9 min read
Most complex asset organizations do not suffer from a lack of data. Engineering teams have product definitions. Production systems contain build information. ERP platforms hold inventory and financial records. Maintenance teams create service histories, while assets generate growing volumes of usage and condition data.
The problem is that these records rarely form a reliable picture of the asset across its lifecycle.
Data is created by different teams, for different purposes, in systems that use different structures. As the asset moves from engineering to assembly, delivery, operation, and maintenance, information is copied, summarized, re-entered, or lost. The organization ends up with large quantities of data but limited ability to use it for decisions.
This is one of the central lifecycle data management challenges facing OEMs and operators. It affects asset availability, maintenance planning, product improvement, customer support, and the ability to develop digital threads or digital twins.
If you want to understand how to build a connected, reliable view of your assets across every phase, explore our complete guide to asset lifecycle management for OEMs.
Data exists, but it does not follow the asset
Effective lifecycle data should connect information from design, production, and product use in a way that allows decisions made in one phase to be informed by outcomes in another. The US government’s Manufacturing.gov overview of digital manufacturing emphasizes this as a foundation for improving not just individual processes, but overall enterprise performance by enabling continuous feedback between lifecycle stages.
In practice, this continuity is rarely achieved. Engineering information is typically held in PLM systems, while production data is distributed across MES, ERP, quality platforms, or even local spreadsheets. After delivery, maintenance records are often captured in separate systems managed by the operator, and usage or inspection data may be stored independently or recorded in unstructured formats.
Each system may work adequately within its own boundaries. The failure appears when someone needs to understand the asset across those boundaries.
Every handover weakens the data
The asset lifecycle contains repeated handovers between systems, teams, suppliers, and organizations. Each handover creates an opportunity for data to lose context.
These handovers are rarely managed as a continuous flow of information that stays tied to the asset. Instead, data is exported into spreadsheets, converted into PDFs, attached to emails, or recreated in another system. Identifiers may change between lifecycle phases, component records may lose their link to the specific asset configuration in which they were installed, and free-text maintenance notes may use different terminology for the same failure.
Over time, the organization accumulates several partial versions of the asset.
Empact’s experience working with complex asset organizations reflects this operational reality. Teams commonly work across email, Excel, SharePoint, and fragmented databases. These tools make it difficult to maintain a current overview of ownership, deadlines, evidence, and required actions. The information may technically exist, but it cannot be reliably followed across the workflow.
Better data structures must account for what happens before, during, and after every lifecycle handover. See our guide on how to structure asset data across lifecycle phases.
Broken lifecycle data leads to weak decisions
Fragmented data creates consequences far beyond inconvenient reporting. Maintenance plans become dependent on fixed intervals because reliable usage or condition data is unavailable. Spare parts forecasts rely on assumptions rather than actual consumption and failure patterns. Engineers struggle to distinguish isolated faults from recurring design or production issues. OEMs cannot see how their products behave after delivery.
The UK Ministry of Defence has described a similar problem as a data paradox. Although defense organizations generate growing volumes of information, much of it remains inaccessible in contractual or organizational silos, is stored in overlapping holdings, or cannot be exploited consistently.
The same pattern appears in manufacturing and asset support. More data does not automatically produce better decisions. The data must be discoverable, trustworthy, sufficiently detailed, and connected to the question being asked.
This is especially important for long-life assets. Decisions made during assembly may affect maintenance years later. A configuration change introduced during sustainment may alter the validity of earlier instructions. Without a continuous record, teams are forced to reconstruct the asset history from incomplete sources.
Poor asset records create accountability problems
Lifecycle data also fails when organizations do not establish clear ownership for its quality.
A 2025 US Government Accountability Office report on contractor-acquired defense property examined how contractors tracked government assets associated with major defense acquisition programs. GAO tested asset records at Army, Navy, and Air Force contractor locations and assessed whether key data had been recorded accurately in contractor property systems, finding that inaccuracies and inconsistencies in the records limited the reliability of asset tracking and oversight.
The report concerns government property accountability rather than the full asset lifecycle, but it illustrates a broader issue: asset data cannot be assumed to be accurate simply because it has been entered into a formal system.
Records require ownership, verification, governance, and a defined purpose. Without these controls, organizations can have systems full of data while remaining uncertain about what assets they have, where those assets are, how they are configured, or which record should be trusted.
Lifecycle data management therefore includes more than technical integration. It requires agreement on who creates the data, who maintains it, which standards apply, and how errors are corrected.
Security can become another data barrier
Defense organizations have valid reasons to limit access to asset and operational data. Usage patterns, configurations, locations, maintenance findings, and system performance may all be sensitive.
The problem arises when the only available options appear to be unrestricted sharing or no sharing at all. Teams may avoid capturing information because they are unsure how it will be handled. Data may be isolated within security domains that prevent legitimate downstream use.
This protects information in the short term but weakens lifecycle visibility. Maintenance teams lack operational context. OEMs lose feedback from the field. Operators become dependent on manual reporting.
The goal should be to give organizations clear control over how data moves, where it is shared, and who can access it. Data sovereignty, role-based access, traceability, and secure transport should be built into the lifecycle data flow. Security should determine how information is shared, rather than preventing useful data from being captured.
Predictive maintenance depends on strong data foundations
Predictive maintenance depends on more than sensors and analytical models. It requires reliable connections between condition data, asset configuration, maintenance history, operating conditions, and failure outcomes.
If these records use different asset identifiers or sit in disconnected systems, the model receives an incomplete view. If maintenance actions are recorded inconsistently, it may be impossible to determine whether a predicted failure occurred or whether a component was replaced for another reason.
Organizations can invest in analytics while still lacking the basic lifecycle records needed to generate dependable predictions.
The same applies to digital twins. A model may represent the intended design or current sensor readings, but it cannot reflect the real asset accurately without information about how the asset was built, modified, used, inspected, and maintained. This is why understanding the role of data in predictive maintenance is critical to building reliable models.
A digital thread provides the connections that allow data to move between these stages, linking lifecycle information into a continuous flow. To explore this concept further, see what is a digital thread.
The digital twin uses that connected information to represent and analyze the asset. The two concepts are closely related, but they solve different parts of the lifecycle data problem. For a clearer comparison, read digital twin vs digital thread.
What usable lifecycle data looks like
Good lifecycle data does not require every record to live in one system. It requires the information to remain understandable and usable across systems.
At a minimum, lifecycle data should be:
-
Connected to a specific asset and configuration:
Records must show which asset, component, revision, or serial number they describe. -
Structured around lifecycle events:
Assembly, inspection, deployment, maintenance, modification, and failure data should be connected to the event that created it. -
Owned and governed:
Responsibilities for creating, validating, maintaining, and correcting data must be clear. -
Secure and accessible to authorized users:
Protection and controlled use must be considered together. -
Traceable to the work performed:
Organizations should be able to see who completed an action, what information they used, what they found, and what changed. -
Usable beyond the original project:
Data should survive program handovers, system replacements, contract changes, and workforce transitions.
These principles create continuity without demanding a single all-encompassing platform.
Fix the workflow before adding more technology
Organizations often approach fragmented lifecycle data as an integration project. Integration is important, but connecting systems will not solve inconsistent records or work that still happens outside controlled processes.
The first step is to map where important lifecycle data is created and where it becomes disconnected.
Which decisions will the data support? Which workflow produces it? Who is responsible for recording it? Which asset or component should it be linked to? Who needs access during the next lifecycle phase?
From Empact’s work with complex asset organizations, one recurring pattern is clear: lifecycle data improves when it is captured in the flow of work, rather than reconstructed later from emails, spreadsheets, and disconnected reports.
The goal is not to collect everything. It is to preserve the information needed to understand the asset, support the people responsible for it, and make better decisions throughout its operational life.
FAQ