Standard Definition, Data cleansing is process of ensuring that a set of data is correct and accurate.
Data records are checked for accuracy and consistency, and they are either corrected or deleted as necessary.
The goal of data cleansing is to minimize data errors and to make the data as useful and as meaningful as possible.
In APO-DP, History data should be as 100% accurate to get fair Stat Fcst. Whar are the options available in APO for Data Cleansing. It can be used for Adjusting actual Sales/Shipment Hist data for Past Promotions, Outlier correction, Manual adjustment etc
Appreciate if you share your experience/inputs in this regard.
Data records are checked for accuracy and consistency
There is very little anyone here can contribute to your company's policies and procedures. The task to improve accuracy is dependent upon what data you are collecting. This is different in every company. In general, you compare the source records with the records found in DP, determine the discrepancies, and then correct as necessary. For this you may have to develop custom reports.
In APO-DP, History data should be as 100% accurate to get fair Stat Fcst.
Well, I suppose this is true if you have unlimited resources available. I have never worked in such a company. In most companies the historical data need only be accurate enough to produce an acceptable stat forecast. Consider that even under the best of circumstances, say, if your historical data were to be 'perfectly accurate', your stat forecast is still just a forecast, meaning it will always be a poor match with the actual future events. It makes no sense to spend resources improving your data accuracy of historical data up to, say, 99%, when the ultimate forecast can never be more than, say, 75% accurate.
The main issues that should be considered is to eliminate irrelevant, obsolete or misleading data from consideration. You should have a process to eliminate or adjust forecasts for discontinued characteristics. You should have a process to eliminate historical key figure data that are no longer timely (e.g. too far in the past).
It can be used for Adjusting actual Sales/Shipment Hist data for Past Promotions, Outlier correction, Manual adjustment etc
??? Data cleansing takes place before these tasks are done. These items are part of the forecasting business process cycle, and not part of data cleansing. For these tasks, you start with the historical, and then make business decisions as to how to process the historical into a usable forecast.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.