Machine Learning Engineers in Oil Will Become 'Bored' Without Quality Data



Machine Learning Engineers in Oil Will Become 'Bored' Without Quality Data
Machine learning engineers being hired into the oil and gas industry will become 'bored' if companies can't provide quality data for them to work on.

Machine learning engineers being hired into the oil and gas industry will become bored if companies can’t provide quality data for them to work on, Duncan Irving, Teradata oil and gas practice partner, has warned.

“Speaking from a data management point of view, I think as an industry we need to bring data right back to a central focus so we can empower these digitalization agendas in the operators,” Irving said in a presentation at a London energy conference attended by Rigzone.

“That means we need to have consistent standardization across an organization’s data, it needs to have full provenance, you need to have very strong governance around it,” he added.

Irving said that this would allow data to have the highest quality necessary for the role it’s playing.

“There should be some idea that quality and governance [are] being applied consistently across all of the domain silos as they exist at the moment. If you don’t do that, the AI [artificial intelligence] engineers, machine learning engineers that are being hired into the industry will get bored because they do not have data that’s fit for purpose for them to work on,” said Irving.

“You can’t use poor quality data that’s not well integrated to do these predictive things that we are expecting of it,” he added.

Several oil and gas companies, including BP plc, Royal Dutch Shell plc and Chevron Corporation have championed the benefits digitalization can offer the sector.

Earlier this month the International Energy Agency released its first Digitalization & Energy report, which revealed that artificial intelligence offers a ‘great deal of promise’ for upstream operations.



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