Why Bad Asset Data Will Break Your FM AI Strategy
Why the success of machine learning, automated dispatch and predictive maintenance depends almost entirely on the unglamorous work of asset register hygiene.
- ResponseOut-of-hours cover
- ComplianceStatutory testing & records
- EstatesCommercial & industrial
- ScopeHard & soft services
Every major facilities management technology initiative begins with high ambitions, but a significant portion stall or fail during deployment. In the vast majority of cases, the failure is not caused by algorithmic limitations; it is caused by dirty, incomplete, and unstructured asset data in the legacy CAFM.
The four chronic data problems in commercial estates
When auditing property portfolios across the UK, we routinely encounter four data structural failures:
- Vague asset descriptions: Equipment logged simply as "Pump 1" or "Extract Fan" with no manufacturer, model number, serial code, or duty rating.
- Broken spatial hierarchy: Assets assigned to a site without floor, room, or zone mapping, making automated routing impossible for visiting engineers.
- Free-text maintenance notes: Ten years of engineer callout logs written in shorthand ("Adjusted belt, running ok") without standardised failure cause codes.
- Orphaned plant: Equipment installed during tenant fit-outs that was never added to the landlord asset register or assigned a statutory PPM schedule.
How to clean data before buying AI tools
Before investing in advanced analytics, estate managers should conduct a physical asset verification audit: barcode or QR-tag every maintainable asset, establish parent-child relationships between primary plant and terminal units, and align asset naming with standard UK classification conventions such as Uniclass 2015 or SFG20.
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