Image by reynermedia, CC license
Finance and accounting functions were among the first to be automated through software. The sheer volume of numbers and calculations, reporting requirements, tax filings and payroll mechanics, plus the fact that nearly every business has to engage in these activities, made the area perfect for software.
When just these basic functions are needed, not much distinguishes one finance application from another. They all post transactions to a cost center and sub ledger account, they all capture sales and costs and calculate required P&L and balance sheet data, and they all provide reports. They might distinguish themselves in terms of ease of use or report writing, or banking account integration, or cash management, or some other aspect.
Many finance applications are simply bookkeeping systems; if you want real analysis you’ll need to extract data to Excel, Business Objects, or another analysis and reporting tool. My own experience with both Oracle and SAP bears this out: even these leading finance packages are mostly concerned with accounting and financial, not management reporting.
Oracle and SAP both have what they call “business intelligence” capabilities, but they are contained in separate modules that must be purchased and integrated with the core software. So companies can easily spend millions implementing SAP or Oracle, and still find themselves extracting data into Excel spreadsheets for basic business analysis.
My experience is that most finance applications lack budgeting and financial modeling capabilities. It is one thing to know that your prior month results were over budget because of rising fuel prices, and quite another to project the future profit impact of different oil price scenarios. At what point would it make sense to switch to alternative fuels, to pass on some of these increased costs, or to buy oil futures as a hedge? A typical finance application won’t help you to answer these questions because they mostly record and categorize costs based on what already happened, not what might happen in the future.
Yes, there are “what if” modeling applications available on the market, but as a stand-alone application they aren’t very useful, since you have to enter all of your data, as if you’re using an Excel spreadsheet. The modeling application needs integration with your ERP to be most effective. Your ERP is the source of all kinds of data needed for financial modeling: production costs, formulas, material costs, transportation costs, revenue by product, as well as cost standards and budget information. This data changes frequently based on business conditions, competition, labor costs, and many other factors.
Microstrategy, Oracle Hyperion and Cognos are leading names in the financial modeling and analytics areas, but other, smaller firms are emerging. Netsuite, the ERP-in-the-cloud vendor, offers an add-on financial modeling application. Netsuite’s web site states that the modeling application features these capabilities:
• Dynamic formulas and assumptions
• “Actuals” data incorporated into new forecasts
• Workflow management
• Planning of full financial statements
• Unlimited versions for “what-if” analysis
• Multi-dimensional models for complex sales and product planning
• Multiple currency budgeting
• Graphic drag-and-drop report builder
• Multi-version variance reporting (vs. budget, vs. plan, vs. forecast)
A3 Solutions is another, smaller firm offering financial modeling applications, either on-premise or as Software-as-a-Service. A3 uses the Excel spreadsheet as the user interface, claiming it is the friendliest environment for creating what-if scenarios, and provides tools to link multiple sources of corporate data and manage modeling versions dynamically and virtually through its Spreadsheet Automation Server. A3 claims McDonalds, Honda, Toyota, T. Rowe Price, and American Airlines as clients. Simplicity, speed of implementation, and low cost are A3’s main selling points.
Once you have the “system of record” stabilized in a strong finance application, as well as good controls over product, customer, and sales data, you can start to think about these higher-level analytical tools. Define a standard model for delivering analytics, put someone in charge of the data, and tightly control the “official” analyses that are produced.