Although there are some substantial differences between Financial Modelling and Predictive Analytics, both help us cope with uncertainties and make better decisions.

Inside this article are the bread and butter ratios of statement analysis. Some gauge effective use of assets. Some report the financial condition of the company. It is with these the ratios (relationships) where it all begins. What I am outlying here the very core of corporate finance statement analysis.

Considering forecasting as an exercise to assess future financial performance as accurately as possible through a bottom-up approach based on actual facts, it appears necessary for an Organisation to become conscious of its own culture.

As most forecasting methods require data, a forecaster analyzes the availability of data from both external and internal sources. The availability of external data is improving rapidly. With the explosion of Internet websites, potential sources of valuable data are becoming limitless. With unstructured data, the need for data mining tools has become a necessity for exploring potential sources of data for consumer analyses and predictive modelling purposes.

In an uncertain and fast-changing world, line managers need to be made aware of the uncertainties and risk inherent in the financial forecasts provided to them. Uncertainty is difficult to manage but uncertainties can be converted into known risk as forecasting capabilities and data management improve.

Planners and managers in supply chain organizations are accustomed to using the Mean Absolute Percentage Error (MAPE) as their best (and sometimes only) answer to measuring forecast accuracy. It is so ubiquitous that it is hardly questioned.