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GITA 1998


Data Evolution
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Data Conversion - Many Souces, One Target

Nathan W. Pollock
IES Utilities
2001s’St. SE, Cedar Rapids, Iowa 52401, USA

Jeff Nash
MSE Corporation
941 N. Meridian Street, Indianapolis, Indiana 46204, USA


Introduction
We have all heard many times, “...communication is the key”. This remains true in a successfd data conversion project, even more so when it is complicated. It cannot be managed by memory and conversation alone. It requires a well-organized, documented data conversion plan.

The conversion specification covers all components of a data conversion project. The conversion matrix documents the relationship of existing data to its place in the target data model. When a project must combine many sources into one target system, documentation of these technical details becomes vitally important to keep everyone on schedule and synchronized.

The expectation remains to deliver quality data to the users quickly. Time does not permit the luxury of careful deliberation and reiteration in favor of quick, correct decisions and implementation. A process must be followed to stage each activity where it can be accomplished most efficiently. Remove all activities from the critical path that unnecessarily delay getting converted data to users.

This paper was based on the data conversion portion of the Vision IMPACT project at IES Utilities. This combines the positive experiences and those that could have been better with a focus on documentation and process. If we had to do it all over again...

Documentation
In the simplest terms, the conversion matrix is a database itself with three major components: the data sources, the target data model and the relationship between them. When it is well organized it serves as a list of issues to resolve during the conversion process. As questions arise over an issue, the matrix can be revisited to refresh or reevaluate. It serves as the conversion vendor’s guide in creating the translation program or conversion procedures. Missing, inconsistent or unidentifiable information is identified and will be handled during data scrub.


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