Top Energy implemented DataFrame to monitor the quality of the data behind its asset risk models. Within 90 days, data quality for its low voltage overhead assets had improved by 60%.
- Service
- Data health monitoring
- Platform
- DataFrame
- Implemented
- 2023
Situation
Top Energy is the electricity network provider for the Far North, supplying power to more than 32,000 consumers. One of the largest businesses in the region, it manages assets of over $680 million and employs more than 150 staff.
Providing consumers with a reliable supply of power requires highly effective asset management capabilities supported by high-quality data.
Many of Top Energy’s customers are in rural areas, meaning the network has limited redundancy. When a fault occurs, the lights go out. The network can also be exposed to extreme weather.
To help reduce faults, Top Energy decided to improve how it plans and invests in maintaining and renewing its assets by using asset health models.
“The goal was to more efficiently deploy our network investment, as well as reduce unplanned outages caused by equipment in poor health,” says Elaine Collinson, GIS Team Leader at Top Energy.
“To do that, we needed an asset information strategy that would ensure the right data at the right level of quality would be available to the asset risk models prior to commissioning.”
GIS Team Leader, Top Energy
Elaine and her team chose DataFrame, with a key requirement being to minimise the impact on Top Energy’s busy engineering and information management teams.
Solution
The DataFrame team was on site at Kerikeri within three weeks and worked with Top Energy’s IT experts through three steps.
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Step 1
Daily data quality checks
The team created more than 100 daily checks to monitor the quality of critical data held in Top Energy’s asset management information systems. The checks were customised to the criticality of the data: the impact on the models if the data was unavailable or of poor quality.
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Step 2
Dashboards
User-friendly dashboards were commissioned and connected to Top Energy’s databases, so real-time insights could be seen at a glance.
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Step 3
Governance reviews
Asset Information Governance reviews began, supported by the DataFrame team. Insights from the data health monitoring enabled Top Energy’s data users to prioritise improvements across their network.
“We started seeing benefits almost immediately,” says Elaine. “DataFrame provided a rigorous approach that was quick and easy to implement, and because the system is so flexible it meant a wide range of data across our information systems could be accessed and harnessed.”
Linking maintenance planning to data quality
Through the DataFrame implementation process, Top Energy’s asset management processes were aligned with critical data quality requirements. The data model links Top Energy’s maintenance planning strategy, the risk models for the different asset fleets, and the more than 100 data quality checks required to quality assure data inputs.
- Asset planning
- Maintenance planning
- Subtransmission overhead risk
- Distribution overhead risk
- Low voltage overhead risk
- Crossarm
- Feeder
- Overhead line
- Pole
- Tower
- Conductor type complete
- Conductor usage complete
- Length complete
- Manufacture date complete
- Operating voltage complete
An extract from Top Energy’s asset risk data model. The highlighted path runs from maintenance planning to the data quality requirements for subtransmission overhead lines.
Results
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Team alignment
By establishing asset information governance, the Top Energy team brought together data collectors, custodians and customers. This collaboration led to better decision-making and a shared sense of responsibility for data quality.
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Rapid quality improvement
After the DataFrame dashboards were implemented, a number of low-volume data defects were identified and resolved, quickly improving data quality.
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Ongoing improvements
Daily data checks are reflected in the dashboards, so opportunities for improvement are identified and fed into maintenance planning.
“Now we’re using DataFrame, it’s hard to imagine working without it. Because we’re getting better quality data, we now have greater confidence when making critical decisions that rely on that data. That’s crucial for an asset-intensive organisation like ours, and also reassuring for our customers.”
GIS Team Leader, Top Energy