By Mike Morini
There really is a “right tool” for every challenge. Some tasks cannot or should not even be attempted without the right tool. All of us have experiences where the solution for a basic task or process first became possible, then expanded in depth and breadth, and ultimately made previously difficult and complex functions easy and straightforward. This evolution is typically the product of shared knowledge leading to greater focus and subject matter expertise, combined with ever-increasing enablement from advances in technology.
Yes, there are the occasional examples where the latest incarnation of a solution is really not as good as the previous instance, and declarations of “They don’t make them like they use to!” can be heard. But these are exceptions to the rule, and these events are relatively brief aberrations. Wherever there is an unmet need for the “right tool”, a best solution will ultimately appear. This is true of most business processes, and especially applicable to a well-designed system which automates the enterprise recurring revenue recognition process.
Recurring revenue business models drive high volumes of transactions across a variety of service types, producing well-defined and highly predictable revenue streams. The best revenue recognition solution automates a sequence of steps to acquire all of the relevant data from one or more source systems, transforms the data into revenue recognition information, and then places the information into persistent repositories. The combined challenges of volume, variety, complexity, and integrity inherent in any recurring revenue recognition process demand an automated solution which consistently delivers timely and accurate content in accounting entries, audit controls, and reporting.
Automation starts with the preconfiguration of source system data types into service transaction keys and well-designed models to prescribe the desired accounting results. The configuration activity also encompasses the definition of the preferred process steps and flow as well as any personalization of the various screens and reports. Configuration settings must be tested for every expected use case in a “sandbox” and/or “staged” environment that emulates the anticipated production solution. The automated solution used in production should then substantially reduce the operational demands on the accounting team, significantly improve the integrity of revenue recognition content, and allow the appropriate focus on critical exceptions.
Of course, automation requires different skills from the accounting and finance team’s users. Just as the skills requirements changed when huge preprinted spreadsheets were replaced by electronic spreadsheets with powerful expression editors, the best revenue recognition solutions require the development of skills in use case definitions, process modeling, system configuration, application testing, exception management, revenue reporting, and analysis. There should be reports for audit details, ledger detail and summary balances. Really good solutions will provide automated balance reconciliation solutions for all of the real accounts (assets, liabilities, equity), and encompassing the effects of related nominal (P&L) accounts. The very best ones provide forecast functionality for a wide range of uses in predictive, prescriptive, and optimization revenue analyses.
The automation of the recurring revenue process allows the accounting and finance team to apply their expertise through well-designed technology applications and thereby provide the greatest possible value to their enterprise: relevant, timely, flexible, accurate, compliant, and complete revenue information, and the ability to quickly recognize and resolve problems when they occur. When there is integrity in the revenue recognition solution and the value of the team’s contribution is optimized, this is a very good thing indeed!
If you haven’t yet read the Aria Expert Series whitepaper, Key Requirements of Recurring Revenue Recognition, click here to download, and give us feedback after you’ve had a chance to review it. We’d love to hear your comments on the whitepaper or this subject in general.