An ERP migration, a Fabric rollout, or an AI project inherits every mistake already in your data.
None of these projects fix bad data on their own. They move it, or build on top of it. Pearstop cleans and structures the reference data first, whether the project is an ERP migration, a Microsoft Fabric rollout, an AI initiative, or unifying every service line on one multi-year FM contract.
The reference data arrived wrong. Nobody caught it for years.
We are basically happy with what we have seen, but maybe we expected a little bit more from the beginning of what was possible with the data.
Reference data is not neutral. It carries the mistakes of whoever entered it, multiplied across every site and year since. A wrong manufacturer part number, a name spelled two different ways depending on which system someone used that day. None of it shows up until you try to build on top of it: migrate it into Microsoft Fabric, connect it to an AI model, or compare cleaning costs against hard services costs on the same integrated FM contract. All three fail for the same reason. The data underneath was never actually the same data twice.
- Reference data is wrong before it reaches any new system, compounding across sites and years before anyone catches it
- Each service line, or each source system, reports in its own format, so no single structured dataset exists to migrate, model, or analyse
- An ERP migration, a Fabric rollout, or an AI pilot delivers less than expected, because the underlying reference data was flawed before the project started
- Reconciling it by hand happens every reporting cycle or every project kickoff, and starts from zero again next time
From scattered source systems to one dataset
The system checks, cleans, and standardises your data across every source feeding it, and gets better over time as your team uses it.
Ingest, by source
Connect each system's data via API or CSV, in whatever format it already arrives, whether that is an ERP module, an asset register, or a facilities contract's service lines (cleaning, hard services, security, catering).
Standardise and reconcile
Naming, coding, and reference data are corrected and standardised across every source, so the same equipment or supplier looks the same everywhere.
Flag and review
Items outside confident thresholds are flagged for your team. Decisions feed back into the engine, so the queue shrinks over time.
What clean data makes possible
Decisions you can defend
One contract-level report, not five
ERP, Fabric, and AI initiatives that work
“You feel a bit siloed, don't you?”
What is data quality automation and why does it matter before an ERP migration, a Fabric rollout, or an AI initiative?
Data quality automation uses rules, machine learning, and large language models to identify and resolve errors in operational datasets without manual intervention. An ERP migration moves data as it is. A Fabric rollout or an AI initiative assumes the data underneath it is already structured. Neither one fixes what was wrong to begin with. For hard services and FM providers running multiple service lines, cleaning, hard services, security, and catering each arrive in a different format, with reference data that was often wrong from the start. Pearstop has corrected supplier and equipment reference data for FM providers including SPIE, standardising naming across service lines and sites so the same clean dataset can support a migration, an AI model, or a single contract-level report instead of five separate ones.
Frequently asked
How is this different from just building a dashboard on top of what we already have?
A dashboard displays whatever is underneath it. If cleaning and hard services reference data do not actually describe the same thing, the dashboard just shows that mismatch faster. Pearstop fixes the data itself, so what the dashboard displays is real.
Do all our service lines need to be on the same system before this works?
No. Each service line's data is ingested from wherever it already lives, in whatever format it already arrives. Standardisation happens after ingestion, not as a precondition for starting.
Ready to fix the data underneath your next project?
Talk to sales. We will show you exactly where your data would break an ERP migration, a Fabric rollout, or an AI initiative today.
