The most familiar business performance management (BPM) program for a CFO to implement is budgeting. Many organizations already have a set of spreadsheets that are used to support the budgeting and reporting process. These spreadsheets are often passed around by e-mail or via the file server and can sometimes get changed to the point where you need to spend hours relinking all of them together once the budgeting process is done.
BPMS vs. GL Systems
Most BPM systems (BPMS) are focused to address this specific need and related/similar activities (e.g. financial reporting, managerial reporting, and consolidation). For familiarity, BPMS allow these spreadsheets to be leveraged and the workflow process to be automated, and they put the power of a database server behind them to store the data and automatically route budgets and reports.
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.
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
However, a budgeting initiative is more than just automating your spreadsheets. Budgets following best practices contain not only financial items, but also the baseline business drivers and assumptions that are used to derive the financial statements.
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.
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.
Summary
So before you start trying to automate the spreadsheets you already have, first take a look at your current budgeting process and make sure that you are capturing all of the data that you need to really analyze the relationship between your assumptions, business drivers, and financial performance. A well led budgeting initiative can not only help to change your data into information, but it can also help to drive the transformation of information into INSIGHT.
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