An Asset Master data is a collection of data records pertaining to all the fixed assets that a company maintains in its ERP or EAM systems. These assets may have been in use in the past, present, or were part of the procurement process at some point in time.
The sheer scale of operations at enterprises with heavy production operations means several assets need to be deployed or replaced at any point in time, thus generating a lot of asset-related data. This requires rigorous processes to be adopted so data management workflows pertaining to these assets are centrally managed to maximize data quality
This discipline of managing, categorizing, governing and maintaining asset data is commonly referred to as “Asset Master Data Management”.
Advanced use-cases in Asset MDM can also extend to integrating and synchronizing the asset master with other related master data domains like Supplier Master, Materials (spare parts) or Service Master.
The table below summarizes what an asset master is referred to as in different ERP systems
ERP System | Asset Master Term | Module / Area |
SAP | Asset Master Record | FI-AA, PM |
Oracle | Fixed Asset / Asset Master | Oracle Assets |
Microsoft D365 | Fixed Asset | Fixed Assets |
IFS | Object / Equipment | Asset Management |
Infor | Equipment / Asset | EAM |
NetSuite | Fixed Asset Record | Fixed Assets Management |
Asset Master Data Model
Here’s a standard data model of an equipment master that is used in most production operations. Naturally, the properties and fields maintained can change depending on the purpose and extent of operational complexity:
| Field | Field Type | Example |
| Asset ID / Number (unique identifier) | Free Text with Validation | EQP-2025-001 |
| Asset Description / Name | Free Text | High-Pressure Hydraulic Pump for Cooling System |
| Asset Type / Class | DropDown | Equipment → Mechanical |
| Asset Group / Category | DropDown | Pumps → Rotating Equipment |
| Serial Number | Free Text with Validation | SN-HP-984521 |
| Manufacturer Name | Free Text | Siemens Industrial Solutions |
| Model Number | Free Text | HPP-X2000 |
| Supplier / Vendor | Free Text | ABC Engineering Supplies Pvt. Ltd. |
| Tag Number / Barcode / RFID | Icon (SVG or supported format) | ![Barcode Icon] (icon.svg) (e.g., RFID: RFID-778899) |
| Location (Plant, Building, Floor, GPS) | DropDown | Plant A → Production Block 2 → Floor 3 (GPS: 40.7128 N, 74.0060 W) |
| Status | DropDown | Active / In Use |
| Acquisition Date | Date Format | 2022-04-12 |
| Commissioning / Installation Date | Date Format | 2022-06-05 |
| Expected Useful Life | Numerical (Months/Years) | 180 months (15 years) |
Challenges with Asset Master Data
Similar to the well-known challenges in master data management, an asset master requires governance processes to maintain the integrity and accuracy of the data.
In cases where the data quality of an asset master is already compromised, then a thorough data cleansing exercise is generally a prerequisite to ensure the data quality pertaining to the fixed assets remains intact.
Most of the challenges that an Asset MDM software aims to solve for are commonly known challenges across other master data domains.
However, some challenges are unique to asset data records that require a distinct approach to resolve and ensure that asset data remains synchronized and reliable on a going basis.
Some of these challenges are detailed below.
A standard problem that isn’t unique to fixed assets data, duplication of data records is one of the leading challenges in any master data domain.
Distributed requestor teams across different plant locations, combined with the absence of data governance processes and an unstructured approach to data entry, invariably lead to duplicated equipment records.
The Duplication Dilemma says that
Duplicate items range from 15–20% within a single manufacturing site. Across multiple sites, the duplicate rate can go as high as 25–30%.
Since every data record is unique [Learn About Golden Record in MDM], eliminating these duplicates requires a software-based approach, preferably one with embedded AI that can flag duplicates at scale and ensure that only clean data is maintained in the system.
Duplication of equipment records can directly result in mis-procurement, Maverick Spending and an increase in procurement and storage costs; and can result in inefficiencies across procurement, maintenance and production management workflows
Any given piece of equipment consists of numerous individual parts, spare parts and consumables that are required for ensuring timely upkeep of the fixed assets.
The supplier typically details these parts and consumables in a Bill of Materials (or BOM) that is specified either in technical documents, engineered drawings or shared separately with the client.
According to Faster Capital’s electronic manufacturing client success story,
Integrating BOMs with inventory and production systems has led to a 30% reduction in excess inventory in some case studies.
As per best practices in ERP data management, this BOM information should be updated in the system and linked with the relevant fixed asset record in the equipment master.
This ensures that maintenance crews and production planning teams are aware of the procurement requirements for upkeep of any of the fixed assets that are deployed at a given production facility.
Here’s a walkthrough of our Software:
According to Market Growth, in aerospace / defense and heavy equipment industries, version control of BOM and hierarchical BOM use has become standard: roughly 58% of global users employ hierarchical BOMs to manage configurable and customizable products.
As per best practices in asset master data management, the spare parts and consumables required for upkeep of a piece of asset should be linked to the spare part in the MRO master.
Similarly, best practices in Materials Data Management require that all spare parts linked to asset BOMs do, in fact, exist in the material master, and are linked with the dependent piece of equipment as well
Before the introduction of embedded Agentic AI systems, mapping this data was resource-intensive and required dedicated teams, but specifically trained AI models, like Synchronize, not only eliminate human intervention but also complete the workflow almost instantaneously.
This mapping of data ensures that;
- Critical maintenance spare parts are stored at least at a minimal required level at the relevant plant locations
- Maintenance planning for business-critical assets can be done in a far more informed manner for seamless manufacturing operations
Almost all enterprises follow a specific taxonomy, especially in mature and asset-intensive organizations.
A taxonomy is nothing but a centrally maintained protocol for storing the data records in the ERP. It enables organizations to manage, classify and maintain information in a structured format.
Some commonly accepted taxonomies for assets and MRO parts are UNSPC, PIDX, eClass and ISO 14224.
More importantly, though, a centrally managed taxonomy will ensure that description formats are standardized, specific data sheet formats are maintained based on the “asset category” and specific attributes, units of measure, manufacturer make or part number details are duly mapped into fixed properties or columns.
This empowers teams to introduce automations, generate insights through analytics and represents a clean, golden source of truth.
In a research (conducted by learnmar),
When a global manufacturing firm standardized 40% of its components across product lines, it achieved a 25% reduction in inventory costs.
Before advanced data management software solutions like Integrity© began using AI agents to autonomously categorize and extract this information, enterprises would need to invest in data normalization projects periodically simply to ensure sanity in ERP master data.
With advanced data governance solutions built into integrity, these challenges are tackled at the source itself ensuring ERP data on a going basis.
Poor data stewardship and ungoverned data management practices breeds poor data quality.
The poor data quality is evinced in “incomplete information” within any given data records and asset master data is no exception. It’s not uncommon for critical information to be completely excluded from the data record.
This is precisely where data records need to be “completed” by employing data enrichment methods.
Data enrichment tools leverage properties like “short description” to fetch information from first- and third-party data sources and completes data records
According to Xtivity’s mining client success story,
A full 39% of a company's master catalog items were identified as needing data cleansing and/or enrichment. After addressing these issues, the company achieved inventory reductions and service level improvements, freeing up cash flow by optimizing inventory stocking parameters.
Before advanced data management software solutions like Integrity© began using AI agents to autonomously categorize and extract this information, enterprises would need to invest in data normalization projects periodically simply to ensure sanity in ERP data.
With advanced data governance solutions built into integrity, these challenges are tackled at the source itself ensuring ERP data on a going basis.
Agentic data enrichment processes, like enriching manufacturer name, equipment number is now possible with advanced crawling, retrieval and AI-led data processing capabilities and can solve for lazy data stewardship right at the source itself.
What are the types of Asset MDM Solutions?
Here are a few solutions that asset-intensive enterprises can adopt to build excellence in asset MDM.
Asset Master Data Cleansing & Normalization [Services + Software]
The first step after identifying asset data-quality issues is to “clean” and “normalize” the entire asset master data. This typically means
1. Deduplication: Since master data management relies on a “Golden Record” as the single source of truth for any given entity, the highest priority is to rid the dataset of duplicate entries referring to the same piece of equipment.
This means either merging 2 records or deleting one of them.
2. Standardization: If a centrally managed taxonomy is not already in place, one of the first steps is to finalize and implement a globally accepted taxonomy (generally UNSPC or some native taxonomy) that will dictate the “data sheet” of every equipment category. Most enterprises already adopt a certain convention for categorizing and maintaining this data.
3. Normalization: The records will then be processed to extract key information and data points from the “short” or “long” description and populate the relevant properties or columns
4. Enrichment: After extracting the data points from existing descriptions, missing information pertaining to any given data record can be enriched from third-party sources to “complete” the data normalization exercise.
Here is a video demonstrating how Verdantis’ software cleanses and normalizes data:
Asset Master Data Governance [Software]
After the completion of a cleansing project, the next step is to ensure the data sanity remains intact on a going basis. This can be done by implementing a data governance software that supports fixed asset data models.
Here’s what an asset data governance software typically does
1. Real-time Duplicate Check: The governance software is already synced with the asset master, and any new procurement request typically has to pass a rigorous series of steps to ensure that the record for the same equipment doesn’t already exist in the master data.
With advanced, “context-aware” AI models embedded into the workflows, duplicate detection at source has become quite powerful and ensures a duplicate-free asset master.
2. Approval Workflow & Logs: Another critical function of an Asset data governance software is building approval workflows based on org policies, hierarchies and the established approval matrix to clearly hold the right personnel accountable.
3. Data Validation: A data governance software that supports the fixed asset data domain typically also ensures the structuring of data across different data fields and prevents incorrect naming conventions, formats, units of measure or any other configured data governance rule. This can be better explained with an example here;
Example:
A utility company adds a new power transformer to its substation. When the procurement request is logged, the asset data governance software performs a real-time duplicate check against the asset master.
The system identifies that a transformer of the same make and model already exists in the database, preventing the creation of a duplicate record.
The request then goes through the approval workflow, involving the Electrical Engineering Head, Substation Manager, and Finance Controller, each validating the technical specifications, operational need, and financial justification.
Before finalizing the entry, the system applies data validation rules by standardizing the asset name to “Transformer_GE132kV_SubstationB,” confirming the correct unit of measure for capacity (MVA), and ensuring the serial number complies with the approved format.
This ensures the new transformer record is consistent, reliable, and aligned with enterprise-wide data standards.
Here is a video demonstrating how Verdantis’ software helps establish robust data governance:
Asset Master Data Enrichment [Software]
A compromised asset master typically contains data records with “missing” details across key fields.
For example – manufacturer name, asset type, asset category, manufacturer/supplier, and equipment ID/Code.
The reasons for this missing information can often be traced to poor data governance practices, and these can be addressed by implementing structured data enrichment processes.
The process can be done manually by employing a team of subject matter experts, through a software-based approach or by deploying purpose-built AI agents that autonomously retrieve data from multiple third-party or verified sources and update the data records back into the ERP.
Below is a video demonstrating how Verdantis’ AI agent enriches the data through different internal and external sources, and flags the obsolete records:
Tracking Equipment Obsolescence [Software]
In addition to enriching standard data fields, advanced asset data management platforms also flag equipment records as “obsolete” or “active” by retrieving data from multiple sources, often with the use of AI Agents.
This is a critical process to avoid erroneous procurement and plan asset procurement accordingly.
Integrating Equipment Master with Material Master using BOMs [Software]
To build excellence in enterprise asset management, maintaining optimal MRO inventory levels is crucial. This is generally managed by a purpose-built software that depends on clean, integrated MRO Master Data.
An “integrated” or “synchronized” master data is one that highlights the relationship between different master data domains by clearly charting out the relationship between them through specifically mapped out properties.
Based on a recent Survey conducted by Verdantis’ research team spanning 1200+ respondents, almost 85%+ enterprises do not have any integrations between their spare part materials data and their asset master.
Integrating these disparate master data domains has always been possible through a programmatic approach, but is also known to be tricky.
This is typically done by integrating 3 different ERP modules;
- The Asset Master
- The Equipment BOM
- The MRO Materials Master
As per standard enterprise asset management policies, every active asset has a digital Bill of Materials created in an ERP to record the components, parts and consumables required for asset maintenance.
The BOM can help establish the link between an equipment and the spare parts required for upkeep.
Advanced Asset Master Data solutions with embedded AI agents can autonomously fetch the spare parts required for the upkeep of an equipment.
Once compiled, the agent can “Create or Update” the spare part data record in the material master.
- “Creation” of the part record is performed if the spare part doesn’t already exist
- “Updation” of the part record is performed if the spare part already exists
In either of the cases, a property (column) called ‘where used’ is updated in the ‘material master’, which is typically comma-separated and can include multiple equipment IDs
This establishes a clear relationship between the spare parts and the fixed asset and can give MRO category managers and the spare parts management software, the right insight into the parts required for equipment upkeep.
For Example: Say a CNC machine (Equipment ID CNC101) has 100 spare parts or consumables that are required to ensure upkeep of the CNC machine
A digital BOM records these 100 spare parts with their specifications.
Of these 100 spare parts, let’s say one of them, “A 35v motor pump” is also used in another “lathe machine” (Equipment ID LAT101).
In this case, the material master record for the “35v motor pump” will have the “where used” property updated and the field value will be “CNC101”, “LAT101”
‘Context Aware’ AI agents fetch the parts data from the BOMs, enriches the data, performs a duplicate-check and updates the spare parts master. [As Detailed in the Video Below]
Integrating Equipment Master with Vendor & Service Master [Software]
In a similar way, asset master data can also be integrated with the vendor master records.
This is also best done with the help of AI agents simply due to the sheer efficiency that comes with it.
Supplier Invoices and PO records can be fetched to retrieve the “Vendor” information linked to any given piece of equipment, and the same is updated in the “Vendor
master” in a ‘Equipment Supplied’ property
The Value Drivers [Benefits] of an Asset MDM Program
Fuels Inventory Management
Clean, reliable and seamlessly integrated multi-domain MRO data is the foundation of a perfect MRO inventory management software.
When combined with top-notch part criticality and spare part demand forecasting solutions, it makes for a holistic inventory management software that, in turn, can lead to outcomes like;
- Almost Negligible Downtime in Production Operations
- Optimal Spends in MRO Procurement
Spend Visibility
Analysing historical spends in detail empowers asset management and MRO procurement teams with the right intelligence that enables them to consolidate expenses and get a deeper understanding of cost-saving opportunities without hampering the value generated from asset procurement activities.
Clean data, pertaining to fixed assets, when integrated deeply with spare parts information, can uncover opportunities for supplier consolidation, bulk orders, thus improving the negotiating power and ultimately resulting in unlocked value
Verdantis' Solution for Asset Management
Verdantis is a specialist in MRO lifecycle data management with deep expertise in master data management in asset-intensive industries across Manufacturing, Mining, Energy, Shipping & Maritime and Chemical industries.
Verdantis MDM suite supports “Fixed Asset” as a data domain, and both “Harmonize” & “Integrity” modules within the suite support equipment data cleansing and governance, respectively.
The legacy data and every new data entry sync with all major ERPs with native integrations on SAP, Infor, MS Dynamics, and Oracle.
With deep industry expertise, cutting-edge technologies and logically crafted master data management processes, we’ve pioneered several agentic solutions for enterprise teams to build operational excellence and maintain ERP hygiene across key functions.
