Big data has become big business. It’s also a big headache. With the already staggering rate of data production (we create 2.5 quintillion bytes of data a day) predicted to grow exponentially, data intelligence is becoming more important than ever.
The treasury function has long been both risk-averse and under-invested. It has traditionally been seen as a cost, rather than a potential profit, or even strategic centre. However, says Jane Turner, Chief Strategy Officer at real-time treasury management fintech, Centtrip, with the ever-expanding burden of regulatory compliance, increasing complexity of treasury operations, and growing focus on liquidity management and predictive capabilities, corporate treasury teams are finding themselves in catch-up mode.
“After some initial reservation about the security and robustness of new technological solutions, many treasurers are seeing their businesses transformed by digital innovation,” she states. “However, it’s no longer sufficient to just be tech-savvy, they must also become data-savvy.”
Digital innovation across businesses and functions is only set to continue, and data generated in these systems will help drive operational efficiencies. This could put the treasury function back on the map, says Turner.
Big data analytics systems, cloud computing, new AI and machine learning tools and technologies such as robotic process automation (RPA) are some of the latest solutions developed to ensure unified and secure access to data.
Perhaps more importantly, these tools are also designed to automate data collection, processing and analysis with the aim of helping corporate treasurers to make informed decisions and to drive business efficiencies at a number of levels.
It is essential for treasurers to understand that harnessing data can not only help their companies learn, grow and improve, but that it can also enable the idea of ‘real-time’ treasury. This concept is becoming increasingly important in today’s fast-paced business environment. But how should treasury – as a traditionally, but necessarily, conservative function – address the onslaught of fintech, and benefit from it?
Change takes time and companies without a data strategy in place yet should probably start devising one now, advises Turner. A recent report commissioned by Deutsche Bank suggests that only 53% of 300 senior corporate treasury executives surveyed had a well-defined data strategy in place. Evidently there is still plenty to be done in this area.
For those with a successful strategy, shifting to real-time treasury is a natural and achievable goal, Turner believes. However, she adds that many are not prepared for such a change and have yet to become data intelligent.
“These changes require a new breed of treasurer who knows how to get the best out of emerging technologies and the mountain of data at their fingertips,” she counsels. “Understanding and translating data, to create better products, provide better services, and produce more in-depth insights, are becoming essential elements in the new treasurer’s toolkit.” Those that rise to the challenge “will be ever-more in demand as everything from Brexit to trade tensions buffets the global economy”.
The reason is simple. Access to accurate data on payments and other transactions helps treasury functions have a clear view of their cash positions in different parts of the business, and better forecast cashflows. It can also help mitigate FX risk exposure, and even assist in predicting liquidity and foreign exchange or hedging requirements.
Creating a strategy
In its white paper ‘The 5 Essential Components of a Data Strategy’, analytics, business intelligence and data management software vendor, SAS, shows that where business units require access to the same data content, there is often no coordination to prevent duplicated data processing, overlaps and wasted work.
With no facility for data sharing, data reuse, or leveraging economies-of-scale, the case for a mechanism to support communicating, collaborating and sharing data methods and practices is clear.
For SAS, the concept of creating a data strategy is about supporting improved accuracy, access, sharing and reuse of data. In its white paper, it puts forward five core components that must be executed as part of such a strategy:
Identify: this means establishing consistent data element naming and value conventions as a core part of using and sharing data.
Store: it is vital to ensure that there is a practical means of storing all data that allows it to be easily accessed and shared. There is no absolute requirement to store all the data in one place; it just needs to be stored once, in a way that employees can find and access.
Provision: if data is truly seen as a corporate asset, then it must be packaged and prepared for sharing as a standard business process.
Process: it is essential that the right tools and processes are made available that are capable of producing data that individuals can use without IT involvement.
Governance: proper governance should ensure that data is managed consistently across the company so that it is easier to access, use and share.
As an additional thought, having implemented a data strategy, it must be regularly maintained. Businesses change and thus so do their data requirements. SAS argues that a well-honed strategy should be seen as a “roadmap”, capable of ensuring that when gaps appear within data management, the framework and methodology will provide the means of identifying the right solution.