The UN estimates that internet of things data flows across connected devices will create productivity benefits for businesses worth US$3.7trn by 2025. If treasurers are to harness this force, they must move from data silos to a discipline of enriched data that illuminates workflows, automates operations and informs strategy.
The growing availability of data promises to transform businesses. The vision of a ‘data lake,’ powering better decision-making with actionable insights, is a tempting one. But without a robust framework for connecting and clarifying inputs, more data can simply muddy the waters.The sheer quantity of data in most businesses can be overwhelming, creating less of a data lake and more of a swamp – murky and hard to navigate.
Cash flow forecasting is a prime example. Without a sharp awareness of data and workflows that ensure robust underlying processes to “feed the forecast,” the usefulness and reliability of new technology are often hampered.
Without meaningful data insights, customer payment behaviours may go unnoticed and unnecessarily trigger held orders and collection strategy techniques, both of which reduce forecast accuracy.
Swamp-like data also inhibits the understanding and application of advanced data science, like artificial intelligence, that is transforming treasury functions. Gartner consultants estimate that 85% of artificial intelligence projects will produce errors due to improperly managed processes that foster swamp-like data.
The issue of data quality is not just about rectifying bad data but about identifying useful data.The first step in this process is to engage with stakeholders to evaluate which key performance indicators matter most to the business and drive collaborative outcomes.
The critical, and often overlooked, starting point for moving from data distraction to tangible application is creating a common understanding of what specifically adds value. Achieving this with the high volumes of data generated by multinational corporations requires blocking out data noise and refining data sets from source systems outside the organisation.
In cash flow forecasting, a disciplined process reflected in data and technology infrastructure helps to filter the blips caused by limited information, best estimates and general business volatility.
Bank transactional data, for example, can act as a clarifying agent, either refining existing data to improve its application or uncovering process inefficiencies that are outside of the scope of analysis of traditional treasury systems.
A case in point is FX, where the addition of transactional bank data can clarify swamp-like foreign currency payment processes. The FX capabilities of platforms such as Bank of America’s CashPro® can use bank data to automate trades and deliver much greater efficiency.
As the value of data increases, many of these use cases will find their commercial footing. In the near future, as treasurers work to structure and clarify their data, tools and applications, the opportunity for easily available bank transactional data to accelerate the process cannot be overlooked.
