A pattern describes the shape of a data value in an attribute. Patterns help you identify format deviations, misspellings, and duplications in your data. For example, If you have attributes that need to conform to a fixed format, such a date or currency format, you can examine patterns to find inconsistencies and errors.
There are four categories of patterns, each offering a different insight into the content and shape of your data:
- Character Patterns. The shape of a data value described by coded values. There are three types of character patterns: default, rich, and long.
- Masks. A description of a word, phrase, or number that identifies characters as alphabetic, numeric, or as a special character (a character that is not a number or a letter).
- Metaphones. The Discovery Center groups data values with similar patterns and identifies those values that share a phonetic pattern, or metaphone.
- Soundexes. A coded identification of data values that have been analyzed as sounding similar.
Note: Patterns are available for profiled data sources only.