Data value framework in the context of asset management

Asset management is a critical discipline for organisations that rely on physical assets to deliver value. In today's data-driven world, the effective management of asset-related data has become equally important. A value framework for asset management data helps to understand, measure, and maximise the value of the data. Ultimately it is about the data being optimised in terms of cost / risk/ performance / opportunities to support asset management objectives. This article explores the concept of a value framework in asset management, its application to asset management data, critical features, and some New Zealand examples.

What is a value framework in asset management?

A value framework in asset management is a structured approach to defining, measuring, and realising value from assets throughout their lifecycle. It provides a systematic way to align decisions about asset management with an organisation's strategic objectives and stakeholder expectations. A value framework for a utility typically includes measures for cost of an investment, the financial benefits of the investment, service level impacts and the risks mitigated such as safety, environmental and regulatory. An asset management value framework can be used to identify asset data to be collected relating to costs, benefits, risks and service measures. Recent updates to asset management standards and guidelines introduce changes that emphasise the importance of value frameworks in supporting decision-making and performance evaluation.

A value framework supports decision-making by defining what "value" means to the organisation in the context of asset management, establishing a foundation for all asset-related decisions. Organisations can then establish decision-making criteria, which serve as standards or requirements for asset management decisions.

In terms of performance evaluation, the value framework provides a basis for defining desired outcomes and assessing the effectiveness of actions in various areas, including awareness, communication, and the decision framework itself. This assessment is inherently linked to the value framework, as it determines whether actions are delivering the intended value.

The new Clause 10.3 in ISO 55001:2024 on "Predictive action" further enhances the role of the value framework in performance evaluation. It requires organisations to consider potential failures not only in asset performance but also in assets, asset management, and the asset management system as a whole. This predictive approach is guided by the value framework, ensuring that potential issues are evaluated based on their impact on organisational value.

Figure 1 shows the value framework as a central component in asset management, providing a structured approach to decision-making and performance evaluation. By requiring organisations to explicitly define value, establish decision-making criteria, and assess the effectiveness of their actions, the standard ensures that asset management activities are consistently aligned with organisational objectives and deliver measurable value.

Figure 1 - A value framework as a central component of asset management system

A value framework provides quantified value-based measurements to support investment governance for decisions related to assets within an organisation's asset management system. By implementing such a framework, organisations can:

Clarify and streamline the realisation of value from and through assets
Make the best use of limited resources in achieving organisational objectives
Focus decision-making on value generation
Align with ISO 55001 requirements for asset management

The Institute of Asset Management provide useful guidance on value frameworks in their publication Value – the Apex of Asset Management.

Applying a value framework to asset management data

When applied to asset management data, a value framework serves as a vital tool for organisations to understand, measure, and address the impact of data issues whilst optimising the value of their data assets. A recent study from United Kingdom Water Industry Research examines methods for valuing data in water asset management and concludes that impact-based approaches are the most promising and relevant. These approaches focus on the value derived from improved insights and better decision-making. Given this finding, value frameworks provide a structured method for understanding the impact and therefore the value derived from asset management data.

In the New Zealand context, where infrastructure faces challenges such as funding constraints, natural disasters and the effects of climate change, such a framework is particularly valuable.

A value framework enables identifying and cataloguing existing data assets pertinent to asset management, thereby highlighting areas where data quality might be compromised. For instance, a local council might discover inconsistencies in their stormwater network data, potentially leading to inefficient maintenance schedules or inadequate flood preparedness. By recognising these issues, the organisation can prioritise data cleansing efforts and allocate resources more effectively, which is crucial given the limited budgets many New Zealand councils operate under.

Moreover, a value framework enables organisations to explore both current and potential applications of asset management data, evaluating these through a value lens. This approach is particularly beneficial when assessing the impact of poor data quality on decision-making processes. For example, a lines company might realise that inaccurate vegetation management data is leading to unnecessary tree-trimming operations, resulting in avoidable costs and potential conflicts with communities.

A key benefit of employing a value framework is its capacity to quantify the value of asset management data, particularly in terms of its impact on asset performance, cost management and risk mitigation. This quantification supports data-driven decision-making by offering a standardised approach to assessing the value of various data-related initiatives or investments. In the context of data issues, it allows organisations to calculate the potential return on investment for data quality improvement projects. For instance, a roading agency might determine that investing in a comprehensive data validation exercise for their bridge inventory could yield significant savings in maintenance costs and improve resilience planning for natural disasters.

Furthermore, the framework ensures that data management efforts are in harmony with overarching asset management and organisational strategies. The value framework provides traceability between data inputs to decision-making and value. This alignment is crucial for maintaining focus and achieving long-term objectives, especially when addressing data quality issues. It helps organisations prioritise data improvement initiatives that align with strategic goals, whether they are enhancing mana whenua engagement or reducing environmental impacts.

Lastly, a value framework provides a solid foundation for measuring the performance and value contribution of asset management data initiatives over time. This enables organisations to track progress in resolving data issues and make informed adjustments as needed. For example, a water utility might use the framework to monitor the impact of improved asset condition data on network efficiency, quantifying the benefits in terms of reduced water losses and improved compliance with drinking water standards.

In essence, a value framework for asset management data acts as a tool that guides New Zealand organisations in maximising the potential of their data assets. By highlighting the impact of data issues and quantifying the value of addressing them, it empowers organisations to make informed decisions about data quality investments, ultimately leading to more effective asset management practices and improved outcomes for New Zealand communities.

Examples of asset management data value frameworks

In the realm of asset management in New Zealand, several value frameworks have been employed to assess the value of key data points and the repercussions of subpar data quality.

Asset Renewal Planning

Asset renewal planning is a crucial aspect of asset management that relies heavily on high-quality data. In New Zealand, where infrastructure often faces unique challenges due to geographical and seismic factors, accurate asset data is paramount. Poor data quality in this framework could lead to ill-timed renewals, resulting in either premature replacements or increased risk of asset failure. For instance, an electricity distribution business might unnecessarily replace power lines that still have years of useful life, wasting consumers’ money, or conversely, delay the renewal of a critical asset, potentially causing service disruptions.

The quality of the data will determine the quality of investment decisions by ensuring critical assets at end of life are replaced while others are not replaced prematurely. The cost associated with of not proactively replacing assets at end of life arise from unplanned asset failures, asset outages and inefficient reactive replacements. Conversely, replacing assets decades before they need to be replaced results significant inefficiency use of scarce funding. Fundamental to optimising asset replacements is accurate and up to date on asset performance and condition.

Figure 2 presents an example of valuing data required for efficient asset replacement and renewal planning for a electricity lines company. This example takes the forecast Capex expenditure and identifies the data required to most efficiently deploy these funds. This provides a basis for understanding the valuing of that data being accurate, complete and up to date.

Figure 2 - Example of valuing data required for efficient asset replacement planning

Work Delivery Optimisation

When it comes to work delivery optimisation, asset management data plays a vital role in ensuring efficient resource allocation and scheduling. In the New Zealand context, where many regions are geographically isolated, optimising work delivery is especially important. Substandard data quality results in inefficient scheduling, increased travel times for maintenance crews, and higher operational costs. For example, a power company might dispatch a team to repair a faulty transformer, only to find that the asset information is outdated, leading to wasted time and resources. Therefore, understanding what data is critical for optimising work delivery enables an organisation to ensure the right data is accurate and up to date. A value framework for work delivery data provides a view of the most valuable data to ensure is fit for purpose and how much to invest in that data.

Financial and Technical Asset Register Alignment

The alignment of financial and technical asset registers is fundamental for effective asset management and financial reporting. In New Zealand, where public sector organisations are required to comply with specific accounting standards, this alignment is particularly critical. Poor data quality in this framework could lead to discrepancies between the financial value of assets and their actual condition, potentially resulting in audit issues or misinformed investment decisions. For instance, a local council might overstate the value of its roading assets due to outdated condition assessments, leading to inadequate budgeting for maintenance and renewals. A value framework shows which data is critical for financial and technical asset register data and the financial value of keeping that data up to date and accurate.

Information Disclosures

In New Zealand, many infrastructure providers are required to make regular information disclosures to regulatory bodies such as the Commerce Commission. These disclosures rely heavily on accurate asset management data. Poor data quality in this framework could lead to non-compliance with regulatory requirements, potential fines, and loss of public trust. For example, an electricity distribution business might inadvertently report inaccurate reliability statistics due to incomplete outage data, potentially leading to regulatory scrutiny and reputational damage. A value framework for information disclosure data shows what is critical and the likely cost to the organisation of gaps in information disclosure data. This can help inform investment in ensuring this data is correct.

Low Voltage Data

In the electricity sector, low voltage data is particularly relevant for distribution networks. With the increasing adoption of distributed energy resources such as solar panels and electric vehicles, accurate low voltage data is becoming increasingly important. Poor data quality in this framework could lead to suboptimal network planning, increased technical losses, and potential power quality issues. For instance, a lines company might underestimate the capacity required in a residential area experiencing rapid uptake of electric vehicles, leading to localised network constraints and potential outages. A value framework for low voltage network data highlights what data points are critical in the new energy future, whether the organisation currently has accurate and complete data in these areas and therefore where investment is needed. It also allows evaluation of the performance of low voltage data improvement to assess the benefits accrued from improvements that have been implemented.

Conclusion

These value frameworks demonstrate the critical importance of high-quality asset management data in the New Zealand context. From ensuring efficient infrastructure renewal to meeting regulatory requirements, the implications of poor data quality can be far-reaching and costly. As such, organisations managing assets in New Zealand must understand the value of critical data and prioritise data improvements. This is essential in order make informed decisions, optimise operations, and deliver value to their stakeholders.

Our advice is to develop value frameworks for the various sets of asset management data required to understand what is critical to support an organisation’s asset management strategy. As there are common asset management processes and data requirements, standard value frameworks have been developed and are available for asset management data in New Zealand. These are tailored for infrastructure sectors and specific asset management strategies and domains.

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Data Value Frameworks for New Zealand Infrastructure