Role of Master Data in Asset Management
In enterprise data management, the term “asset” typically refers to “fixed assets” or “equipment.” Managing these assets is essential for asset-intensive organizations involved in large-scale production and manufacturing.
The vast scope of production operations at such companies generates substantial data, making it necessary to adopt data-first processes. These processes ensure that workflows are centrally managed and optimized to maximize operational efficiency.
For effective asset management, several specific types of master data must be “complete,” “clean,” organized according to a standardized taxonomy, and uniquely identifiable.
Below, we highlight a few key data types:
Asset Master Data
This is also commonly referred to as “equipment master data.”
Asset master data serves as a central repository for all “fixed assets” along with their associated data points.
Typically, an asset master includes the data points listed below, though the specific details may vary between organizations based on their requirements, operational scale, and industry.
Asset Master Data Model
Here’s an overview of the fundamental information typically stored—along with their formats—in a standard asset master data model:
Asset ID/Number – A unique code used to identify and track a specific asset and its associated details.
Asset Class/Category – A defined classification of the asset, such as “Lathe Machine” or “CNC Machine.”
Description/Name – A free-text field providing either a short or long description of the asset.
Asset Status – Details whether the asset is in-use or not
Capitalization Date – The date from which the asset will begin depreciating
Useful Life – Used for calculating depreciation
Location – The plant or production facility where the asset is located, typically represented by location codes.
Manufacturing Serial Number – A unique code that identifies the manufacturer and the specific equipment.
Cost Centre – The functional department responsible for the allocation of the equipment’s cost.
BOM Code – A code that links the asset to its corresponding Bill of Materials (BOM) information within the Enterprise Resource Planning (ERP) system.
Work Order Codes – A code that links historic “work orders” maintained in the ERP to that specific piece of equipment
Challenges with Asset Master Data
Like the well-known challenges in master data management, an asset master requires robust governance processes to ensure data Assure and accuracy.
Additionally, if the quality of asset master data is compromised, a comprehensive data cleansing effort will be necessary to restore reliable and trustworthy information.
Unlike other master data types, asset master data management extends beyond the master model itself. It also requires up-to-date data in the BOM, work orders, and MRO masters to ensure smooth and efficient asset management operations.
Duplication
Data duplication is a common issue—not unique to fixed asset data—and remains one of the leading challenges in data management.
Distributed requestor teams across multiple plant locations, combined with a lack of data governance and absence of a data-first approach, often result in duplicated equipment records.
Since each data record should be unique, eliminating duplicates requires a software-driven solution—ideally one with embedded AI that can identify duplicates at scale and maintain only clean, accurate data in the system.
PureData© and Assure© by Moresco provide advanced legacy data cleansing and fixed asset data governance solutions. Their embedded AI extracts key attributes, specifications, and measurement units to detect potential duplicates—even when common properties or IDs are missing between records.
Duplication of equipment records can lead to mis-procurement, increased maverick spending, higher procurement and storage costs, and inefficiencies across procurement, maintenance, and production management processes.
Unlinked BOM Data
Each piece of equipment comprises numerous individual parts, spare parts, and consumables necessary for the timely maintenance of fixed assets.
Suppliers typically list these components in a Bill of Materials (BOM), which may be included in technical documents, engineering drawings, or provided separately to the client.
According to best practices in ERP data management, BOM information should be updated in the system and linked to the corresponding fixed asset record within the equipment master.
This linkage ensures that maintenance teams and production planners have clear visibility into the procurement needs required for maintaining any fixed assets deployed at a given production facility.
Today’s advanced software solutions like Synchronize© utilize embedded AI and computer vision technologies to automatically extract BOM details from technical documentation, creating a digital record of the fixed asset BOM.
Additionally, Synchronize© links the BOM ID to the corresponding equipment record in the “asset master,” empowering maintenance teams by automating routine data entry and updates.
Syncing Asset & MRO Master Data
According to best practices in asset master data management, spare parts and consumables necessary for asset maintenance should be linked to the corresponding items in the MRO master.
Similarly, best practices in Materials Data Management dictate that all spare parts linked to asset BOMs must exist in the material master and be associated with their respective equipment.
Prior to the advent of embedded Agentic AI systems, mapping this data was resource-intensive and required dedicated teams. However, specialized AI models like Synchronize now eliminate the need for human intervention and complete the process almost instantly.
This data mapping guarantees that:
- Essential maintenance spare parts are consistently stocked at necessary levels across relevant plant locations.
- Maintenance scheduling for critical business assets is better informed, supporting uninterrupted manufacturing processes.
Unstructured Data Records
Nearly all enterprises, particularly mature and asset-intensive ones, adhere to a specific taxonomy.
A taxonomy is essentially a centrally maintained protocol for organizing data records within an ERP system. It helps organizations manage, classify, and maintain information in a structured and consistent manner.
Some commonly accepted taxonomies for assets and MRO parts are UNSPC, PIDX, eClass and ISO 14224.
More importantly, a centrally managed taxonomy ensures standardized description formats, consistent data sheet structures based on the asset category, and proper mapping of specific attributes such as units of measure, manufacturer details, and part numbers into fixed fields.
This standardization enables teams to implement automation, derive insights through analytics, and maintain a clean, reliable source of truth.
Prior to advanced data management solutions like Assure©, which use AI agents to autonomously categorize and extract information, enterprises had to periodically undertake data normalization projects to maintain consistency in ERP data.
With Assure’s built-in advanced data governance capabilities, these issues are addressed at the source, ensuring ongoing accuracy and reliability of ERP data.
Incomplete Records
Poor data stewardship and ungoverned data management practices breeds poor data quality.
Poor data quality is often reflected in "incomplete information" within data records, and asset master data is no exception. It is common for critical details to be entirely missing from the records.
This is exactly where data enrichment methods come into play to “complete” the data records.
Data enrichment tools use properties such as "short description" to retrieve information from first- and third-party data sources, completing the data records.
Before advanced data management solutions like Assure© utilized AI agents to autonomously categorize and extract information, enterprises regularly invested in data normalization projects to maintain ERP data consistency.
With Assure’s built-in advanced data governance, these challenges are addressed at their source, ensuring continuous accuracy of ERP data.
Agentic data enrichment processes—such as enriching manufacturer names and equipment numbers—are now achievable through advanced crawling, retrieval, and AI-driven data processing, effectively addressing poor data stewardship at its source.
Moresco' Solution for Asset Management
Moresco specializes in enterprise lifecycle data management, with extensive expertise in asset-intensive industries such as Manufacturing, Mining, Energy, Shipping & Maritime, and Chemicals.
Our AI-embedded software solutions address legacy data normalization and governance through PureData© and Assure©, offering both self-service and do-it-yourself models.
Combining deep industry knowledge, advanced technologies, and well-designed processes, we have pioneered several agentic solutions that empower enterprise teams to achieve operational excellence and maintain ERP data quality across key functions.
Below, we highlight some of the key benefits these software solutions provide:
Explore our client use cases below to see how we have helped organizations achieve enriched and standardized asset data.
Incomplete Information
Missing information is a significant challenge often caused by poorly managed MRO master data systems. For effective MRO data management, each MRO record must be accurately categorized and sub-categorized, and updated with essential details such as manufacturer name, part number, and unit of measurement to provide comprehensive context about the part.
Maintenance teams often depend on detailed technical information, and missing data can lead to unnecessary part requests, causing duplication, delays, production downtime, and ultimately lost revenue.
Purpose-built software for MRO Master Data Management, such as PureData & Assure, can automatically enrich part and product details with manufacturer names and part numbers to close this information gap.
