Introducing DataFrame: data health monitoring for asset-intensive organisations
Having the right data to support asset management decision-making is a critical issue for infrastructure organisations and other asset-intensive businesses. The challenges facing the New Zealand infrastructure sector mean that establishing structured and systematic approaches to data and information management cannot be deferred.
Just as asset managers need to understand asset health, they also need to understand data health. Data health is the extent to which data and information supports the needs of the organisation and its stakeholders. Organisations with high levels of data health make decisions more efficiently and can develop increasingly sophisticated decision-making processes.
Asset health and data health
Figure 1 - Asset Health and Data Health
Large and complex organisations hold significant data about their asset portfolio and asset management activities. Improving data health requires a strategic approach. Figure 2 shows the necessary interrelationships between asset management objectives and asset information strategy, and the cycle required to continually improve data health. All infrastructure and asset-intensive businesses should be able to demonstrate how they manage and control this fundamental process of the asset management system.
Figure 2 - Data health management
Gaps in data health management
Asset Dynamics' research identified three critical gaps in data health management that DataFrame was designed to address.
First, data held in information systems is often not explicitly linked with the processes and systems that depend on them. Second, comprehensive and timely measurement of the extent to which processes and systems are supported by fit-for-purpose data is typically unavailable. Third, issues and risks associated with data that is not fit for purpose cannot be robustly prioritised, making it difficult to plan and implement actions to address root causes.
How DataFrame works
DataFrame creates linkages between an organisation's operating model and its data model, providing full traceability between data held by the organisation and the processes that consume it. Rather than focusing on data in isolation, the organisation can focus on the value that the use of data creates.
In addition to linking data to value, DataFrame provides monitoring and reporting on the extent to which that value is being realised. This is achieved through automated data quality checks, the results of which are surfaced through an interactive dashboard.
Figure 3 provides an example of how data is linked to business requirements, and to the upstream business systems impacted if that data is not fit for purpose. The traffic light schema represents the extent to which each data requirement is met.
In this example, data quality checks have found that air break switch manufacturer data is not complete (red bar at the right of the tree). These data are required by the organisation's Air Break Switch Risk Model, which supports capital investment decision-making — a critical business process.
Figure 3 - Data Health Dashboard - Tree View
DataFrame enables asset management and information practitioners to build an aligned and consistent understanding of where data and information issues exist. It is also possible to drill down on any data issue to reveal the specific data quality checks that have failed, and the specific equipment to which the data defect relates.
Figure 4 provides an example of this functionality. Drilling down on the red data quality requirement reveals that two air break switches do not have a manufacturer recorded, relating to switches R433 and G343. The dashboard also shows that over the past two daily data quality checks the number of defects of this type has reduced, indicating the issue may be in hand.
Figure 4 - Data Defects Dashboard
DataFrame in practice
DataFrame is now in use across multiple New Zealand asset-intensive businesses, delivering immediate value by helping asset management and information management teams understand how well data and information is currently supporting the organisation. Read our Top Energy case study to see how DataFrame has been applied in practice. It is based on tried and tested concepts developed through Asset Dynamics' consulting work and is continuously improved to meet the evolving needs of our clients.
Want to improve your organisation's data health?Book a free demonstration of DataFrame and see how it can help your organisation understand and improve the quality of its asset management data.
Learn moreBook a demo