In last month's column, I discussed some common data architecture mistakes. This month, I point out areas where companies tend to go wrong in data integration or extract, transform and load (ETL) processing.

1. Not developing an overall architecture and workflow. The usual development approach for data integration is to gather the data requirements, determine what data is needed from source systems, create the target databases such as a data warehouse and then code. This is an incomplete, bottom-up approach. It needs to be coupled with a top-down approach that emphasizes an overall data integration architecture and workflow.

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