Asset Dynamics now offering data health monitoring solutions with DataFrame
Overview
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 increasingly 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. In this context, data health is the extent to which data and information supports the needs of the organisation and its stakeholders. Businesses with high levels of data health are able to make decisions more efficiently, and can develop increasingly sophisticated decision-making processes.
Figure 1 - Asset Health and Data Health
Large and complex organisations hold significant data about the asset portfolio and asset management. In this context, improving data health requires a strategic approach. Figure 2 shows the necessary interrelationships between asset management objectives and asset information strategy, and the cycle that is 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 has identified three critical gaps in data health management.
Data held in information systems is not explicitly linked with the processes and systems that depend on them.
Comprehensive and timely measurement of the extent to which processes and systems are supported by fit for purpose data is not available.
Issues and risks associated with data that is not fit for purpose cannot be robustly prioritised, enabling tasking to address root causes of issues to be planned and implemented.
DataFrame development
These observations led Asset Dynamics to develop the DataFrame solution. DataFrame creates linkages between the organisation’s operating model and its data model. This results in full traceability between data held by the organisation and the processes that consume that data. Rather than focusing on data, the organisation can focus on the value that the use of the data creates.
In addition to linking data to value, DataFrame also provides monitoring and reporting on the extent to which the value expected from the data is being realised. This is achieved through the implementation of automated data quality checks, the results of which are surfaced through an interactive dashboard.
Introducing DataFrame
In this section we provide a brief introduction to some of the functionality available in DataFrame.
Figure 3 provides an example of how data is linked to the business requirements for that data, and the upstream business systems that are impacted if that data is not fit for purpose. The traffic light schema represents the extent to which a data requirement is met or otherwise.
In this example we have a situation where data quality checks have found that air break switch (ABS) manufacturer data is not complete (red bar at the right of the tree). These data are required by the organisations Air Break Switch Risk Model, which supports Capital Investment Decision-making - a critical business process.
Figure 3 - Data Health Dashboard - Tree View
By surfacing this information to an appropriate group of asset management and information practitioners, organisations can build an aligned and consistent understanding of where data and information issues exist. DataFrame also enables such a group to start considering solutions using the Data Defects dashboard.
In DataFrame it is possible to ‘drill-down’ on any data issues to reveal the specific data quality checks that have failed, and even the specific equipment to which the data defect relates. Figure 4 provides an example of this functionality through drilling down on the ‘red’ data quality requirement in the example above. This reveals that two air break switches do not have a manufacturer recorded, and these defects relate to switches R433 and G343.
Figure 4 - Data Defects Dashboard
Figure 4 also reveals that over the past two daily data quality checks the number of defects of this type has reduced. This may indicate that the problem is in hand, but probably requires an action from the team to confirm this is the case.
Advancing data health
DataFrame is a powerful solution for advancing data health. It is based upon tried and tested concepts in New Zealand asset-intensive businesses, and delivers immediate value by helping your asset management and information management teams understand how well data and information is currently supporting your organisation.
Find out more
More information on DataFrame, including how you can book a free demo, is available at www.dataframeinsights.com.