Practical guides on data centre asset normalisation, DCIM platform migrations, data quality, and infrastructure management — written by practitioners, for practitioners.
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Every data centre team has the same problem: asset records that look different depending on who entered them. DCIM data normalisation is the process that fixes that — and it is the foundation of every successful DCIM implementation.
NetBox's CSV import is powerful but unforgiving. One mismatched manufacturer name and the whole batch fails. This guide walks you through every step — from preparing your data to completing a clean import.
dcTrack is one of the most capable DCIM platforms available — but migrating data into it requires careful preparation. Here is what experienced DC teams know before they start.
Device42 is a powerful CMDB and DCIM platform, but a successful migration requires more than just uploading a spreadsheet. Here is what the preparation actually looks like.
Before you can implement DCIM, you need accurate asset data. But most organisations discover their inventory is far messier than they thought. Here is how to build a solid foundation.
"Dell", "Dell Inc.", "Dell EMC", "Dell Technologies" — four names, one company, and a data quality problem that will break your DCIM import. Here is how to fix it.
Running out of rack space mid-project is a preventable problem. But prevention requires accurate capacity data — and most organisations discover their capacity data is wrong only when they need it most.
The NetBox Device Type Library is one of the most valuable resources in the DCIM community. Here is how to use it effectively — and how to contribute back when your devices are not in it.
Not all asset records are created equal. A quality score tells you which records you can trust and which ones need attention before they go into your DCIM platform.
NetBox, dcTrack, and Device42 are three of the most widely used DCIM platforms. They have different strengths, different data models, and different ideal use cases. Here is how to choose.
Slugs are the silent killer of NetBox imports. They look simple — just lowercase letters and hyphens — but the rules governing them are strict, and a single collision can fail an entire batch.
Most DCIM migrations fail not because of technology but because of project management. Here is the phase-by-phase playbook that experienced DC teams use to get from spreadsheet to live platform.
Compliance audits for NIS2, EN 50600, and DORA all start with the same question: can you prove what you have and where it is? Most organisations cannot answer that question on demand. Here is how to prepare.
Resolving 'Liebert' to 'Vertiv', 'LENOVA' to 'Lenovo', and 'APC by Schneider Electric' to 'Schneider Electric' sounds simple. At scale, with 400+ variants and acquisition history to track, it requires a structured three-layer approach.
A rack capacity report is one of the most valuable outputs of a data normalisation exercise. Here is the exact process for building one from a raw asset CSV — without needing a live DCIM platform.
When an auditor asks for your asset inventory, handing over a spreadsheet is no longer sufficient. The question is not just what the inventory says — it is whether the inventory can be proven unmodified, when it was generated, and what changed since the last audit cycle.
A data centre is a specialised facility designed to house critical IT infrastructure. But not all data centres are the same — and the asset normalisation challenge looks very different depending on whether you run an enterprise DC, a colo, a hyperscale fleet, an edge node, or a modular deployment.
The Netherlands hosts more than 300 data centres and is one of Europe's largest DC markets by capacity. EU EED and NIS2 have placed new data quality obligations on Dutch operators — and the asset inventory is the foundation of both.
Most DCIM import failures are not caused by bad data — they are caused by data that is formatted correctly for humans but not for machines. Here are the 12 errors that account for 90% of failed imports, and exactly how to fix each one.
A serial number should uniquely identify a physical asset for its entire lifecycle. In practice, most DC inventories contain placeholder serials, duplicates, and blank fields that make asset tracking unreliable. Here is why it happens and what to do about it.
A DCIM platform migration touches every team that depends on accurate asset data. This 47-point checklist covers the data quality, platform readiness, team preparation, and rollback planning steps that separate successful migrations from ones that drag on for months.
NetBox's REST API turns your DCIM into an automation platform. This guide covers the key endpoints, authentication patterns, and Python examples for the most common DC automation tasks — from bulk device imports to rack utilisation monitoring.
Power density — watts per rack unit — is the single most important capacity metric in a modern data centre. This guide explains how to calculate it from your asset inventory, identify thermal hotspots before they cause incidents, and produce the EU EED Annex I pre-report that European regulators now require.
An equipment register is only as useful as the quality of the data inside it. This guide covers every stage of the normalisation process — from raw CSV to clean, scored, import-ready records — and explains why each step matters for DCIM migration, EAM implementation, and compliance audit.
Every mine site maintenance team has the same problem: equipment records that look different depending on who entered them, which site entered them, and which contractor last touched the register. EAM data normalisation is the process that fixes that — and it is the foundation of every successful EAM implementation.
Maximo's bulk asset import is powerful but unforgiving. One mismatched SITEID and the whole batch fails. This guide walks you through every step — from preparing your equipment register to completing a clean import into Maximo 7.6 or MAS 8.x.
SAP PM Equipment Master imports require a different approach to mine-site data than most EAM platforms. This guide covers the complete import process — from field mapping to functional location hierarchy to the exact format LSMW and BAPI_EQUI_CREATE expect.
The average mine site carries 15–25% duplicate parts in its MRO catalog — the same part recorded under different part numbers, different OEM names, or different descriptions. Normalisation eliminates duplicates, standardises descriptions, and enables accurate spare parts linkage in your EAM platform.
A quality score of 85 on a mine-site equipment record means something specific: the record has a confirmed OEM name, a valid serial number, a parseable mine hierarchy location, and a classified equipment category. Here is exactly how that score is calculated — and why mining records are scored differently from data centre assets.
In mining M&A, the equipment register is often the most problematic data room asset. Acquirers spend weeks manually cleaning it — and still miss the signals that matter. Here is what a normalised EAM dataset actually tells you about a target's maintenance maturity.
Hexagon EAM's Data Import utility is powerful but requires precise field mapping. One mismatched ORGANIZATION code and the entire batch fails. This guide covers the complete import process for mine-site equipment registers — from field mapping to status codes to the exact CSV format Hexagon expects.
ISO 55001 certification requires a documented asset register with specific data quality standards. Most mine sites attempting certification discover the same gaps: missing serial numbers, inconsistent OEM names, and no formal criticality classification. Here is what the standard actually requires — and how to close the gaps.
P&H and Bucyrus no longer exist as independent companies — and haven't for over a decade. Every time a mine site runs a vendor analysis or migrates to a new EAM platform, legacy brand names produce wrong answers. Here is the acquisition history your equipment register needs to know.
Underground mine location data uses level designators, drift names, stope identifiers, and bench codes that no standard location parser handles correctly. The result is that location fields arrive in EAM systems as free text — unsearchable, unreportable, and useless for maintenance planning.
Scope 1 greenhouse gas emissions from mining operations are primarily generated by mobile equipment. Accurate Scope 1 reporting starts with knowing exactly what equipment you operate — how many units of each type, what their power ratings are, and what fuel they consume. That is an equipment register problem before it is an emissions calculation problem.
Migrating from CMMS to EAM is a critical opportunity for data cleanup, not just a transfer. A strategic approach to data auditing and exclusion is essential to prevent day-one disasters and maximize your new EAM system's potential.
Directly loading legacy data into a new EAM system is a recipe for disaster. Learn why staging tables are indispensable for data integrity and operational efficiency in EAM migration.
Migrating from CMMS to EAM is a cleanup opportunity. The hardest part is deciding what data to decommission. This framework guides you through identifying and managing irrelevant data for a cleaner migration.
Legacy CMMS systems often store asset data in a flat structure, lacking the hierarchical relationships modern EAM platforms require. This deficiency leads to significant challenges in PM scheduling, cost roll-up, and reliability analysis. Struktive automates the complex process of building a proper EAM hierarchy from flat data.
EAM migrations often overlook PM schedule generation, a silent failure point that can lead to missed maintenance and compliance issues. Proactive testing and accurate data are crucial for success.
Duplicate MRO parts, or 'ghost parts,' silently inflate inventory costs and disrupt supply chains at mine sites. This article explores how these duplicates emerge and their significant financial impact.
The parallel run period is the most skipped step in EAM migrations — and the most expensive to skip. This post covers what a parallel run is, why 2–4 weeks minimum is the industry standard, and what to validate during this critical phase.
Many EAM implementations falter because User Acceptance Testing (UAT) is conflated with IT system testing. This crucial distinction ensures that the system truly supports maintenance engineers' daily workflows.
Mining M&A deal value rose approximately 60% year-on-year to US$114.6b in 2025. Behind every transaction is an equipment register that the acquirer needs to trust. Most aren't ready for that scrutiny.
Most EAM migrations fail at the parts catalog, not the equipment register. The equipment register is messy but bounded — a mine site has a finite number of assets. The parts catalog is unbounded, accumulated over decades, and touched by every contractor, every OEM, and every storeroom manager who ever worked the site.
Natural Resources Canada's critical minerals initiative is raising the bar on supply chain transparency. For mine operators, that means demonstrating not just what equipment you operate — but where it was manufactured, by whom, and under which trade and environmental frameworks.
Most mine operators treat equipment provenance as a one-off exercise — something assembled when a regulator asks for it. The operators who treat it as a standing record save significant time and money when audits, transactions, and regulatory submissions arrive.
Equipment provenance has moved from a procurement footnote to a board-level compliance obligation. Three forces are driving that shift: the geopolitical contest for critical minerals supply chains, tightening sanctions enforcement against equipment from restricted jurisdictions, and the arrival of mandatory ESG supply chain disclosure requirements.
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