Improving Data Quality and Managing Documentation


In this chapter, you looked at data quality issues and the analysis that can help identify and address them. We discussed suggestions for analysis that should be addressed up front in the Logical modeling process. Unfortunately, in our experience modeling software doesn’t store this information (although some gives you options of creating user-defined elements to deal with these issues). When creating mapping, documentation becomes more common; the tools will probably adapt to store this information and provide the capability to track what you’ve discovered for each entity, table, attribute, and column. We discussed documenting the following:
  • Data fidelity

  • Data criticality

  • Data sensitivity and privacy

  • Data stewardship

  • Cross-footing as a method of validating data

  • Data process validation

  • Risk identification and mitigation

You also looked at creating mappings, which are documents that help you manage the relationships between your models and other documentation, organizations, and requirements. Keeping this type of information in mapping documents is a stopgap measure that you’ll have to continue until metadata management becomes an integral part of your enterprise. In discussing mapping documents, you looked at many different examples, including the following:
  • Conceptual mappings

  • Logical mappings

  • Physical mappings

  • Pedigree traces

  • Data element roles


Data Element Customer Relationship Management Business Rule Improve Data Quality Marketing Department 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Sharon Allen and Evan Terry 2005

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