Multifaceted and sometimes risky, technology transformation should ultimately bring benefits. But so often the focus is on the tech integration itself rather than the building blocks that lead to that point.
Data is one of those fundamental building blocks upon which the process and the outcome can succeed or fail.
Best-in-class treasury processes and systems are built on strong data practices. Data often sits across multiple systems, in different formats and locations, and bringing that together into a clean, consistent structure is essential, attendees at this year’s ACT annual conference heard.
The success of any transformation project starts with being able to walk before you can run
Speaking at the conference, Declan Ware, group treasurer at European Metal Recycling, said: “The whole world seems to be caught up in the buzz around transformation. But that risks putting the cart before the horse. The success of any transformation project starts with being able to walk before you can run, and that means having a solid data foundation on which to build.”
At its core, poor data creates familiar and critical problems: data loss, inconsistency, multiple versions of the truth, and delays. These issues don’t just slow processes down; they also undermine decision-making and reduce confidence across the organisation.
Once standardisation was in place, the need for manual checks reduced significantly
Orianne Steel (pictured above), global treasurer at Freshfields, agreed, saying: “Treasury transformation is about improving control and efficiency. Both depend on having reliable data in place. This becomes particularly important when implementing a treasury management system.”
She shared Freshfields’ experience, saying that implementing a TMS required a comprehensive data integration process. “We started out without a single source of truth. We had to integrate data from multiple sources to make sure it was accurate, timely and consistent across everything; bank statements, journals, mandates, policies, processes, formats, governance and guidelines.
“Once standardisation was in place, the need for manual checks reduced significantly. Instead of validating everything, we only needed to focus on specific exceptions, such as format or source validity. We’ve removed so much friction and smoothed out how we operate… it was the single best decision we made.”
Having accurate, well-structured data supports better auditability and strengthens the overall control environment
Data standardisation and management also bring about direct commercial gains. Ware pointed to European Metal Recycling’s business, which relies on hedging. “Looking after our data brought clear benefits very quickly as we gained better visibility and information that was both timely and accurate. With standardised data, we were able to lock in hedging rates much closer to the same point at which contracts are agreed,” he said.
Cashflow forecasting was another example. Better data led directly to improved forecasting accuracy and less leakage at the margins.
A third advantage lay in compliance and regulatory reporting. “Having accurate, well-structured data supports better auditability and strengthens the overall control environment,” said Ware.
Quality data management becomes even more important as AI is being considered. With clean, reliable data, technologies such as agentic AI can deliver meaningful improvements.
This is because AI is only as good as the data it is fed. It does not “know” facts – it predicts based on inputs. The principle is simple: garbage in, garbage out. But with clean, consistent, high-quality data, organisations can use AI with confidence and unlock far greater value.
However, Steel warned that the process of data transformation was not an overnight exercise. “It requires patience and persistence. But the benefits go beyond data itself; you also get more efficient processes and clearer ways of working once people operate with consistent, standardised information.”
Alison Ebbage is a technology journalist
