The digital shift in rail asset management: Elevating data to an essential asset

A coach yard operated by the Long Island Rail Road, the only active railroad yard in Manhattan. Seen from the extension to the High Line, a linear park in New York City.

However, the rail industry has reached a turning point. Data is no longer a by-product or a nice-to-have —it is a cornerstone of effective asset management, critical to optimizing operations, minimizing costs, and ensuring safety.

This evolution is driven by the recognition that physical and data assets, while distinct, are interdependent. To achieve the full potential of the rail industry’s digital transformation, rail operators must treat these two types of assets as equally important.

Physical and data assets: A dual approach

Physical assets are the backbone of the rail industry, enabling operations and generating revenue. However, physical assets do not exist in isolation. They generate data—on performance, condition, and use—that holds the key to understanding and improving their function. This data, when treated as a strategic asset, can empower operators to make informed decisions, enhancing safety, efficiency, and profitability.

To unlock this potential, the industry must view data as more than a supporting player. Data assets are a rich resource, offering insights that can allow rail operators to monitor conditions, predict failures, and optimize maintenance schedules. When properly managed, they are as integral to network performance as the physical infrastructure itself.

The rise of data-driven asset management

Railways generate large amounts of data through inspections, repairs, and sensor readings. However, managing this data is no small task. It must be governed with the same care as physical assets — designed, maintained, and eventually retired when it is no longer useful.

This lifecycle approach to data is essential to maintaining its relevance. Poorly managed data can quickly become overwhelming, leading to outdated, inaccurate, or unreliable information. This can undermine decision-making, compromising both safety and cost efficiency.

To maintain an efficient and effective data model, operators should focus on starting small and expanding strategically as needed. This ensures that every data point adds value and supports business objectives.

Defining the data asset

A key step in effective data management is understanding the distinction between “asset data” and the “data asset”.

· Asset data refers to specific records about individual assets, such as maintenance logs or condition reports.

· The data asset encompasses a broader set of information, including design data, warranty records, and performance metrics. This means that the data asset is critical for assessing the physical asset’s reliability.

By organizing data into a cohesive data asset, rail operators can create a digital twin—a virtual representation of the physical network. This enables real-time monitoring, predictive analytics, and more informed decision-making.

The benefits of robust data asset management

Enhancing the quality, condition, and management of data assets offers three key benefits:

1. Optimized asset utilization and budgeting Managing physical and data assets together allows operators to optimize the performance of their infrastructure. For instance, rail tracks depend on drainage systems for stability. If these systems are managed in isolation, problems such as poor drainage can lead to costly and unplanned track repairs. Integrated data management can predict such issues, enabling better communication across departments and preventative measures that save time and money.

Data-driven insights can also help operators identify underperforming assets, schedule maintenance more effectively, and allocate resources where they are needed most – making operations more effective and in turn the rail service more reliable.

2. Facilitating predictive maintenance Reactive maintenance—fixing problems after they arise—is costly and disruptive. Scheduled maintenance, while more efficient, often results in unnecessary work as it’s based on pre-set intervals rather than asset need.

Comparatively, predictive maintenance offers a better approach, using data to anticipate failures before they occur.

By analysing patterns in asset performance, operators can identify early signs of wear or degradation. This allows them to intervene proactively, reducing downtime, extending asset life, and ensuring uninterrupted service. Predictive maintenance can also free up funds for other priorities, such as asset renewal programs or technological upgrades.

3. Reducing operational costs Maintenance is one of the most significant expenses in rail operations. Effective data management can substantially reduce these costs by streamlining processes and minimizing inefficiencies.

For example, high-quality data assets enable operators to target maintenance efforts where they are most needed, avoiding unnecessary work and reducing waste. They also support more strategic capital investments, ensuring that every dollar spent delivers maximum value.

Laying the foundation for future success

To fully realize the benefits of data-driven asset management, rail operators must adopt a governance framework that ensures data quality and integrity. The recently introduced ISO 55013:2024 standard provides valuable guidance in this area. It outlines best practices for managing data assets, helping organizations align their data strategies with broader operational and business goals.

The future of asset management is data

As rail networks become increasingly complex, the ability to manage data assets will be a defining factor in their success. Operators who embrace data will gain a competitive edge, unlocking new levels of efficiency, safety, and innovation.