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Using technology to meet the ESG data challenge

“If you don’t consume multiple data sets, you will get an incomplete picture. You should be in a position where you have the tools and technology to consume large datasets, and with consuming multiple datasets- together be able to bring it all together in a single scene that can be questioned by managers and investors,” Fahey says. “If I were polite, I’d say it’s difficult to do in the analog world; if I were direct, I’d say it’s impossible.”

According to Fahey, asset managers should target with a single platform—whether powered by robotics, machine learning, or any other tool—that can effectively consume ESG data. From there, they have to purge the data using AI – not just artificial intelligence, but augmented intelligence.

“You have humans and machines working together to produce better results,” he says. “You’re letting the machine focus on the things it can do much more effectively and efficiently than a human, and then the human applies its ability to glean information and insights from the data. “

Fahey says that what’s important, in addition to processing massive amounts of data, is being able to do so with quick turnaround times. In the absence of a uniform framework for the identification and classification of ESG investments, investors and regulators alike are pressing asset owners and managers to be more transparent.

“As long as they operate, questions coming from both regulators and investors need full responses, no doubt about that,” Fahey says. “Those responses need to be readily available, and coming back in a week or two with answers will not be acceptable.”

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