In the rail industry, asset management has historically been synonymous with the management of physical assets by dedicated teams of highly experienced practitioners and localised ownership of the asset base.
Data was considered to be of limited functional use or even an administrative burden, used only to fulfil regulatory requirements rather than being a valuable asset in its own right.
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Today, managing data assets has become just as business-critical as managing physical assets. To support the digital transformation of the rail industry, physical and data assets must be viewed as equally important elements serving different purposes.
The physical asset enables the operation of the railroad and generates revenue. At the same time, the data asset is used to understand asset characteristics, conditions, and capabilities, enabling safe and efficient network operation.
The changing nature of asset management
Every inspection, service call and sensor reading can initiate an operational response while also contributing to a valuable dataset that can be used to create actionable insights for predictive maintenance, future planning and cost optimisation.
To ensure such data provides an accurate, real-time representation of the physical asset, the data must be governed. It must be carefully designed, created, maintained, and retired – as it has its own lifecycle, similar to that of the physical asset.
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By GlobalDataWhen considering data management in the context of physical asset management, we must make the distinction between asset data and the data asset. Asset data is purely a record related to an individual asset. The data asset encompasses the entire inventory of data and derived information about the physical asset including as-built, maintenance, and warranty data, although it also includes data related to its origin, quality and condition.
Data collection is easy. So easy, that it is entirely possible to collect so much that it becomes unmaintainable, obsolete, and cannot be relied upon to make decisions. The data asset – data model/digital twin – must be designed for decision-making and maintainability so that any insights drawn from it are valid. The moral here: start small, maintain quality, and expand the dataset only where it is of benefit to the business and can be maintained.
Start small, maintain quality, and expand the dataset only where it is of benefit to the business and can be maintained.
Asset data is of paramount importance in meeting the needs of the modern railroad. For example, if the asset data is of low, poor, or even unknown quality and/or condition, it will have an effect on the efficiency of any resultant insights – potentially impacting the safe and economic operation of the system.
Implementing rigorous data governance and management intrinsically improves the quality and condition of the data held in relation to the physical asset, resulting in improved decision-making, reduced risk and improved cost controls. This ultimately builds trust in the data, driving cultural change and organisational recognition that ‘data is an asset’.
The recently published ISO 55013:2024 Asset Management – Guidance on management of data assets, provides guidance on managing data to support an organisation in meeting its asset management objectives and, by extension, its organisational objectives. It underpins the principles of governing and managing the data asset.
By implementing data governance and management, operators can identify patterns and trends that were previously opaque within their existing data assets.
The benefits of data asset management
There are three key benefits to enhancing the quality, condition and management of data assets. First is the optimisation of the asset, resources, and budget.
The objective of the managed data asset is to optimise the maintenance and renewal of physical assets – both within the traditional discipline departments and (critically) as an interactive system. For example, a rail track sits atop a drainage system, essential for its integrity. If both assets are managed independently, inefficiencies occur and risks increase, whereas the integrated data asset may predict poor drainage function ahead of time, preventing mud-spot repairs.
Data-driven insights enable operators to monitor the condition of infrastructure and rolling stock more readily, allowing them to identify underperforming assets and react before issues arise. Not only does this help optimise the performance of the asset and prevent service delays, but it can also support planning maintenance resources and budgeting.
A second benefit is enabling predictive maintenance. The transition from reactive to predictive maintenance is only possible via the data asset. Historically, maintenance was scheduled based on fixed intervals or in reaction to failure. Both approaches are inefficient and result in over-maintenance or high failure rates.
Management of the data asset to inform the performance of the physical is at the vanguard of modern railroad network performance.
An optimally designed and maintained data asset enables insights such as degradation models and failure points. This enables optimization of maintenance regimes releasing funds to be redirected into life-extension and asset renewal programs reducing service disruption, maximizing asset uptime and further increasing revenue.
The third benefit of data asset management is that it drives cost reductions. Maintenance is one of the largest operational costs for any railroad, and a compelling reason for rail operators to invest in data management is the potential for significant cost reductions.
Effective data management unlocks insights to enable optimised maintenance approaches, reduced downtime, and intelligent direction of capital budgets.
Management of the data asset to inform the performance of the physical is at the vanguard of modern railroad network performance and essential to ensure our industry is fit for the challenges of tomorrow.
