Reimagining Treasury Analytics Without a Major Data Overhaul
# AI
# Technology
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Treasury4’s Ed Barrie explains why starting small and keeping data in its native format is the smart way to build an AI-ready foundation.
Treasury teams don’t need a massive project to restructure or normalize every dataset before applying AI. Instead, they can take a gradual approach that preserves data in its original form while enriching it with context and definitions. That’s the method endorsed by Ed Barrie, co-founder and chief product officer of Treasury4, a fintech that offers a cloud-based analytics platform.
In a new video clip from an upcoming Strategic Finance Lab podcast episode sponsored by Treasury4, Mr. Barrie outlines how his firm helps companies enrich datasets one at a time—starting with transaction-level banking data—and apply structure without overhauling systems or forcing everything into a single model.
Watch the video below to learn why keeping data in its native format, paired with strong definitions and metadata, is a more effective foundation for future analytics and AI than launching a top-down transformation.
Start small—and keep data in its native form. Mr. Barrie emphasizes that companies don’t need to cram all their data into a unified model to make it usable for AI. Instead, he says the smarter path is to retain original formats while applying structure and definitions over time.
“It doesn’t have to be like this big bang project,” he says in the video. “You can start one data set at a time—just keep adding it.”
That’s the approach Treasury4 is using with customers: beginning with bank statement data and enriching it through transaction categorization and tagging that aligns with the statement of cash flows. Once that structure is in place, other data sets—such as investment custody, merchant processing, or accounting feeds—can be added incrementally.
Let AI interrogate your data—don’t force it to conform. By preserving depth and dimensionality in the original data, treasury teams can allow AI and machine learning models to deliver richer insights—especially when paired with clear business definitions.
“Whatever question you want to have, and whatever prompt you want to come up with against that sea of transactional data, it ultimately becomes much more effective by having a rich definition around it,” Mr. Barrie said.
He added that treasury will need experts at “prompting”—asking the right questions of AI models trained on their data to unlock insights.
