A roadmap to becoming a data-driven organization
Everyone understands the importance of data in an organization. After all, data is the new oil in terms of its value to a corporate treasury and indeed the wider organization. However, not everyone is aware of how best to utilize data. This article will tell you.
Developing a data strategy depends on using the various types of payment, market, cashflow, bank and risk data available to a treasury, and then considering the time implications of past historical data, present and future models, to better inform decision-making. We provide a roadmap and ‘how to’ guide to becoming a data-driven organization.
Why does this aim matter? Well, in this age of digitization, almost every aspect of the business has a digital footprint. Some significantly more than the others. This presents a unique opportunity where potentially all information can be reliably processed to take tactical and strategic decisions from a position of knowledge. Good data can facilitate hedging, forecasting and other key corporate activities. Having said all that, care must also be taken to not drown in the data lake1 and become over-burdened with useless information. Take the example of Amazon in 2006 when it reported that cross-selling attributed for 35% of their revenue2. This strategy looked at data from shopping carts and recommended other items that may be of interest to the consumer. The uplift in sales was achieved only because Amazon made the best use of their data.
Treasury is no exception. It too can become data-driven thanks to its access to multiple functions and information flows. There are numerous ways to access and assess multiple sets of data (see Figure 1), thereby finding solutions to some of the perennial problems facing any organization that wants to mitigate or harness risk, study behavior, or optimize its finances and cashflow to better shape its future.
Time is money
The practical business use cases that can be realized by harnessing data in the Treasury often revolve around mastering the time function. Cash optimization, pooling for interest and so on often depend on a good understanding of time – even risk hedging strategies can depend on the seasons, for instance, if we’re talking about energy usage.
When we look at the same set of data from a time perspective, it can be used for three different purposes:
I. Understand the ‘The Past’ – to determine what transpired,
II. Ascertain ‘The Present’ situation,
III. Predict ‘The Future’ based on probable scenarios and business projections.
I – The Past
“Study the past if you would define the future”
The data in an organization is the undeniable proof of what transpired in the past. This fact makes it ideal to perform analysis through Key Performance Indicators (KPIs), perform statistical analysis on bank wallet distribution & fee costs, and it can also help to find the root cause of any irregularities in the payments arena. Harnessing historical data can also positively impact hedging strategies.
II – The Present
“The future depends on what we do in the present”
Data when analyzed in real-time can keep stakeholders updated and more importantly provide a substantial basis for taking better informed tactical decisions. Things like exposure, limits & exceptions management, intra-day cash visibility or near real-time insight/access to global cash positions all benefit, as does payment statuses which are particularly important for day-to-day treasury operations.
III – The Future
“The best way to predict the future is to create it.”
There are various areas where an organization would like to know how it would perform under changing conditions. Simulating outcomes and running future probable scenarios can help firms prepare better for the near and long-term future.
These forecast analyses broadly fall under two categories:
Historical data: assumes that history repeats itself. Predictive analytics on forecast models therefore deliver results.
Probabilistic modelling: this creates scenarios for the future based on the best available knowledge in the present.
Some of the more standard uses of forecasting capabilities include:
- risk scenarios analysis,
- sensitivity analysis,
- stress testing,
- analysis of tax implications on cash management structures across countries,
- & collateral management based on predictive cash forecasting, adjusted for different currencies.
Working capital forecasting is also relevant, but has typically been a complex process. The predication accuracy can be improved by analyzing historical trends and business projections of variables like receivables, liabilities, payments, collections, sales, and so on. These can feed the forecasting algorithms. In conjunction with analysis of cash requirements in each business through studying the trends in key variables like balances, intercompany payments and receipts, variance between forecasts and actuals, this approach can lead to more accurate working capital management.
How to become a data-driven organization
“Data is a precious thing and will last longer than the systems themselves.”
There can be many uses of data. Some may not be linked directly to the workings of the treasury or may not even have immediate tangible benefits, although they might in the future for comparative purposes. That is why data is like a gold mine that is waiting to be explored. However, accessing it and making it usable is a challenging proposition. It needs a roadmap.
The most important thing that can be done in the beginning is to perform a gap analysis of the data ecosystem in an organization and to develop a data strategy, which would embed importance of data into the organization’s culture. This would then act as a catalyst for treasury and organizational transformation to reach the target state of being data-driven.
The below roadmap offers a path to corporates that want to consistently make the best use of one of their most critical and under-appreciated resources – namely, data.
We have seen examples like Amazon and countless others where organizations have become data- driven and are reaping the benefits. The same can be said about some of the best treasury departments we at Zanders have interacted with. They are already creating substantial value by analyzing and making the optimum use of their digital footprint. The best part is that they are still on their journey to find better uses of data and have never stopped innovating.
The only thing that one should be asking now is: “Do we have opportunities to look at our digital footprint and create value (like Amazon did), and how soon can we act on it?”