Modern businesses are evolving at a rapid pace. Between the birth of revenue models like SaaS and Freemium, the rise of borderless distribution, and the creation of specialized point solutions, Revenue Accounting teams feel the disconnect between daily business needs and technological infrastructure.
It is the job of Finance Systems teams to address this disconnect. Finance Systems is responsible for modeling revenue datasets and delivering them to Revenue Accounting. As businesses grow, Finance Systems must address these gaps or they will face serious consequences like delayed business reports, inability to scale, and compliance obstacles.
Here are 3 most common issues that plague Finance Systems as they support Revenue Accounting.
Our financial systems cannot scale to support expected growth
Legacy infrastructure creates more complications beyond inflexibility – it forces Finance Systems to create a patchwork of systems. This patchwork compounds with infrastructural issues, creating new challenges such as:
- Applying updates is challenging since new configuration might break legacy logic
- Integrating new systems with the patchwork is tough since older systems might need custom solutions
- Upgrading infrastructure is tough since each portion of the patchwork demands specific upgrades
- New datasets such as new currencies, tax compliance rules, etc, cannot be integrated
The result is that every time the business opens a new unit, Finance Systems must work with Revenue Accounting to redefine processes and hire employees to execute them. Thus, growth comes at a high cost.
Some companies deal with this issue by assuming a continuous build mode. Finance Systems is tasked with updating and upgrading legacy systems constantly, leading to a bloated IT budget. This model strains IT teams since they’re forced to divert from the big picture and fight infrastructure fires instead.
Companies must adopt an agile approach to growth to sustain revenues. Legacy infrastructure, unfortunately, makes this an extremely challenging task.
I spend too much time reformatting revenue datasets from multiple systems
Most companies today have multiple revenue sources (payment processors, billing systems, order management systems, etc.) each producing data in different formats. For example, App Store and Google Play both provide revenue data in lump sum reports. Payment processors, in contrast, provide data in massive dumps that require considerable formatting.
Finance Systems must manually reformat these datasets to ensure compliance with data warehouse schemas, but they lack accounting knowledge to fully understand the data. On the other hand, Revenue Accounting lacks insights into data schemas but understand the application of the data. Because this task often involves input from both teams, the following can result:
- Wasted hours spent executing clerical tasks
- Errors introduced into systems due to manual processes
- Process bottlenecks when handling growing volumes of transaction data
I have a hard time delivering complete revenue datasets for accurate reporting
Legacy ERPs are the final destination for many revenue accounting datasets. However, these softwares were built to manage smaller revenue data volumes than what modern businesses currently possess. As a result, ERPs have struggled to keep pace.
In addition to being unable to manage modern revenue data volumes, infrastructural issues plague legacy ERPs. Finance Systems often cannot upload high volumes of data all at once into their ERP because of upload restrictions. Limits on data uploads create lags in revenue data storage and data silos. The result is that revenue data is only partially stored in the ERP, while the rest of it is stored in the revenue source’s database.
Data silos like these make analysis close to impossible, and Finance Systems face the tough task of delivering complete datasets. Typically, Excel can be a stop-gap measure. However, even this solution runs into issues with high data volumes, such as:
- Excel crashes are common once a company records over 10,000 transactions monthly. However, many companies struggle to use it beyond the 3,000 transaction mark.
- Managing multiple Excel files that store similar data.
- Sharing data and collaborating across multiple teams is impossible.
- Ad-hoc analysis is challenging, leading to lengthy report generation times.
While using Excel as a stopgap might work for small companies, it jeopardizes fast-growing and large enterprises, putting long-term growth at risk. CFOs need data-driven insights to project growth scenarios and fix budgets. Organizations cannot trust reports built from incomplete datasets and may resort to making decisions based on intuition instead of data.
Conclusion
Finance Systems face an abundance of issues while supporting Revenue Accounting. Finance Systems can either adopt a continuous build mode or leverage third party expertise by installing a financial data platform to combat these deficiencies.
The former approach puts a stress on resources and can increase the already unscalable patchwork of systems. In contrast, a third party solution allows Finance Systems to reset finance infrastructure and instantly upgrade their processes to industry best practices.
