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.

About the only thing that everyone seemed to agree on in my old company was that forecasting was really important and that our forecasts were poor. I looked in the corporate controller’s database for a definition of what constitutes a ‘good forecast’. But I got zero hits.

As far as I know, we are not legally required to forecast. So why do we do it? My sense is that forecasting practitioners rarely stop to ask themselves this question. This might be because they are so focussed on techniques and processes.

The gold rush is a defining part of Silicon Valley. The gold of today is data, and many solutions are rushed to the world market from a small radius around Princeton University. On the other side of the Bay lies the University of California, Berkeley, a place of the Liberal Arts in contrast to the technology-driven Princeton.