Looking to the future, we can never predict with certainty how events will turn out. We can, though, forecast the direction of travel where there are established trends and act to take advantage of opportunities as they arise.

Developing a robust data strategy has become increasingly important for treasury organizations as they seek to take advantage of opportunities to build actionable forecasting and planning tools.

A data strategy comprises the following components:

• The determination of the system of record. This is the record of the company’s master data and will vary according to the organization. Options include the bank (because they maintain the data related to the company’s bank accounts) or the general ledger (because this will reflect adjustments to the bank account data to account for factors known to the company, such as for inaccurate payment data).
• The available data. Treasurers need to consider the right data points for their current requirements and where they can source that data. The key is to answer the question, what do we need from our data? Most will focus on improving cash visibility and forecasting and identifying risk exposures.
• The data collation process. There are many different ways to collate data from external sources, from APIs to direct bank feeds via a portal or host-to-host solution. This will be determined, in part, by the complexity of the company’s bank account structure, and whether it partners with multiple banks and/or has a presence in many jurisdictions.
• The platform being used to analyze the data. Companies will vary in their approach from a simple rolling forecast on a standard spreadsheet to a highly sophisticated system incorporating AI to identify payment trends and forecast future sales.
• The control of data. Companies need to understand the impact of any process automation on the company’s control of underlying data. Without access to process data, treasurers may limit their ability to identify trends and develop forecasts.

Data analytics only has value if it results in improved decision-making. It offers the opportunity to forecast and plan cash and liquidity positions more accurately and the prospect of providing decision-makers with access to timely and robust data that they can rely on.

A data strategy requires a constant investment of time and resources to create more accurate forecasts. Methodology and data sources need to be updated as data patterns and relationships are identified. Central to any improvement is variance analysis, the identification of differences between forecast and actual data, and then the attempt to understand any adjustments. In some cases, the adjustments will be a one-off event; in others, they may be a reflection of a trend or a flaw in an underlying assumption.

But treasurers must also recognize that, for all the investment in data analytics, their own personal experiences and judgement remain important when making decisions, especially in times of crisis.