BPMS vs. GL Systems
While the general ledger (GL) system may have a budgeting function, BPMS excel over GL systems in that they can capture non-financial data and also usually have a lot of flexibility in terms of reporting formats and querying (both standardized and ad-hoc). The technology they use (sometimes called OLAP or data cubes) is also superior and much more efficient in querying compared to the GL—allowing reports to be more flexible and generated must faster. This is due to a fundamental difference in their purposes: the GL is designed to record transactions, whereas BPMS are designed to support hierarchies and perform computations quickly—just think of them as spreadsheets on mega-steroids
The need to capture actuals is also an ability that BPMS helps with. Instead of having to re-key or re-link actuals from GL reports into your budget reports, many of the software either leverage technology tools to import data from the GL or multiple GLs if you have multiple entities using different systems, as well as data from operational systems to support assumption or business driver analysis.
Budgeting Best Practices
For example, in a service business, rather than starting the budget with revenues, revenue is basically hours sold multiplied by an hourly rate. Hours sold is actually a factor of the number of employees, the “billable” full-time equivalent of those employees, and the number of labor hours available in a given period. The hourly rate may also vary by customer (e.g. a large customer gets preferred rates), market segment (e.g. different market segments have different pricing or discounts), and staff-level (e.g. partner bill rate vs. senior bill rate). All of the statistically significant assumptions or drivers should be captured.
By doing this, variance analysis can focus on not only dollar variance, but also variances in base level assumptions, which may provider greater insight into the “why” of a dollar variance. This also helps to validate assumptions/business driver models, identify the need to revisit assumptions, and enable trend analysis for better forecasting of assumptions or identify refinements to a business driver model.