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November 3, 2021updated 06 Dec 2021 9:02am

Four principles to harness data for asset management

Every rail organisation sits in an environment that becomes more complex by the day, necessitating an organisational change to enable adaptative processes and evidence-based planning for success.

Help us understand what challenges you are facing within digital asset management by completing a short survey. As a thank you for your participation, for every survey completed, Arcadis Gen will sponsor staff in committing one hour of work with sustainable charities in 2022.

Pivotal to addressing evolving challenges and external pressures is an organisation’s ability to leverage technology and data effectively, to establish smart asset management. When done correctly and optimised over time, an organisation that harnesses the power of asset data establishes lean, efficient, compliant, and adaptive decision-making processes, yielding value in operational efficiencies, as well as to end-users through the reduction of failures and unplanned outages.

Smart asset management allows the mitigation of risk and helps organisations to become more resilient, enabling real-time, evidence-based responses to emerging challenges. Yet, real-time responses cannot come about from static spreadsheets that capture a specific moment in time, emphasising the importance of establishing effective data practices.

Effective data practices within smart asset management require collating datasets and leveraging insights from across the organisation to have a holistic view of the past, the present state of asset health and predicted future scenarios.

For those getting started within smart asset management and the harnessing of data, this can be overwhelming. Through Arcadis Gen’s experience developing digital solutions for asset-intensive rail organisations in the UK and US, there are four key principles for effective data practices in asset management.

Principle One: leveraging data is a journey, start where possible

Decision-making maturity using data does not occur overnight, rather it is a journey composed of three stages of decision-making maturity.

The first stage is to map out what has happened and what is currently going on in the organisation right now. This enables evidence-based decisions based on what the data reveals, particularly about the current state.

The second stage is to combine data sets, even if starting just with two, to reveal insights on asset performance. By demonstrating that when X happens, then Y is impacted in this way, the organisation can make simple predictions and develop plans for potential scenarios. At this stage, the organisation become more resilient as predictions allow for advanced planning and decisions to become more evidenced.

The third stage is where the data sets can provide more nuanced answers to questions like ‘What should the organisation do?’, ‘When should this be done?’, and ‘What would be the impact of this not being done?’. This is the stage where data is typically utilised across the organisation to make decisions about investments, prioritisations, or asset health.

Most organisations start at stage one and build through the stages of decision-making maturity. It is important not to delay getting started and to start at the realistic stage for the organisation currently. Building this maturity from an early stage allows time to develop the right processes, including version control to minimise human error and the shift from disparate spreadsheets to software solutions.

Principle Two: aim for a relevant data set and incorporate ongoing learnings

Simply gathering any form of data regarding the organisation’s assets will not always yield insights. If unnecessary datasets are compiled and maintained, there is a greater risk of important data points or insights being lost within the granularity of what has been collected.

Instead, it is important to think about what data is meaningful to the health and operational logistics of the organisation’s assets. This ensures the focus in maintaining up-to-date data is on high-quality datasets that are directly relevant to organisational objectives. For those looking for more guidance on relevant asset data fields for rail, it is important to look for asset management software that has sector expertise, as this will pinpoint the most relevant datasets for the objectives of a rail organisation.

As most organisations begin their data journey, statistical analysis of comparable assets or assumptions made with expert judgement are often used to fill any gaps in what datasets are readily available. This enables the organisation to glean initial insights, and slowly adjust or replace the assumptions over time as learnings occur from different result sets.

Principle Three: refresh regularly and develop a single source of truth

To gain the best insights from the available data, it is important that when possible available data is set up as a live stream, ensuring accuracy whenever viewed. When this is not possible, data must be refreshed regularly so that insights are reliable.

For the most accurate data, over time an organisation should aim to develop a single source of truth. Whether this is by establishing a central hub for all data, or by establishing active linkages between the data systems used so that a data change in one system is consequently visible in all other systems. In the rail industry, it is additionally important to aim to establish integrations with planning cycles or inspection schedules, so that all datasets held by the organisation are up to date.

Principle Four: plan for adaptability and achieve a state of least regrets

Amid evolving challenges such as climate change, global health concerns or extreme weather events, an important benefit of maximising data insights is in being able to predict the impact on assets within potential scenarios. Potential scenarios should consider external factors to the organisation, as well as internal factors, creating a set of possible futures for which data insights can be created.

When possible futures are planned for, the organisation is demonstrating resilience and can make evidence-based decisions in real-time when such futures occur. This combined with a regular uncertainty analysis creates a state of least regrets in asset management and leverages the power of data for long-term organisational success.

Help us understand what challenges you are facing within digital asset management by completing a short survey. As a thank you for your participation, for every survey completed, Arcadis Gen will sponsor staff in committing one hour of work with sustainable charities in 2022.

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