Recently, I was engaged in a conversation with a client to determine which data sets were subject to our next round of data quality profiling and assessment. After a successful first round where we identified serious data problems that were associated with the inability to successfully deploy a client analytic function, our goal was to expand the assessment process to look for more opportunities for system improvement. When we came to the subject of one particular data set and application, my client suggested that it was not useful to do any assessment on that data because that system was already recognized as being faulty and had reached the end of its useful life. The replacement system was already being designed.

After our conversation, my client's comments about the system that was to be retired kept floating through my head. Because there was already a recognition that the system was flawed, that there were problems with the data and that the system needed to be replaced, I wondered if it would be prudent to isolate the data problems with the old system to make sure they don't afflict the new system.

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