An Asset Management Data Standard (AMDS) defines what maintenance data to collect, how to structure it, and how it flows through your CMMS/EAM. With a clear standard, planners and reliability teams can find assets faster, code work consistently, and trust KPIs like MTBF and MTTR. This guide shows a practical, six-step AMDS playbook: align the asset hierarchy (ISO 14224), standardize equipment classes and attributes, define work categories, normalize failure/cause codes, integrate M&R processes, and formalize governance. The payoff is decision-ready data that accelerates planning, cuts rework, and surfaces bad actors—across single sites and multi-plant networks in North America, APAC, and the Gulf.
Goal: consistent, analysis-ready maintenance data
6 steps: Hierarchy → Classes → Work Categories → Codes → Processes → Governance
Wins: faster planning, accurate costs, better root-cause insights, safer work
What Is an Asset Management Data Standard (AMDS)?
An Asset Management Data Standard (AMDS) defines what data to collect, how to structure it, and how it flows through CMMS/EAM so maintenance and reliability teams can analyze MTBF/MTTR, reduce failures, and benchmark performance. To implement AMDS, align asset hierarchy (ISO 14224), standardize classes/attributes, normalize work categories, codify failure data, map integrated M&R processes, and formalize governance and KPIs. The outcome is consistent, decision-ready data that accelerates continuous improvement.
Why AMDS matters for CMMS/EAM and reliability (MTBF, MTTR)
Asset Management Data governance is an important part of any successful asset management data strategy. This should be a part of a company’s overall data framework. Ideally, executives and other representatives of an organization's business operations should be involved in AMDS governance efforts, as well as IT and data management teams.
The 6-Step AMDS Implementation Playbook
A monster challenge in today’s world is the enormous amount of data that is collected, modified, and stored on a day-to-day basis. An Asset Management Data Standard provides guidelines on what data needs to be collected. However, a structured approach is required to understand how the data will be collected, consumed, analyzed, and sustained to drive decisions. The intent is to provide a simplified process to drive decision making. The six key areas are:
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Develop an asset hierarchy standard
An Inconsistent asset hierarchy makes it difficult to understand the location of the physical asset in the facility as well as its relationship to other equipment within the process. ISO14224 provides guidelines of what the asset hierarchy should look like. Impacts from a consistent hierarchy will provide potential impacts as mentioned below:
- Work identification – A well-defined hierarchy makes it easy to locate the asset in the system and assign work to the asset. This improves planning efficiency, wrench time and safety, which can reduce maintenance costs.
- Cost visibility – By being able to identify work to the correct asset, costs can be tracked against the asset. In today’s low-cost environment, this helps in the pareto analysis of what are the high-cost assets
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Define equipment classes and attributes
Develop a standardized list of minimum equipment characteristics required for each specific asset class. This will ensure that:
- The operating limits (safe operating limit and integrity operating limit) for your equipment has been defined in the CMMS and is easily accessible. This should be used as triggers for alerts during condition monitoring programs and operator rounds
- Classifying assets in asset classes will help the maintenance and reliability engineering teams “bucket” data in a way that dedicated teams with specific skill sets can address. For example - centrifugal pumps within the rotating equipment class have the highest maintenance repair costs.
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Define maintenance work categories
ISO14224 provides excellent guidelines on the categories of maintenance work and the related activities. By enforcing a process and monitoring it on a frequent basis, maintenance work coming through the CMMS can be categorized. This categorization of maintenance work provides the foundation of what data will be analyzed from a reliability perspective. Some key definitions include breakdown repairs, failures, operator basic care, etc. This is essential because:
- It defines the data set that will be used to analyze maintenance and reliability performance
- It helps understand the reactive versus proactive nature of maintenance
- Provides maintenance team the tools necessary to improve business processes
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Normalize maintainable item failure & cause codes
Defining a set of maintainable item codes, failure codes and causes codes in a structured hierarchy will provide end users with a small set of relevant codes. For e.g. Centrifugal Pump, Seal, Leaking, installation. By having this information over a period, reliability improvement efforts can be targeted, and maintenance cost optimization can be achieved. Defining these codes in a methodical way will:
- Provide end users with small set (10-15) of codes to pick from
- Set-up measurement of performance for work order completion and close out
- Provide reliability and engineering teams specific and relevant information to define the problem. Typically, the cause code may not be known at the time of work execution, but efforts and metrics can be put in place to track number of outstanding work orders/ notifications that do not have a cause code etc.
Implementation should also define how that data will be consumed and what decisions will be made based on the data. For that purpose, the last 2 processes are as follows:
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Integrate maintenance & reliability business processes
As part of the implementation, a set business processes need to be defined to understand how information and data flow, how the maintenance work will be identified, planned, scheduled, executed, and closed out, as well as, how reliability will consume the data, analyze it, validate it and provide recommendations. I must emphasize that Maintenance and Reliability (and for that matter operations) are joined at the hip. One cannot function without the other.
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Establish governance, change management & KPIs
This is where leadership, governance and performance measures are required to sustain the implementation of the program. While this piece applicable to any implementation program, for an M&R implementation perspective, it is critical to pay attention to this because:
- Maintenance is seen as a cost center and is under pressure to reduce cost instantaneously.
- Reliability is often relied on after big events and not part of continuous improvement philosophy.
- Results from reliability analytics and usage of codes are often not communicated back to the shop floor. This is important for people to realize that the codes that are used and the information put into CMMS is consumed to make data driven decisions.
- History, History, History... It is difficult to prove that a failure was prevented, and cost was avoided if it was never documented correctly.
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Common Pitfalls (and how to avoid them)
| Common Pitfalls |
Risk |
Fix |
| Too many codes, not enough usage |
Techs skip coding; data becomes free-text. |
Keep a short, curated set per class; require codes on closeout; audit monthly. |
| Unclear ownership of master data |
Duplicates, drift, and one-off edits. |
Define a RACI for hierarchy, classes, and code sets; route changes through a data steward. |
| Hierarchy built for projects, not maintenance |
Planners can’t find assets or roll up costs meaningfully. |
Base structure on ISO 14224 and maintenance reporting needs; pilot on one area first. |
| Attributes aren’t standardized |
Inconsistent analytics and spare parts mismatches. |
Mandate required attributes by class with dropdowns, units, and validation rules. |
| Work categories and types are mixed up |
Preventive vs. corrective performance is impossible to compare. |
Publish a work taxonomy with examples; lock selections with picklists. |
| Failure vs. cause codes conflated |
Root‑cause trends are noisy and misleading. |
Train on the difference; make both fields mandatory; review weekly during planning. |
| No closeout discipline |
Missing time, parts, and codes; MTBF/MTTR unreliable. |
Implement closeout checklists, supervisor approvals, and data quality KPIs. |
| One‑and‑done rollout |
Standards decay after go‑live. |
Establish governance cadence (monthly DQ reviews, quarterly code set updates), and publish a change log. |
| Tool‑only mindset |
Expecting CMMS/EAM configuration to fix process issues. |
Align process maps, training, and roles alongside configuration. |
| No regional context |
Low adoption across diverse sites. |
Provide regional templates (e.g., mining in AU, O&G in Oman/Brunei) and examples that match local operations and terminology. |
AMDS FAQs
What is an Asset Management Data Standard (AMDS)?
A documented model that defines asset hierarchy, classes/attributes, work categories, and failure/cause codes so CMMS/EAM data is consistent and analysis-ready for KPIs like MTBF/MTTR.
Which ISO fits AMDS work?
ISO 14224 guides equipment taxonomy, failure modes, and data fields used in maintenance/reliability programs.
How long does AMDS implementation take?
Typically phased over 8–16 weeks per site depending on CMMS maturity, data hygiene, and training scope.
What KPIs improve first?
% coded work orders, planning time per WO, mean time to identify bad actors, and accuracy of cost-by-asset rollups.