for the forecasting process to be effective, internal data should be multiple choice highly aggregated. highly disaggregated. sampled once a year. none of the options are correct. kept on an annual basis.

Predicting the future based on historical and current facts is the forecasting process. The most popular method for doing this is trend analysis. It is typically ideal to use internal data that is significantly disaggregated for forecasting to be successful. This means that rather than aggregating or summarizing the data at a high level, it should be broken down into as much detail as feasible.

Since highly disaggregated data presents a more complete and precise picture of the variables that may impact future events, forecasting can be done more accurately and dependably. This can include information about previous sales, manufacturing rates, inventory levels, costs, and other elements that can be important for forecasting. Whereas, using highly aggregated data can lead to less accurate and reliable forecasts, as it may not capture the full range of factors that may influence future outcomes.