Published: October 14, 2019
There’s something delicious about an exclusive interview – especially when it involves Ole Matthiessen, Global Head of Cash Management, Deutsche Bank, candidly discussing the eyebrow-raising results of the bank’s latest corporate treasury survey. This year’s report examines the technologies enabling treasurers to mine for ‘data gold’, as well as the hurdles facing treasury functions as they wrangle more data and aim to deliver greater insights to the business.
Ole Matthiessen, Deutsche Bank (OM): I’m really excited to be able to share highlights of this year’s survey with you. The title of this edition ‘A Quantum Leap: Building a data-driven treasury’ reflects the fact that data is quickly being recognised as a corporate asset. As digitisation sweeps through businesses, the growing volume and depth of financial data available presents a strategic opportunity for the treasury function. Using this data wisely can help treasurers to generate timely cash and liquidity insights, and facilitate faster treasury decision making.
Simply ‘owning’ data is not enough, however; digital transformation is required in order to extract, aggregate, and analyse good quality data. As such, the journey towards data-driven treasury takes time. The survey results therefore enable treasurers to identify how far along they are on that journey, depending on the organisation’s size, complexity, and treasury team structure, for instance.
Bringing together responses from over 300 corporate treasurers across the globe, the report is structured in three sections:
1. The past – looking at the digitisation that has already taken place and the opportunities treasurers have harnessed in order to become more data driven
2. The present – focusing on the priorities when working on a data-driven strategy
3. The future – exploring emerging technologies to further treasury’s data intelligence
OM: Using data analytics to drive treasury decisions helps to reinforce treasury’s role as a profit centre – according to the survey principally through higher operational efficiency (39%) and improved return on investments (36%). Being a data-driven treasury function also has value when it comes to navigating complex regulations such as IFRS 9 and GDPR.
OM: Treasurers are using technologies such as big data analytics (45%), Treasury Management Systems (TMS) (43%) and cloud-based applications (42%) to manage their data. This marks a significant shift from our survey last year, where cloud technologies ranked close to the bottom (chosen by 30% of respondents in 2018 versus 42% in 2019).
Naturally, the extent of technology adoption varies significantly among treasury departments. And unsurprisingly, many continue to rely extensively on spreadsheets for core treasury functions. But the transition is underway from static technology tools to more automated systems, which is contributing to greater awareness of the value of data.
OM: It’s a good question because cloud solutions for treasury first materialised just over a decade ago, when TMS vendors began to offer software-as-a-service (SaaS).
It has taken some time for risk-averse treasurers to accept the security and robustness of cloud-based solutions and I think that initiatives such as the Statement on Auditing Standards (SAS) 70, which scrutinises the data security and control of cloud providers, have led to a wider acceptance of more advanced analytical systems among treasurers. We’re also another 12 months into the cloud phenomenon now and treasurers feel generally more comfortable with the concept as a result of the passage of time, and more of their peers adopting such solutions.
OM: The secret to becoming a truly data-driven treasury is crafting a data strategy upfront. Over half of treasury professionals (53%) surveyed already have a well-defined data strategy, but the remaining 46% say that their data strategy is only ‘somewhat’ well defined, suggesting that there is still a way to go in formalising this policy.
Creating a data strategy requires aggregating and visualising the data available, assessing data quality, and identifying specific treasury responsibilities that can be enhanced through data analytics. To give an idea on the latter, respondents suggested that those functions most in need of data assistance are investments (29%), cash flow forecasting (25%) and exposure identification and measurement (24%).
OM: Quality of data underpins the effectiveness of any data strategy.
Four in 10 survey respondents were very concerned about the poor quality of financial and other business data in their organisations. A further 27% were somewhat concerned and 30% were slightly concerned.
The survey also showed that those who are involved in compiling the data and/or looking closely at data-driven approaches have more serious trust issues about the quality of the data than others using the data.
Interestingly, unstructured data appears to be preferable to treasurers when it comes to improving the quality of their analysis. Pooled data from multiple ERP systems (among others) may be standardised to use across different parts of the business; such standardisation can counteract the data-driven treasury approach since key nuances in the data may be lost or hidden.
OM: You’re right that issues with the quality of the data may stem from the team’s culture and skillset. But finding treasury staff that understand the technologies required for a data-driven strategy can prove challenging. Training treasurers in the principles of data analytics is therefore important, so that they can communicate more effectively with IT about treasury data requirements.
OM: It’s clear from the survey results that technologies for aggregating data and those with advanced processing capability will be key to raising a treasury’s data intelligence. Respondents stated that the most important technology in the next five years will be cloud computing (44%), closely followed by big data analytics (42%) and then artificial intelligence (AI) (37%).
According to the findings, big data analytics and AI will be applicable across a broad range of treasury functions, in particular working capital management, inventory management, and operational risk management. These echo responses to other questions, which prioritised cash flow forecasting and driving operational efficiency as essential components of a data-driven treasury.
OM: Despite the hype around robotic process automation (RPA), only 9% of treasurers say that RPA technologies are important in improving their company’s data intelligence. Although RPA is helpful in automating repetitive treasury tasks, it does not deliver the insights that treasurers are looking for from a data-driven strategy.
Application programming interfaces (APIs) were met with similar levels of enthusiasm – just 10% of treasurers surveyed believe these will be important for the company’s data intelligence in the next five years. What’s clear is that although using APIs can boost connectivity between systems and organisations, the role of APIs in enhancing data intelligence has yet to be fully recognised.
Distributed ledger technology (DLT) or blockchain also ranks lower down on the list of priority technologies for the next five years – with conflicting views over its usefulness, especially within the treasury, rather than trade, environment.
OM: Treasurers are now in execution mode when it comes to building a data-driven treasury – but, as we discussed earlier, it is important to establish a solid data strategy as a foundation layer. The report highlights five top tips for achieving this:
Download the complete survey results at db.com/cm to learn more about the challenges and opportunities in building a data-driven treasury.
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