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How Avangrid transformed it’s data management

The first step for utilities deploying artificial intelligence? Ditch the spreadsheets.

When Mark Waclawiak started running the operational performance team at Avangrid in 2020, he quickly realized the whole utility was hungry for data. Similar to many other utilities, Microsoft’s Excel was the “heart of data management,” limiting the potential for analytics and insights.

“Our biggest challenge was these underlying infrastructures and systems that are not necessarily built for analytics,” said Waclawiak on the With Great Power podcast. “We were looking at…Access databases from 1997 and a series of Excel spreadsheets.”

The first thing the operational performance team did was move data out of primitive data management applications into something more nimble. They decided to ditch Access and Excel, and build SQL databases.

“We unified the data. We started cleaning the data. We started improving the processes,” which immediately created “huge improvements in what we were capable of,” said Waclawiak.

The team started with operations data because “so much of their work has had to rely on the subjective.” Waclawiak wanted to make the operational decision making process objective by creating insights with data. “Right off the bat, we started developing…significantly more advanced reporting, but reporting tailored to the needs…of our operational people,” he added.

Vegetation management data was one of the first datasets they targeted because it could immediately improve reliability.

The operational performance team also worked to combine deficiency maintenance notifications, or equipment failure data, with outage notifications. That way, Waclawiak’s team could “make smarter decisions about where [they] address both our maintenance programs and our investment programs,” he said.

Then, they tackled their first major project: building a unified outage database that integrated all of the outage data across Avangrid’s operating companies. “We were looking at a series of Excel spreadsheets and Access databases, and we essentially dismantled all of that and built pipelines from our different systems to just a centralized SQL server,” Waclawiak said.

From there, they started adding additional data, like regulatory comments, customer data, and field worker observations: “You start getting all of these different capabilities from just having a simple SQL server database,” he said.

They quickly moved on to building a SQL database for millions of asset records, which are the “core of the utility business.”

From there, they added program data. “When you centralize program data into a SQL server database, you’ve got three relatively simple, straightforward, relational databases that are all now essentially interlinked,” Waclawiak said.

With these three interlinked databases, Waclawiak said his team could start developing “really powerful tools.” He likens it to building a house: “You can’t really build a house from the roof down,” he said.

Waclawiak says AI hinges on good data in available structures that can be continuously built up. “If you’re trying to build an AI model…whether it’s machine learning or [generative] AI on a series of…disparate Excel spreadsheets or Access databases, you’re doomed to fail,” he said.

Waclawiak’s team is currently building a new model of their service territory, called Geomesh, that combines historical data with various conditions to predict grid performance.

“You really give yourself the opportunity to start asking these questions that we previously thought were unanswerable,” he said, “just because we took the approach of centralizing all of our data in very clean, very well structured tables, and just simple relational databases.”

In this episode of With Great Power, host Brad Langley goes deep with Mark Waclawiak about how Avangrid built the foundation for its work in AI.

Read the original article from Latitude Media here.


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