Access to reliable data is particularly difficult in global organisations where data originates or resides in different systems, teams and locations. This can result in fragmented, incomplete data in multiple formats that is difficult to put together.

Participants in the Economist Impact webinar, Building a Data-Driven Treasury emphasised that the journey towards a data-driven treasury may be a long one, but it is an essential path on which treasurers should either be leading or following. The panel comprised:

• Damian Glendinning, Chair of the Advisory Board, CompleXCountries (Chair)
• Catherine Hill, Senior Director, Global Treasury, Salesforce
• Mario Del Natale, Director, Global Digital Treasury, Johnson Controls
• Claudia Villasis-Wallraff, Head of Cash Management Treasury Advisory, APAC, Deutsche Bank

Treasury has always been data-driven. Treasurers process, report and make decisions based on mountains of data every day, whether payables and receivables, bank balances and transactions, business budgets and forecasts, market information or risk exposures.

Today, however, there are unprecedented opportunities to harness data in new ways. Some companies are choosing to develop algorithms or business rules in order to use artificial intelligence (AI) or machine learning (ML) for automatic reconciliation or cash flow forecasting. Others are producing and executing FX hedge decisions, digitising processes from end-to-end across supply chains, or developing more sophisticated data analytics to support strategic decision-making.

The value of high-quality data is not only enabling innovative techniques in process automation and decision support, it is also essential for the treasury basics. Cash flow forecasting is routinely highlighted as treasurers’ top priority in treasury surveys; for example, 56% of participants in the EACT Annual Survey 2022 stressed this was crucial.

As webinar participants emphasised, data projects can have a notable improvement in this area. Catherine Hill, Salesforce said, “Cash flow forecasting is something we are constantly trying to improve, with different data needs to create short-term or long-term forecasts and company-level cash flow analysis. Timeliness of data is essential to achieve this, and we have improved our forecasting as a result of the data initiatives we have undertaken. Furthermore, you need to steward data to make sure that historic data, for example, can be used for forecasting.”

To take advantage of new opportunities to digitise and automate processes, and drive improved decision-making, treasurers need to have confidence in the accuracy, timeliness and completeness of data they produce within the department or bring in from internal or external sources.

This is not easy in practice. Multinational corporations (MNCs) often have many systems with data ownership spread across the company. Some of the challenges associated with bringing this data together in a single location are technical, such as reformatting data, and enriching or correcting data held with different fields or levels of detail. To help overcome this, many companies are trying to standardise the way that data is produced, stored and exchanged based on international standards such as ISO 20022.

Sharing and integrating data is also an organisational issue, however. Data may not be referred to in the same way, so establishing a common language to identify data is key. People need to be comfortable sharing data across departments and understand both how it is being used and the controls that are in place. Finance and treasury teams then need the skills to make the best use of data.

"We feel there is value in bringing data from across finance together into a data hub"
Catherine Hill, Senior Director, Global Treasury, Salesforce

“The first challenge we have is to collect data from our banks and bring it into the company. Having done that, we then need to gather this data from different systems and entities into our treasury management system (TMS). This can be a multi-step process, which takes time and introduces an element of risk,” explained Salesforce’s Catherine Hill. To overcome this, she continued, “we feel there is value in bringing data from across finance together into a data hub. On top of this, we use visualisation tools to enable our subject matter experts to make sense of and use the data to make the decisions we need to support the business effectively.”

Mario Del Natale, Johnson Controls then outlined the challenges facing his organisation with the solutions put in place. “Our banking strategy is to have one bank per country wherever possible, but even when this is feasible, the result is a large number of banks, accounts, electronic banking systems and security tokens. We also have more than 180 ERP platforms and instances. The scale and complexity of our banking and ERP infrastructure drove us to create a payments factory to which all ERPs, banks and transactions are linked,” he explained.

According to Del Natale, while they recognised that the data held within the payments factory offered considerable value to the business, there were a variety of steps needed to realise this:

1. They had to establish common terminology and understanding to be bank and business function-agnostic.
2. They needed to agree how data would be used, particularly around consistent analytics. For example, some parts of the group were using budgets and business forecasts to create cash flow forecasts, while others were using historic data.
3. They had to develop and maintain data skills in treasury to ensure that they were managing and using data in the most appropriate way.

“These represent a considerable investment but are essential in creating a data-driven treasury and finance function,” he said.

While the challenges – and opportunities – associated with improved management and use of data are most apparent for MNCs, data underpins effective treasury processing and decision-making across companies of all sizes. Small and medium-sized companies (SMEs) may experience fewer difficulties around data fragmentation, disparate ownership and siloed business functions. At the same time, they often lack the resources and access to blue-chip technologies enjoyed by their larger peers. Increasingly, SMEs are developing cross-functional skills across finance and IT to overcome this. Furthermore, cloud-based and software-as-a-service-based applications are increasingly accessible to smaller businesses, enabling them to achieve the same – or an even greater – degree of data analytics and process automation as larger companies.

One of the potential benefits of centralising data and standardising the way this is exchanged is to take advantage of opportunities for intraday, regularly scheduled or even real-time data, particularly through the use of application programming interfaces (APIs). Del Natale observed, “We recognised that by centralising and managing data in a consistent way, we were well-positioned to move towards intraday, or close to real-time data to improve the responsiveness of decision-making and create a real-time global cash position.”

As opportunities emerge for faster data exchange, it is tempting to think that because data can be obtained 24/7, it should be. Just as selecting the right data is required to drive automated processes and better decisions, so is choosing the right frequency. Most payments need to be executed before cut-off times, and unless activities such as cash pooling or FX hedging transactions are also executed incrementally during the course of a day, real-time or very frequent data exchange may not always be necessary. MNCs are likely to be more inclined towards real-time data, not least as they operate across timezones and locations, leading to 24/7 operations.

Mario Del Natale, Johnson Controls shared what this means for his treasury function, which operates on a 24-hour basis by passing the book to each region in turn. “By using a central Treasury Digital App based on Power BI¹ to provide a single source of the truth and consistent processes and controls. We need regular data exchange to meet local cut-off times in each market and manage our liquidity globally, so we do this every 30 minutes. Crucially, it is not just the frequency of transmission of data to and from our systems that matters, but also the degree of automation, to avoid having to initiate this manually,” he said.

Supply chain disruption and rapidly accelerating digitisation of business models during the pandemic forced treasury teams across all industries to think differently about how they worked with and supported the business. Treasurers were tasked to understand new cash flow dynamics, enable emerging liquidity requirements, boost supply chain resilience and support new customer and supplier models through integrated digital payments and collections.

“Changing the culture to think about data throughout the ecosystem represents a major shift”
Claudia Villasis-Wallraff, Head of Cash Management, Treasury Advisory, APAC, Deutsche Bank

Deutsche Bank’s Claudia Villasis-Wallraff, reflected, “Data is at the heart of digital business models, with stakeholders across the business from end-to-end relying on a single source of reliable data, whether for sales, supply chain management, procurement, logistics or treasury. Technically, this can be daunting but it is achievable; however, managing the organisational and people issues is just as challenging, as people – and data – have traditionally been organised in siloes. Changing the culture to think about data throughout the ecosystem represents a major shift, but is essential to ensure robust supply chains, meet customer expectations and manage liquidity and risk across the business.”

Could the data-driven data treasury, with optimised, automated processes, and algorithm-based decision-making mean that treasurers themselves become obsolete? Catherine Hill, Salesforce did not think so. “I could see a time when some simpler or low value transactions are conceived, executed and processed automatically – some of which already happens, but it is far less likely that treasury will operate entirely robotically, particularly when it comes to higher risk transactions. It also becomes more complex when operating with multiple banking partners that may have different algorithms, processes etc.” she said.

The reality is that most treasurers have more immediate concerns around data: achieving a global view of payables, receivables and bank balances, creating a reliable cash flow forecast, or understanding their true FX risk. Overcoming the challenges of disparate systems and data owners, fragmented and incomplete data, multiple formats is the priority before treasurers can consider the next step in what the data-driven treasury could be.

The webinar, Building a data-driven treasury from Economist Impact, sponsored by Deutsche Bank took place on 16 June 2022

Sources

¹ See powerbi.microsoft.com