Data-driven trust issues, a real-world problem for electric utilities. 

When you are responsible for the biggest machine in the world which entire societies rely upon to function, trustworthy asset data is a must. However, industry reports show that one-third of utility executives see poor asset data quality as a major challenge as it carries cross-functional impact on operations, planning and compliance. 

The lack of trust in data is due to fragmented storing across multiple systems, and in multiple formats with a lack of audit trails, causing inconsistency, inaccuracies and can lead to severe consequences for the utility such as unplanned outages, an increase in field visits, costly compliance risks and poor capital planning and investment decisions.  

Improving asset data management and ensuring high-quality data is often focused on centralizing the information, but without processes in place to validate and maintain the information these efforts quickly become a wasted opportunity, something several of our customers have reported to be true. 

How do you uplift the quality of your data at scale?  

Our Grid Vision^® solution is developed by utility professionals, for utilities to not only uplift the quality of data, but to also include visual data, which is validated and maintained over time, through an established three step process ensuring you do your quality uplift right the first time:  

Qualification: Understanding the starting point & identify gaps 
To be able to uplift the data quality, you need to understand where you are and what you want to achieve with your data.  

• Validating your data rules and models 

Your asset data strategy needs to be tied to your business rules and built on the correct data model for your business to enable utilization.  

• Verify your gaps and test the methodology 

A small sample of assets are captured in the field and compared with the asset data in the system. As well as capturing the data, images of each asset are taken and linked to the asset in the field.  The data is validated through a filling rate and qualified to assess the quality level. This step also identifies gaps in the data and tests the process in practice. 

This stage qualifies gaps in your data to determine if there is a data quality issue. It provides an insight into image-based accurate asset data and tests the process end-to end so you can successfully scale.  

Scaling: Enriching data 
As your foundation is built, filling the gaps and enriching existing data is next. By using the qualification of the sampled asset data, the asset management strategy can be rolled out to all assets. In this stage, thousands of substations can be digitalized and visually documented with a clear audit trail and consistent capturing processes within a time frame so that the data remains accurate and current.  A complete, image-based digital inventory is created through high-quality data, ready to be utilized by utility teams.  

Utilization and Maintenance
Using an image-based digital inventory, utility teams can make data-driven decisions across their business processes such as network planning, maintenance and investment decisions with confidence, as the data is accurate with a clear audit trail. However, a digital asset requires maintenance to ensure it continues to represent its physical equivalent in the field. This requires continued updates to your data to reflect and validate changes to the physical asset through maintenance activities and updates.  

Grid Vision supports every stage of the asset data management journey through its software and services, supporting each step in the lifecycle of infrastructure assets.  

Listen to how Stedin used this phased approach and utilized Grid Vision to avoid potential failures among 22,000 substations: Watch webinar here