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Data Issues: Why put them off another day?

by Jodee Hale-Schmid

December 14, 2010

Authored by Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.


Let’s get it right…right NOW. We can't afford problems later. You can fix your data problem…right now.

Leaving data issues outstanding during a data migration is not a good idea. It is very rare that anyone will have the time to fix problems after the new system goes live. People have enough to do learning a new system and sometimes completely new processes to also be trying to clean up poor data.

Many times when the data conversion stream is not given the attention it deserves during an ERP implementation the data is just forced through at the last minute and only just enough effort is put into ensure it loads but not necessarily ensure it is business ready.

One of the issues with this is that with modern ERPs (like SAP) once you get poor data in the system and start using it, it is very difficult to change it. For instance if you load duplicate vendors and then raise POs for them it is difficult (though not impossible :)) to remove the duplicate vendors once POs have been created. Similarly if the address on the vendor master i s incorrect then even after you update the vendor master with the correct address any existing POs need to have the address updated individually – which could be a lot of work. Better to load the right data upfront than try to clean it up afterwards.

So how can we do that? From my experience the most effective way is to ensure there is a team dedicated to ensuring business ready data in prepared for loading during the ERP implementation. This requires the right combination of people, process and technology. The people need to be data experts, with knowledge of ERP systems and the data they use, the process needs to be specific for data migration and conversion and the technology needs to be geared towards data ETL and also improving data quality. It is of little use to simply move data from one place to another and not improve its quality. The users expect the new system to be better than the old (hopefully :)) and that includes the data that they see in the new system.

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