In 2006, British mathematician Clive Humby famously stated that “data is the new oil,” recognizing that the transformation of data into useful information would become a global driver of economic growth. However, while technology companies, retailers and governments have embraced data as a source of competitive and comparative advantage infrastructure managers struggle to realize full value from information assets.
Low confidence in asset information is a common issue for asset managers who are charged with making decisions that have far-reaching consequences for customers and other stakeholders. International research conducted by the Asset Management Council in 2020 found that 72 percent of asset managers do not have confidence in their organization’s asset information.1
Low confidence in asset information is a common issue for asset managers who are charged with making decisions that have far-reaching consequences for customers and other stakeholders.
This has significant implications for the achievement of asset management objectives, including ensuring safety of workers and the public, maintaining legislative compliance, meeting customer expectations, efficiently delivering work on assets, and realizing value from new technologies, such as digital twins and the Internet of Things.
Effective asset information governance supported by data health monitoring is a critical requirement for all infrastructure asset managers. This article provides an asset information governance framework, along with a practical and innovative approach to implementing data health monitoring to enable data failures to be detected and the root causes addressed.
Asset Information Governance
The goal of asset information governance is to ensure an organization will realize full value from its asset information. Asset information is all the data and information required to support and enable the processes of the asset management system, including strategy and planning, decision-making, lifecycle delivery, organization and people, and risk and review.
Figure 1: Asset Information Governance
Figure 2: Asset management system decomposition
Figure 3: Information requirements
Figure 4: Data quality requirements
Figure 5: Data health monitoring
The most important ingredient for successful asset information governance is leadership. The critical roles of leadership are to create awareness of the importance of asset information for meeting business goals and to maintain the line of sight between asset information management and the needs of the organization.
The most important ingredient for successful asset information governance is leadership.
Figure 1 shows a basic model for asset information governance. The model features these key steps:
- The needs of the organization and its stakeholders are translated into asset management objectives.
- An asset information strategy specifies the top-level approach to ensuring asset information will be effective to support the achievement of the asset management objectives.
- Plans are developed and implemented to support the achievement of the asset information objectives.
- Monitoring processes measure performance against the asset information objectives, informing the review of the asset information strategy.
- An asset information governance committee, chaired by a senior executive and composed of asset management and information management subject matter experts, provides leadership and control of this process.
Implementing Data Health Monitoring
Beyond securing a suitably competent and motivated senior executive champion, the most significant challenge to overcome when implementing asset information governance is defining the asset information needs of the organization. The goal is to define these requirements at a level of precision appropriate for enabling data health monitoring.
Decompose the asset management system
The asset management system can be thought of as a collection of objectives and processes to achieve those objectives. It may be logically decomposed to present the hierarchical relationships between the processes. Process decomposition is a valuable exercise for many purposes, but for asset information governance, it enables traceability of how asset information is consumed to deliver organizational capability.
An appropriate number of levels of decomposition varies depending on the size and complexity of the organization, however, three levels is generally effective. Figure 2 shows a partial decomposition of the asset management system for an electricity distribution business.
In this illustrative example, The Asset Management Landscape subjects are used as the basis for the subject area level and the capability level.2 Two business processes are identified at the sub-capability level.
Define information requirements
The next step is to identify the information requirements for each process at the sub-capability level. This typically requires engagement with the organization’s subject matter experts.
In Figure 3, seven information requirements relating to asset risk modeling processes are identified.
Define data quality requirements
To ensure asset information meets the requirements of the organization, it is not enough to just identify the information requirements; data quality requirements also must be specified. This is because while information may exist, it may not meet business needs. Data quality requirements may be specified using the six data quality dimensions defined in the Data Management Body of Knowledge.3
In Figure 4, five high criticality data quality requirements for air break switches (ABS) have been identified. They are: switches have a valid date of manufacture, the manufacturer is identified, the correct operating voltage is assigned, and the equipment has both complete and valid serial numbers.
Monitor data health
After decomposing the asset management system and defining information and data quality, the necessary structures are now available to implement data health monitoring. This is achieved by identifying the asset management information systems where the data is held and implementing data quality checks. Timely feedback can be achieved through automated database queries, resulting in a clear understanding of asset information risks and opportunities.
Figure 5 provides a snapshot of data health for the asset management system using a traffic light schema.
The visualization shows that ABS manufacturer data does not meet the completeness data quality requirement. This is a problem for an electricity distributor because switches produced by different manufacturers have different failure modes and, therefore, different lifecycle management strategies. If the manufacturer is unknown, then the correct strategies may not be applied, creating risk in the assets.
This report provides the asset information governance committee with a clear and actionable insight: focusing on identifying and addressing the root causes of incomplete ABS manufacturer information will meaningfully improve the organization’s capital investment decision-making capability.
Conclusion
Realizing full value from asset information is essential for modern infrastructure organizations to meet stakeholder expectations. Asset information governance provides the structured and systematic framework necessary for this to be achieved on a sustainable basis.
Asset information governance requires timely and accurate feedback on the extent to which asset information meets the needs of the organization. This requires linkages between the processes of the asset management system and the data held in asset management information systems to be established. The data health monitoring approach presented in this article provides a rigorous foundation for the continual improvement of asset information.
References
- 1.Asset Management Council. “AM Sector Report 2020.” The Asset Journal, Vol. 14, No. 01, pp. 67-78, 2020.
- 2.Global Forum on Maintenance and Asset Management. The Asset Management Landscape, Second Edition, 2014.
- 3.DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition. Bradley Beach: Technics Publications, 2017.