I can’t tell you how many times when our company starts talking with a client about a data integration or other data management project that before we get started with the main purpose of the project the client first thinks that they have to “clean-up” the data.
If your organization has multiple data silos and has a significant amount of data records, a data clean-up effort could be a massive project. Most likely, your organization will never get around to cleaning up the data, and if does, its going to be a one-time event that only provides clean data for a moment in time. Therefore, if you are waiting for the day that you have perfectly clean data before solving your other data issues, that day will never come.
Instead, we suggest that your organization develops a Data Filtration System. A Data Filtration system is a process where you can clean-up data on an on-going and systematic basis. The system should match and link same and related organization and contact information from disparate data sources as well as within single data sources so that you can have a single view of your customers, prospects, and other organizations.
With this single customer view, the data should be sufficient so that a person can make a determination on how to handle a group of records that have been matched and linked by your numbering schema. Sometimes, a best record can be made from the group and the remaining duplicates can be purged. Other times, however, records that at first appear to be duplicates are in fact records from the same organization but are separate sites. Instead of merging and purging these records, these records should be kept separate but correlated by your numbering system. There are other times when some records in your group need to be merged and purged while other records needs to be correlated. Whatever the scenario, the system should be set-up so that these “Solomon” like decisions can be made.
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