> For the complete documentation index, see [llms.txt](https://docs.snomed.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snomed.org/snomed-ct-practical-guides/snomed-ct-mapping-guide/evaluating-mapping-as-a-solution/4.4-data-quality.md).
# Data quality
Mapping cannot mitigate data quality issues.
## Poor data quality
Poor-quality source data or insufficient information for the map development team to understand the source code and identify an appropriate target code will result in poor or low-quality maps, with many source codes remaining unmapped.
Also, "dirty" data, e.g., abbreviations, shorthand, use of symbols, or free-text sentences, can be expensive to process and map. "Dirty" data is difficult to process, and incorrect maps can be created if the author's true intent is unclear.
## Unknown, unfamiliar or undocumented source code sets
Often, legacy code sets have existed for some time, have been developed locally, and may not be well documented. The original intent of the source code set may be vague, not well understood, or not agreed upon. 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 the source or the target will result in a less robust, less safe map product.
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