Improving data management is at the forefront of my Office of Finance research practice because, when not managed well, it can have a profoundly negative impact on departmental efficiency, individual performance, organizational agility and sustainability. Conversely, straight-through processing (STP), a business process design and data management methodology, reduces process friction throughout an organization and, by minimizing errors, builds trust in the accuracy and reliability of all data, including that used in analysis, accounting, billing and performance management.
A decade ago, I codified the key elements of what I call “continuous accounting,” a finance department management mindset that uses technology and the more productive and flexible process execution it permits to increase both accounting efficiency and finance department effectiveness. A central principle of this approach is managing the flow of data continuously through a process from end to end without human intervention. Doing so ensures data integrity, which all but eliminates the need for accountants to check and reconcile data for errors, a low-value, time-consuming task.
Especially as enterprises increase focus on improving data management to support artificial intelligence and Generative AI initiatives, finance and accounting will increasingly adopt straight-through processing. By 2027,
The value of managing data continuously in an end-to-end approach applies throughout the enterprise. Technology advances have made straight-through processes increasingly feasible for any business procedure. This allows organizations to design and execute them from inception to completion in a more automated fashion, minimizing or eliminating human intervention in extracting, moving and, if necessary, transforming data as it moves from one system to the next. This automation accelerates process execution and ensures the data used in the final step of the process is reliably the same as at the point of original entry.
The term straight-through processing originated decades ago in financial services, where there’s a need to automate trading to process high volumes of orders rapidly, accurately and efficiently. Financial services businesses were able to implement STP first because of information technology adopted earlier and more thoroughly than other industries.
Straight-through processing gained early traction in business-to-consumer commerce. Internet retailing processes are designed from the ground up to require as little human intervention as possible; Amazon is a role model in this regard. It relies heavily on information technology to (among a long list of things) manage a massive catalog of products, make personalized suggestions to buyers, organize product and vendor reviews from customers, process orders, handle logistics and process returns. STP orchestrates business procedures to eliminate friction in the buying process.
Straight-through processing provides a systems design framework that’s also useful for any form of business and commerce. It not only enables companies to redesign processes to improve efficiency and lower costs but also supports a more pleasurable buying experience. The data gathered about customer interactions with the system provides deeper insight into customer behavior and improves an organization’s agility. This data also can be used for a range of purposes including business and financial analytics applied to performance management and planning as well as machine learning.
In straight-through processing, transaction hand-offs are managed exclusively by IT systems and all data is entered only once. Only the system makes calculations and data transformations, using technologies such as application programming interfaces and robotic process automation to take humans out of the loop. While there may be multiple systems of record involved in a process (for example, a customer relationship management and enterprise resource planning system), there is only one authoritative system of record for any specific piece of data. At no point do users manually download numbers from one system and upload them into another.
Compared to working with legacy software, it’s easier to build an enterprise’s IT and applications infrastructure as a greenfield implementation. However, even companies with legacy systems will find that it’s straightforward to implement an STP approach. Connecting systems through APIs and integration services requires much less maintenance than older, point-to-point integration. Robotic process automation has also evolved as a means of connecting data and processes to support STP. And cloud-to-cloud integration can be easier than on-premises systems because multi-tenant services have standard architectures and data models.
STP is especially useful when an end-to-end business process involves multiple systems, particularly when it includes multiple business units or departments. For example, the sale of some industrial machinery begins as an opportunity in a customer relationship management system. As the process progresses, a formal proposal is prepared using a configure, price and quote system. When the proposal is accepted, a contract management system might create the contract. When the contract is signed and accepted, the enterprise resource planning system handles the inventory, supply chain, production and logistics of delivering the product to the customer as well as the billing and accounting aspects of the transaction. STP uses technology to orchestrate the process—including system-to-system handoffs—from beginning to end. STP also simultaneously manages the movement of the data from system to system, ensuring that it’s consistent.
Recurring revenue and subscription businesses find STP especially beneficial because these customer relationships are more dynamic. That is, the number of subscribers, the terms of the subscription and the quantities purchased (to name three aspects) are usually in flux. This makes a straight connection of the process and data—from taking an order to delivery and billing—useful.
Straight-through processing addresses several issues. Eliminating spreadsheets that may be used for data transfers, extract, transform and load processes or data entry substantially reduces the likelihood of errors. Our Spreadsheets in Today’s Enterprise Benchmark Research finds that more than one-third (35%) of participants said the most important spreadsheet they use has data errors. A further 26 percent cited errors in formulas. STP also accelerates the completion of a process because it automates handoffs between individuals: The same research shows that almost half (45%) of companies reported frequent or constant processing delays because people forget to forward the spreadsheet or don’t know what to do next.
Ensuring data integrity throughout a process reduces errors that create additional work. Especially for finance and accounting departments, ensuring that the back-office data used in records, billing and analyses is accurate and always consistent with the data in front-office and operational systems eliminates the need for checks and reconciliations. STP increases customer satisfaction in that an organization can reduce errors in orders and reliably generate accurate bills.
Chief financial officers looking for ways to gain productivity should prioritize finding use cases for the applications of straight-through processing. STP streamlines business process execution from the front office through operations to the back office. It has the potential to make it easier to serve and satisfy customers, create and deliver products and services and perform the related accounting and billing. It can shorten cycle times, and, by substantially reducing errors, increase productivity.
Regards,
Robert Kugel