1
Data Consolidation
Asset data from any source - spreadsheets, ERP systems, legacy CMMS, site surveys - ingested in any format and mapped to a unified schema.
Asset Management
Your asset list should work for you, not against you.
If your asset data needs human interpretation to be useful, it is not usable. Pearstop cleans, structures, and standardises asset registers so maintenance teams, procurement buyers, and management can all work from the same trusted source.
The Problem
Fragmented asset data makes everything harder and more expensive.
Asset registers in hard services and infrastructure typically grow organically across sites and systems. Equipment names vary by site. Manufacturer fields are used inconsistently. Records are split across spreadsheets, legacy CMMS systems, and paper surveys. The result is that the data cannot be compared across sites, used for lifecycle analysis, or fed into planning tools reliably.
- ×Spelling variations and inconsistent naming mean you cannot compare assets across sites
- ×Equipment types crammed into single free-text fields make filtering and analysis impossible
- ×Maintenance planning becomes reactive because data cannot support predictive analysis
- ×Bid pricing for maintenance contracts is based on incomplete or unreliable asset information
How It Works
From fragmented registers to a trusted asset database
2
Standardisation and Enrichment
Naming conventions standardised, duplicates resolved, field mismatches corrected, and missing values filled from reference data so the same asset looks the same everywhere.
3
Linked and Analysis-Ready
The cleaned register is linked to maintenance records and cost data where available - creating an asset intelligence layer that supports planned maintenance, lifecycle analysis, and smarter bidding.
What becomes possible with clean asset data
Predictive Maintenance
Shift from reactive to planned maintenance when your data can support forward-looking analysis.
Cross-Site Benchmarking
Compare asset performance across sites, manufacturers, and contract types - previously impossible with fragmented data.
Risk Management
Identify high-risk assets and maintenance liabilities before they become costly emergencies.
What changes with Pearstop
100,000+
assets cleaned and structured
Across client deployments to date
95%
automated error resolution
Without manual intervention
70-90%
reduction in manual data work
Freeing your team for analysis, not admin
Our asset data worked for the mechanics on-site. It did not work for anyone trying to plan maintenance or run analysis on it. Pearstop fixed that.
How does poor asset data quality affect maintenance planning and FM contract performance?
Asset data quality problems in facilities management and hard services typically start the same way: asset registers built for on-site engineers rather than analytical use. Spelling variations across manufacturers, equipment types crammed into single fields, and records spread across spreadsheets and legacy systems mean the data cannot be compared across sites, used for lifecycle analysis, or fed into planning tools reliably. For FM companies, this makes predictive maintenance planning nearly impossible and exposes contracts to margin risk from reactive, unplanned work. Pearstop standardises asset naming conventions, resolves field mismatches, and consolidates records from multiple source systems - giving FM operators and infrastructure companies a clean, comparable asset register that supports planned maintenance, lifecycle tracking, and data-backed advisory services.
Ready to turn your asset data into a strategic asset?
Book a 7-minute discovery call and see exactly how Pearstop fixes your specific data challenge.