A practical guide to the Brick Schema ontology — how Brick models buildings, how it compares to Haystack, and how to stage messy source data into a clean, validated Brick model.
A step-by-step walkthrough of converting a messy BACnet point list into a validated Brick Schema model — classification, the two relationship graphs, SHACL validation, and export.
A practical comparison of Brick Schema and Project Haystack — tagging vs ontology, reasoning power, the RealEstateCore merger, and why you don't have to choose blindly.
Digital-twin platforms are easy to buy and hard to feed. Why the underlying building data model — not the visualisation — decides whether a twin actually works.
Maximo locations, assets, and classification explained for implementation teams. How to structure the object model correctly before you load a single record.
Why fleet standardisation programmes underdeliver, and how a data staging layer closes the gap between platform consolidation and real operational consistency.
Your Maximo migration will fail without clean data. Here's exactly what to fix - asset hierarchies, duplicates, field gaps - before you move to MAS 9.
The step-by-step process for building an SFI-based asset hierarchy in your CMMS, from licence acquisition to fleet-wide rollout.
Why the best CMMS implementations never load data straight into production. Learn what data staging is, when you need it, and how it prevents costly rework.
MXLoader is unsupported by IBM and disclaims all liability. When it damages production data, who pays? Why consultants add a staging layer first.
Without criticality assessment, maintenance teams operate blind. Why 20% of equipment drives 80% of business risk, and how to prioritise it.
RDS-PS and ISO 14224 solve different problems but work together. Why EPC vendor data never arrives RDS-compliant, and how to fix it before go-live.
You can't standardise maintenance on assets you can't find. Here's how naming chaos quietly kills your CMMS investment.
Why multi-vendor projects quietly create the biggest data risk in any CMMS implementation, and how to spot the corruption before it reaches production.
Six months into implementation, your consultant sends you 47 tabs of asset data. You have two weeks to review it. Here's why this moment breaks most projects.
Data quality problems derail most CMMS upgrades. Here's how to catch them before they cost you 6 months and £200K in consultant time.
Most CMMS implementations violate ISO 14224 without anyone realizing it. Why identical assets get different PM schedules, and how to fix it systematically.
Standardise PM schedules across identical equipment in minutes. See inconsistencies side-by-side, fix with one click, export back to your CMMS.
70% of CMMS implementations fail due to bad data, not bad software. How AssetStage's visual workspace solves the data quality crisis legacy methods can't.
Failed imports, Excel formatting errors, duplicates, timeouts — the most common MXLoader problems in Maximo and the practical fixes that actually work.
How combining SFI equipment identification with ISO 14224 reliability standards creates a comprehensive framework for maritime maintenance management.
With 109,000+ vessels and maintenance eating 30% of operating costs, most shipping still runs on spreadsheets. Here's what maritime CMMS does differently.
A detailed comparison of RDS-PP and RDS-PS reference designation systems, and how RDS-PS improves on its predecessor with broader applications.
Learn how to structure your asset hierarchy for maximum efficiency. Includes real-world examples from manufacturing, utilities, and facilities management.
Everyone's talking about AI in maintenance, but the real ROI comes from something simpler: clean, standardised data. Why quality beats algorithms.
Maximo is object-oriented and maintenance-centric; SAP PM is process-driven and enterprise-integrated. How they differ on hierarchies, work orders and PMs.
Everything you need to know about migrating from legacy systems to modern CMMS platforms. Includes templates, checklists, and common pitfalls to avoid.
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