Most Reporting Systems tend to focus on the positive,
sales targetted ,sales achieved, etc.
For quality reasons , it is important to measure variance or deviation from the mean. This can also be interpreted as measuring error for any planning, forecasting or performance measurement exercise.
It helps bring focus, by aiming to reduce the difference between planned/forecasted and actual performance by looking at the mean absolute percentage error. Also it is impportant to have estimates that increasingly move from qualitative metrics or scale variables to quantitative inputs for forecasting or planning.
The forecasts should be realistic based on data. The forecasted pleasure or displeasure of the boss/ end audience recieving the estimate tends to be the biggest practical input in planning exercises unfortunately.
That is why analytics is sometimes a corporate cultural thing as well.
1) Forecast honestly
2) Use as much quantitative inputs
3) Plan scenario analysis with varying probabilties
4) Measure error from forecast and actual in a feedback loop
5) Aim at reducing error.
What SIX SIGMA would call
.This applies to decision making and planning at most organizations where data /information ends up too many times in floating spreadsheets or gatekeepers/old timers.