Semantic Type Recommendations
Recommendations to repair, enhance, or enrich the dataset, are determined by the type of data.
Examples of semantic type recommendations:
- Enrichments - Adding a new column to your data that corresponds to a specific detected type, such as a geographic location. For example, adding population data for a city.
- Column Concatenations - When two columns are detected in the dataset, one containing first names and the other containing last names, the system recommends concatenating the names into a single column. For example, a first_name_last_name column.
- Semantic Extractions - When a semantic type is composed of subtypes, for example a us_phone number that includes an area code, the system recommends extracting the subtype into its own column.
- Part Extraction - When a generic pattern separator is detected in the data, the system recommends extracting parts of that pattern. For example if the system detects a repeating hyphenation in the data, it recommends extracting the parts into separate columns to potentially make the data more useful for analysis.
- Date Extractions - When dates are detected, the system recommends extracting parts of the date that might augment the analysis of the data. For example, you might extract the day of week from an invoice or purchase date.
- Full and Partial Obfuscation/Masking/Delete - When sensitive fields are detected such as a credit card number, the system recommends a full or partial masking of the column, or even removal.
Last Published Friday, December 8, 2023