The relationship between the forecasted and dependent variable and other influencer or independent variables changes over a period of time. For example, most forecasting models weren't able to predict the global economic crash of 2008. Post-crash, modelers in a leading bank tried to rebuild their models with new variables that would be able to predict behavior better. Some of these new influencer variables weren't available in the central database. A vendor was selected to provide history and a continuing data feed to enable the availability of such variables in the future. In this case, since the influencer variables changed, the modeler had to look outside the scope of available variables in the central database and try to find better fitting variables.