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Master Data Management in Practice: Achieving True Customer MDM by Mark Allen, Dalton Cervo

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Data Correction Initiatives

The ultimate goal for measuring the quality of the data is to identify what needs to be fixed. The data quality baseline will, in essence, lead to a series of data quality projects. Data correction could come down to two main activities:

1. Cleanup of existing bad data

2. Correction of offending system, process, or business practice causing the data problem

If the data quality baseline score is low for a given element, it will obviously require a data cleanup effort to improve the score. However, it's not always the source of the offending data that will be an ongoing issue. It is possible the bad data was caused by a one-time data migration effort, neglecting the need for correcting the migration code, for example. It is also possible an already corrected software bug or bad business process introduced the offending data, again negating the need for readdressing the origin of the problem. Nonetheless, a detailed root cause analysis must be completed. No assumptions should be made regarding the reason why bad data was introduced in the first place. As discussed earlier in this chapter, proactive data quality measures are essential to increase the maturity and effectiveness of a strong data management program.

Assuming the data problem is ongoing, it will normally fall into one of the two following categories:

1. A rule exists, but it is not being followed or implemented properly. A particular rule can either be enforced at the application level or ...

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