A little refinement goes a long way: new thinking and new tools can refine the budgeting and forecasting process to make it more productive and more insightful.

• Author: Houle, Chris
• Position: BUDGETING/FORECASTING

As in any profession, it's easy for financial executives to get into a rut. Yet, in order to provide the true insight needed to boost business performance, it's important to break out of old habits in budgeting and forecasting. Too many CFOs rely on rote procedures by plugging new numbers into existing templates without stopping to consider: will this model answer my fundamental business questions correctly?

Answering this vital question certainly will improve the performance of any business, and will enable CFOs to fulfill their true purpose as the leading executives responsible for financial decision-making within an organization. Financial executives should take a step back and consider whether their budgets and forecasts provide true business insight and support informed decision-making.

Developing a budget or forecast can be a routine exercise or an inspirational process. At its best, a forecast enables financial managers to provide an accurate and timely framework for business insight and improvement. At its most common, it is a historical document tracking monies spent and earned with a nod toward anticipated changes.

At its worst, however, a budget is a stagnant document in which errors and misassumptions are unquestioned and allowed to go forward. The leap from the ordinary to the outstanding can be hampered both by processes and the toolsets used. A responsible financial executive will execute control over both of these factors.

The Process

Financial executives have wrestled for years with the process of developing budgets and forecasts, and just about every "expert" has a favored methodology. One useful approach earning favor with the international financial community in the area of data mining is CRISP-DM, or the Cross-Industry Standard Process for Data Mining. Developed in the late 1990s by data-mining pioneers at DaimlerChrysler, SPSS and NCR, CRISP-DM is an industry- and tool-neutral data mining process model methodology that makes large data mining projects faster, cheaper, more reliable and more manageable.

A slight modification of CRISP-DM offers a solid approach for developing a business modeling and analytic process for developing reliable and insightful forecasts and budgets:

* Understand the Business -- The first and most important step in developing a budgeting or forecasting model is to understand just what business questions the model should answer. They can be as simple as, "how does my company make money?" or more...