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September 14, 2010

Data Structure Mismatch during Data Migration Process

Filed under: Data Integration, Data Migration — Katherine Vasilega @ 4:11 am

It’s no secret that one can face serious challenges during the data migration process. Today, I’m going to tell you about one of the most common issues that may complicate the process of data migration or data integration, as the solution may require certain efforts from the side of your technical staff. I’m talking about the mismatch in the ways the data is structured in various systems, such as CRMs, ERP, DBs, and others.

It is often the case that the source system contains different data in one table (e.g., product name, price, description) that has to be migrated to different tables in the target. On the contrary, the source may have different tables (customer name and phone number) for the data that has to be aggregated in one table. Such mismatches are also common for the field names, data formats, and table connections.

For example, the popular CRM systems, such as Salesforce.com CRM and SugarCRM, store customer’s name and email in different tables. If you strive for efficient customer management, it’s a good idea to keep this information in one place – so you can view and use your customers’ names and their emails at once. In this case, data migration from Salesforce.com CRM and SugarCRM to the target is going to be really time-consuming, unless you connect the different data using an ETL tool. ETL tools provide fast and easy data migration of different types, aggregating unconnected fields into one, or separating one field into several. Data migration with ETL improves your customer relationship management and internal operations, which helps make quality business decisions.

However, a company that is about to start data migration from one system to another for the sake of better customer management should always keep in mind the possible mismatches in how date is structured in these systems. So when it comes to the choice of an ETL tool, ensure that the tool provides for these mismatches and can cope with them. Otherwise, data migration may take much more time and resources than it was initially planned.

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