Technology
How Should Treasurers Think About Real-Time Data?
Published by Jessica Weisman-Pitts
Posted on February 22, 2024
5 min readLast updated: January 30, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on February 22, 2024
5 min readLast updated: January 30, 2026
Joe Susienka is a Senior PM at GTreasury
The most effective treasurers continuously question their data. This is especially true when trying to determine whether more real-time data could enable faster and more accurate decisions. But optimizing real-time data requires treasurers to first consider the goals they seek to accomplish—be that more responsive cash forecasting, payments, hedging, or risk assessment—and the data sources needed to fulfill those goals. Let’s take a look at how today’s treasurer should view real-time data and their strategy around it.
Very few data sources actually provide true real-time data: in most cases, “near-real-time data” is a more accurate label. It’s also important to recognize that not all data needs to be available in real-time, nor should be. Any data utilization objectives first need to be underpinned by three principle goals: achieving data accessibility, data completeness, and data security. Some data isn’t appropriate to inform real-time action and can carry a cost. Receiving current-day reporting for accounts that are being used for batch processing carries a cost from the bank to provide and will have very little impact on your visibility as they data will only be available when the batches are complete, likely overnight. Many processes are still based on batching or completing by end-of-day, like an accounts payable run for example, you don’t need real-time updates on the status of those payments as it is not going to change until the end of the day.
Treasurers also usually operate on term horizons longer than just one day. Rapid access to data is certainly a key factor that can inform faster decisions, but ensuring data accuracy and completeness are even higher priorities. Treasurers considering their approach to real-time data management should take a careful inventory of their existing data sources, then think about which sources could offer more value by providing real-time data—and which are better served by maintaining a more deliberate speed.
Depending on factors specific to each organization, some treasury teams will be in a position to simply amend their usual approach to data management to incorporate more real-time data, while others will need to completely reconsider their strategy. Treasurers at large enterprises will have data arriving from myriad sources, and each of which will have its own capacity and potential for providing its data in real-time (or near-real-time).
But again, treasurers should examine the data they receive and the advantages that specific data can offer by being timely versus being complete. As a foundation for these strategies, treasurers should first clarify the tasks that data from each source will be used for, so that they understand how to develop the best workflows to manage certain data and achieve its purpose. Treasurers can then strategize around optimizing data processes to accentuate those advantages.
Treasurers should also be sure not to try and go in alone in leveraging real-time data, but should collaborate closely both with stakeholders across their organizations and external partners as appropriate. Getting on the same page with internal business partners is always essential to more informed and effective data utilization. Greater synergy with external banks and vendors offering established technologies (or customized solutions) can help build the connectivity required to complete data-related processes in a manner that’s timely, accessible, complete, and secure.
Expanding on the point about technology: the right technology strategy is vital for treasurers to leverage data exactly as they prefer, whether in real-time or otherwise. The right deployments will enable connectivity that integrates agile treasury systems with data sources to maintain real-time accessibility throughout processes, from start to finish. A strategy that relies on open architecture technologies able to interconnect all treasury components within a singular seamless workflow, for example, can offer tremendous value. Such strategies can simplify the management of connections and data, while synchronizing treasurers’ systems with banks, ERPs, and other platforms. The end result is greater visibility, and more information and lead time to help treasurers make smarter decisions.
Newer technologies, including AI and machine learning, promise additional opportunities for treasurers to extract greater value from real-time data by enabling real-time analysis. AI/ML capabilities have begun to loom large on the treasury technology landscape, and will only become more crucial as it evolves. However, those advantages still hinge on utilizing complete and accurate data. The predictive modeling and other powerful benefits of AI/ML technology will lead treasurers astray if the data itself is incomplete or misleading. In scenarios where delaying data availability can increase accuracy and improve data analysis outcomes, the decision is clear.
Treasurers’ eagerness to achieve real-time data leads to scenarios where perfection can be the enemy of the good. Instead, treasurers should be ready to accept near-real-time as a realistic (and more effective) goal, while prioritizing the accessibility, completeness, and security of their data.
Real-time data refers to information that is delivered immediately after collection, allowing for instant analysis and decision-making. It is crucial for timely responses in financial management.
Risk management involves identifying, assessing, and prioritizing risks followed by coordinated efforts to minimize, monitor, and control the probability or impact of unfortunate events.
Data accessibility refers to the ease with which data can be retrieved and used by authorized individuals, ensuring that necessary information is available when needed.
Data completeness means that all required data is present and accounted for, ensuring that analyses and decisions are based on comprehensive and accurate information.
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