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Complex assets generate data throughout production, operation, and maintenance. Yet those records are usually organized around the systems and departments that create them rather than around the physical asset.
Engineering data may sit in PLM, production records in MES, commercial information in ERP, and maintenance history in separate operational tools. Drawings, task lists, sensor readings, and inspection evidence may be stored elsewhere again.
A workable lifecycle data structure connects these records through a persistent asset identity and a traceable history of what was built, how it was used, and what changed over time. For a closer look at the fragmentation behind the problem, see Why Lifecycle Data Is Broken in Most Organizations.
Give every asset and component a persistent identity
The starting point is an asset hierarchy that distinguishes between the product type, the individual physical asset, its assemblies, and its serialized components. Each item should have a persistent identifier that remains valid when the asset changes location, operator, or configuration. Lifecycle data cannot remain connected if the same asset is identified differently across every system.
ERP, PLM, manufacturing, supplier, and maintenance systems may still use their own internal IDs. Those identifiers should be mapped back to the same asset rather than treated as separate records.
The UK Ministry of Defence Data Strategy recommends maintaining authoritative data sources, applying shared standards, and reducing duplicate records. It also treats data as an asset that must endure beyond individual programs and systems.
Define what each lifecycle phase must contribute
Each lifecycle phase creates a different part of the asset record. The structure should make those contributions clear and prevent important information from being lost during handovers.
Production records what was built
Production should establish the as-built baseline for the individual asset. This includes installed serialized components, approved substitutions, deviations, inspections, tests, and acceptance records.
The result should show the exact configuration that left production. Maintenance teams should not have to reconstruct it later from drawings, spreadsheets, and supplier documents.
Operation records how the asset was used
Operational data describes the conditions the asset has experienced. Depending on the asset, this may include operating hours, cycles, loads, environmental exposure, fault codes, or sensor measurements.
A measurement has limited value without context. It should remain connected to the relevant asset and configuration, with a timestamp, source, unit, and operating state.
Maintenance records what changed
Maintenance records should connect faults, findings, work performed, measurements, parts, approvals, and release status to the relevant asset or component. A replacement, repair, modification, or software update can change the asset configuration. The current record must be updated while the previous state remains available.
Structured tasks also improve continuity when work crosses shifts or teams. Details, evidence, incomplete steps, and approvals can remain attached to the task instead of being handed over through email or informal notes. For a broader view of these lifecycle responsibilities, see The Complete Guide to Asset Lifecycle Management for OEMs.
Separate the product definition from the physical asset record
Research from the NIST Digital Thread for Smart Manufacturing project highlights the importance of distinguishing between the product definition and the physical asset record while maintaining a clear connection between them.
The product definition describes what should exist. It may include engineering requirements, approved configurations, bills of material, technical instructions, inspection criteria, and maintenance limits.
The physical asset record describes what actually exists. It contains the as-built configuration, current installed parts, deviations, operating history, maintenance actions, and present condition.
NIST’s work shows that both views are required. Engineering data alone cannot show what happened to a specific asset, while execution records cannot be interpreted reliably without knowing which approved definition, configuration, or instruction applied at the time.
Their digital thread approach focuses on connecting information across design, manufacturing, and product support so that downstream users can trace asset records back to the definitions that shaped them.
This connected flow is the foundation of What Is a Digital Thread?
Connect records through relationships
Lifecycle data does not need to be consolidated into one universal database. Existing systems can continue to manage the information they are designed to hold.
The lifecycle structure comes from connecting shared entities such as:
- Assets and components
- Configurations
- Requirements
- Tasks and events
- Measurements and faults
- Documents and approvals
These relationships allow a user to move from a physical component to its engineering definition, installation history, operating data, and previous maintenance actions.
Important records should also retain their provenance. This includes the source system, creator or device, timestamp, version, validation status, and any transformations applied to the data. Lifecycle events provide the time dimension. An installation, inspection, fault, repair, or modification should create a traceable event rather than overwrite the previous record.
NIST researchers have proposed a standards-based graph method for linking data across the product lifecycle. Their work demonstrates how design, manufacturing, assembly, and quality data can be connected and traced across separate domains.
This type of connecting structure is explored further in The Missing Layer in Enterprise Architecture.
Define ownership, access, and data rights early
Every important data domain needs clear responsibility. Organizations should define who creates, validates, maintains, corrects, and approves each type of record. The system administrator is not automatically the data owner. Ownership should follow operational responsibility and subject-matter authority.
For OEMs and defense operators, these responsibilities often cross organizational boundaries. Contracts should define which technical data, configuration records, maintenance information, and supporting documentation will be available during sustainment.
Data ownership, custody, access, and usage rights are separate questions. An operator may need the right to use technical data for maintenance even when the OEM retains the underlying intellectual property.
The 2025 GAO report on weapon-system sustainment and data rights found that data-rights shortfalls can increase costs, lengthen repairs, and restrict maintenance options. It also found that planning often concentrates on early acquisition rather than the needs of systems already in sustainment. Data requirements should therefore be defined before the asset enters service, when access rights and deliverables can still be built into the agreement.
Make lifecycle data secure, discoverable, and reusable
Security should determine how data is collected, stored, accessed, and transmitted. It should not automatically prevent useful information from being recorded.
Organizations need to know where sensitive data is held, how it moves, who may access it, and how long it should be retained. Role-based access and classification controls can protect information while keeping it available for authorized maintenance, planning, and analysis.
Records must also use consistent terminology, units, status definitions, metadata, and versioning. Without these controls, data from different systems cannot be combined reliably.
The UK Ministry of Defence describes usable data as assured, discoverable, interoperable, secure, and able to endure beyond individual projects. It also calls for data to be protected during creation, curation, storage, handling, and transmission.
Discoverability is equally important. The Military Review article “The Knowledge Paradox” describes how organizations can possess useful knowledge but remain unable to find or apply it. Legacy databases, incompatible formats, weak interfaces, and organizational silos can make existing information effectively inaccessible.
Structure data around the decisions it must support
Data requirements should begin with the work or decision they are intended to support. An organization may need data for configuration control, fault diagnosis, maintenance planning, readiness reporting, product improvement, or predictive maintenance. Each use requires a specific level of detail, quality, and context.
Data should be captured as part of assembly, inspection, operation, and maintenance. Asking users to reconstruct the record afterward creates gaps and removes context. Structured fields are useful when information must be validated, compared, filtered, or analyzed. Free text is better reserved for explanations and exceptions.
The Defence Horizon Journal’s article on data-driven decision-making argues that the challenge is often the management and evaluation of available information rather than a lack of data. Reliable decisions require a progression from collection and organization to analysis, prediction, and action.
The same principle applies to predictive maintenance. Advanced analytics will remain unreliable when configuration, fault, usage, and maintenance records are inconsistent. See The Role of Data in Predictive Maintenance.
A practical starting sequence for OEMs
Organizations do not need to restructure every data source at once.
1. Choose one asset type
Select an asset with a clear operational need and a manageable number of systems and stakeholders.
2. Map the lifecycle handovers
Identify where information moves between engineering, production, operation, and maintenance. Focus on the points where context is commonly lost.
3. Define the minimum common model
Agree on identifiers, configuration relationships, lifecycle events, ownership, and required metadata.
4. Connect authoritative sources
Keep each system responsible for the information it manages well. Link the required records rather than creating another uncontrolled copy.
5. Improve capture at the point of work
Make structured data part of normal assembly, inspection, and maintenance execution. Reduce manual re-entry and retrospective reporting.
6. Expand according to operational value
Once the first lifecycle flow works, extend the approach to additional assets, data sources, and analytical use cases.
Build continuity around the asset
A usable lifecycle data structure begins with persistent identity and a clear record of configuration, use, and change. It connects information across phases while preserving ownership, provenance, security, and history.
The goal is continuity around the physical asset. When that continuity exists, existing enterprise systems become more useful, lifecycle handovers become clearer, and data can support decisions long after the system or project that created it has changed.
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