Data quality management needs to be an integral part of the design and planning of your corporate performance management (CPM), business intelligence (BI) or data warehouse project -- as I discussed in my previous column, "Data quality management: Follow the doctor's orders."
You need to gather the business requirements and priorities for data quality; determine the appropriate business logic to handle the various data quality conditions you encounter; incorporate data quality processing throughout your data lifecycle from data sourcing through information consumption; and regularly report on the data quality levels through dashboards.
The following five steps will help you weave data quality management into your enterprise data integration efforts.
1. Set a baseline for data quality management
First, determine and establish the baseline data quality level in your data source systems. Use data profiling software to analyze source systems for completeness and accuracy. Trying to perform data profiling manually, by coding queries on the source systems and comparing the answers to expected results, is laborious and rarely thorough. The good news is that you can find excellent data profiling software. The even better news is this functionality is increasingly being bundled with data integration software.
2. Verify the data -- and don't pass the buck!
Second, verify that the data extracted from source systems is the same data that was imported into your data warehouse. This seems obvious, but too often source system data is not as correct as people assume.
In cases like these, IT sometimes assumes it is the source system's problem. But if you move the data into your CPM or BI solutio...
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