requirements, identifying gaps, inconsistencies, and priorities before migration begins.
Automated Cleaning and Structuring
We clean, standardise, and structure your data to meet Fabric's requirements automatically, at scale, with 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.
“If we want a bot that knows everything about our contract, we need to make sure the governance data in the contract is properly agreed first.”
What does Microsoft Fabric data readiness mean?
Microsoft Fabric data readiness is the process of cleaning, structuring, and governing operational data before migrating to Microsoft Fabric. For asset-intensive industries, construction, facilities management, manufacturing, procurement data, asset registers, and operational records are often too inconsistent for Fabric to process reliably. Pearstop automates the data preparation work that makes Fabric migrations succeed, so organisations can use Copilot, AI agents, and real-time analytics from day one rather than spending 12-18 months fixing data quality after migration.
Frequently asked
Should we fix our data before Fabric, or can Pearstop run alongside the migration?
Either works, but earlier is cheaper. Migrating inconsistent data into Fabric does not fix it, it moves it, and cleaning it up after migration typically costs more than cleaning it before.
Does this replace our Fabric implementation partner?
No. Pearstop prepares the data layer underneath the migration. Your implementation partner still builds the platform, they are just building it on data that is already structured.
Planning a Fabric migration?
Talk to us before the migration starts. A 30-minute call now can save months of rework later.
