Mockers

Mockers are used to generate entirely new, random values for a column. They are ideal when privacy is the top priority, and there’s no need to maintain a link to the original data values.

When to use

• To fill columns that contain directly identifiable information (PII)

• To populate empty columns

• When format matters, but data relationships do not

When not to use

• When relationships with original data must be preserved

• For key columns (e.g., primary or foreign keys)

• When data correlations or dependencies matter

• When maintaining statistical properties is important

Available mockers

Mockers are grouped by data type:

• Text

• Numeric (integer and decimal)

• Datetime

• Other

Interactive guide: How to apply a mocker

Follow the interactive guide below to apply a mocker.

Limitations & considerations

• Constraints on Key Columns: Mock generators cannot be applied to primary key or foreign key columns.

• Column-by-Column Operation: Mock generatorsfunction on individual columns. At this point, they can't be used to preserve logical relationships across multiple columns.

• Automatically Cutoff Values: The Syntho platform automatically cuts off generated mock text values based on the data type's supported length. For example, a mock generator applied on an NVARCHAR(5) column, will cutoff all values beyond the first 5 characters of the text.

• Automatically Clip Values: The Syntho platform automatically clips numerical values that exceed the maximum or minimum size, to the largest or smallest value supported by the data type, respectively.

• No Link with Original Records: Mock generators do not link back to the original data records, enhancing privacy but potentially reducing the usefulness of the data. If you want to retain the link with the original values, you can enable the Consistent Mapping feature.

• Database Type Compatibility: The return type of a mock generator indicates its compatibility with specific database data types. For example, a mock generator with a text return type is compatible with database types like (N)VARCHAR or TEXT, but not with a database type NUMERIC (INTEGER). It is important to this into account when applying mock generators on your columns to prevent your data generation job to fail.

Being aware of these limitations and considerations will help you effectively use mock generators while understanding their constraints.