A €1 billion technical-services provider used Pearstop to standardise and clean asset data across 250 000 installations, improving accuracy by 85 % and enabling predictive maintenance.
“We used to debate whose numbers were right. Now, we all work from the same truth.” — Digital Transformation Lead, Technical-Services Provider
Be a winner – Roger Federer playing tennis. Copyright New York Times.
A €1 billion technical-services provider wanted to use its asset data for predictive maintenance — but inconsistent naming, duplicates, and missing fields blocked progress. Pearstop cleaned and standardised the data, creating a foundation for reliable insights.
Across 20 000 buildings and 250 000 installations, assets like HVAC systems and alarms were recorded under 60 different manufacturer spellings and inconsistent type names.
Twelve data stewards manually corrected Excel exports from SAP each quarter — weeks of work that still left blind spots.
The Digital Transformation Lead needed a scalable solution that the team could validate and trust.
Pearstop matched and standardised producer and model names, flagged invalid combinations, and merged duplicates across SAP and Excel.
The process ran transparently, allowing stewards to review flagged records and push clean data back into SAP.
The project connected data and asset teams for the first time, aligning daily operations with strategic data governance.
+85% data accuracy with €100k+ in annual cost saving.