What is Enterprise Asset Management (EAM) in Oil & Gas and Energy?

Enterprise Asset Management (EAM) refers to both a discipline and a category of software — including platforms like IBM Maximo, IFS, and SAP EAM — used to plan, track, and optimise maintenance, reliability, and performance across an organisation's physical assets. EAM systems are only as good as the data behind them: tag numbers, equipment specifications, and documentation that need to be complete, structured, and kept current from the moment an asset is commissioned.

Why EAM systems depend on good source data

An EAM platform can schedule maintenance, manage work orders, and track reliability metrics — but it cannot invent a missing equipment attribute or reconcile a tag number that was never mapped correctly during handover. Poor source data is one of the most common, and most under-diagnosed, reasons EAM rollouts underdeliver: reliability engineers end up maintaining spreadsheets on the side because the system of record does not have what they need.

This is a direct consequence of what happens upstream, during project delivery. If the Master Tag Register and document handover package are incomplete or inconsistent at project close-out, no amount of EAM configuration afterward can fully compensate.

Enterprise Asset Management vs. CMMS

EAM and CMMS (Computerised Maintenance Management System) are closely related and often used interchangeably, but they operate at different scopes. CMMS is typically the maintenance-execution layer — work orders, scheduling, spare parts — while EAM takes a broader, multi-site, full-lifecycle view of asset performance, reliability, and capital planning. Both depend on exactly the same underlying tag and equipment data.

The lifecycle view: from engineering to operations

Enterprise Asset Management is meant to cover an asset's entire operating life, but the data it relies on is created much earlier — during engineering design and construction. This is why Asset Information Management and EAM need to be understood as connected disciplines rather than separate silos: AIM governs the creation and quality of asset data through the project lifecycle, while EAM consumes that data to run maintenance and reliability programmes once the asset is operational.

Common data gaps that undermine EAM rollouts

• Tag numbers that do not match between the engineering register and the EAM system after go-live
• Equipment specifications missing or inconsistent across duplicate records
• Vendor documentation and manuals that exist somewhere on the project but were never linked to the correct EAM asset record
• No clear change control process once the asset is in the EAM system, so drift re-accumulates over time

How Sharecat feeds EAM systems with validated data

Sharecat sits upstream of EAM systems as the tag register and document control layer: validated tag data, equipment specifications, and linked documentation flow directly into IBM Maximo, IFS, or SAP, so the EAM system starts from a complete and trustworthy asset baseline instead of a partial one assembled after the fact. This mirrors the same principle behind data quality management and master data governance — get the data right once, upstream, and every downstream system benefits.

Frequently asked questions about Enterprise Asset Management

Do I need a separate EAM implementation project, or can data be migrated directly? Both are common; either way, the quality of the source tag and equipment data determines how much manual clean-up the EAM implementation team needs to do before go-live — see asset data migration for more on this process.

Is EAM only relevant to large facilities? No — the same data discipline applies at any scale, though the cost of poor data compounds faster the more assets and sites are involved.