# Data quality

Mapping cannot solve issues with data quality.

## Poor data quality

Poor data quality in source codes or insufficient information for the map development team to understand the source code and find an appropriate target code, will result in poor or patchy data quality with many source codes being unable to be mapped.

Also, "dirty" data e.g. abbreviations, shorthand, use of symbols, free text sentences, can be expensive to process and map. "Dirty" data is difficult to process and incorrect maps can be created if the true intent of the author is unclear.

## Unknown, unfamiliar or undocumented source code sets

Often legacy code sets have been in existence for some time, and have themselves been locally developed, and may not be well documented. The original intent of the source code set may be vague or not not well understood or agreed. There may only be informal documentation or the documentation may have aged. Understanding both the source and target vocabularies is essential; guessing at the intended meaning of either of source or target will result in a less robust and safe map product.

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