The continuous improvement process I described in last month's column provides a way to proactively manage data quality. Proactive data quality management begins with a plan. In the case of operational systems, that would apply analysis and design activities to develop operational systems that provide quality data and measure the actual quality. In the next step, do, the systems are executed and used in the operational environment to support transaction processing. The output of the do step is the data that resides in the operational data stores, which is captured for use in the data warehouse. The challenge often facing data warehouse teams is that the quality of the actual data is unknown. The warehouse team is then forced to enter the continuous improvement process at the check step to perform data profiling.
Data profiling consists of investigating data being captured for the data warehouse so that its quality (including true meaning) is understood. This is a critical data warehouse development step, and temptations to skip it should be avoided.
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