In last month's column, I discussed some common data integration mistakes. This month I point out where companies tend to go wrong in their initial use of extract, transform and load (ETL).
1. Turning a blind eye to data quality measures and metrics in ETL processing. I see it all the time when reviewing an enterprise's data integration architecture: data quality processing that's a tangled mess or totally nonexistent. Why is this common? People either fail to do data profiling to understand the true extent of data quality issues in the source systems, or they assume any data quality issues are the sources' problem, not theirs.
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