Using the Column details tab, you can optionally configure how MOSTLY AI will process your dataset’s columns during synthetization. Here you can include or exclude columns, tailor their processing to your use case, and replace the contents of certain types of columns with mock data.

Each column in your dataset has a generation method assigned. This method refers to how a column will be rendered to the synthetic dataset and indicates the available configuration options.

MOSTLY AI preconfigured these generation methods based on its analysis of the uploaded dataset. The table below sets out which generation methods there are, the table roles they appear in, and the configuration options that are available. To learn more about a configuration option, click on its name to jump to the corresponding section on this page.

All generation methods are fixed, except for AI-powered generation, which you can change to Mock data.
Generation method Behavior Roles Configuration options

AI-powered generation

Uses the column for AI-powered synthetic data generation.

  • • Subject

  • • Linked

Context foreign key

Links the entries in this table to their corresponding entries in the subject table.

  • • Linked

  • • No available options

Mock data

Generates random data within the constraints of the configured data type and format.

  • • Subject

  • • Linked

Primary key ID

Generates new primary key ID’s for the synthetic version of the table.

  • • Subject

  • • Sequential

  • • UUID

  • • UUID no hyphen

  • • Hash

Browsing your dataset’s columns

Navigating to a specific column in your dataset is very straightforward. The Column details tab is divided into two panes. The left pane lists your dataset’s tables, and the right pane lists the columns of these tables.

Each row in this list shows the column name and generation method. Here, you can also include or exclude the column from appearing in the synthetic dataset, and by clicking on the gear icon, you can open the Column parameters.

Configuring the encoding types of AI-powered generation columns

Ai-powered generation

To configure or change an encoding type, click on the gear icon of an AI-powered generation column to open its column parameters. Having these encoding types configured correctly is essential for accurately training the synthetic data generation model.

If you want to generate random data instead of synthetic data, you can switch the generation method to Mock data. To learn more about the available mock data types and configuration options, click here to jump to the corresponding section on this page.

Below, you’ll find an overview of available encoding types and how to configure them.


Use the Numeric encoding type to synthesize numerical values that may vary, such as weight and height.

The Precision field appears when you select this type. Here, you can optionally limit the maximum number of digits after the decimal point used during training. The values will be rounded to the number of digits that you specified.

Specifying the Precision option for the Numeric encoding type can save computational resources.

By default, MOSTLY AI considers all digits to be relevant for synthetic data generation. Reducing the precision speeds up the synthesization process when the original data suggests a high precision, but where those digits are not particularly relevant.

Fractional numbers, such as 0.333333333…​ are an example of such high precision numbers.


A categorical variable has a fixed set of possible values that are already present in the input data. An example of such a variable is T-shirt size, which consists of the following categories: 'XS, S, M, L, XL'. Categorical variables prevent random values (for instance, 'XM, A, B') from appearing in your synthetic dataset.

The Rare category protection settings appear below the Encoding type section when selecting this type. These settings help with protecting rare categories. Such categories may cause re-identification of outliers among your data subjects if they’re present in the resulting synthetic data.

There are two rare category protection methods available with which you can mask these categories:


Replaces the rare categories with a constant value. The default constant value is *.

Sample data

Replaces the rare categories with the categories that will appear in the synthetic version
of this column.

With the Threshold parameter, you can specify when a category is considered rare.
A Threshold of 20 means that if a category is only present at 20 or fewer subjects, this category will be masked using the method you specified

The two charts below demonstrate how this works using the Baseball dataset. This dataset contains the records of 19,000 professional baseball players from 57 different countries, describing their country of origin, name, weight, height, etc.

Baseball is a popular sport in the U.S.A and some other countries in that region of the world. It’s therefore rare to find professional baseball players in European or Asian countries. The below chart — depicting the original dataset — reflects this distribution of baseball players. Over 16,000 baseball players come from the U.S.A, whereas there are only a few players in Belgium, Austria, or the Philippines.

rare category protection 1

You can prevent these baseball players from being identified by their country of origin by masking these categories. The below chart — depicting the synthetic dataset — shows the result of replacing them with the * label. Of the 58 countries in the original dataset are only 16 visible in the synthetic dataset. The subjects of the remaining 43 countries are in the new * category, preventing the re-identification of that sole baseball player in Greece, Indonesia, or Singapore.

rare category protection 2

Another use case for the categorical encoding type is postal codes (or ZIP codes). Specifying them as categorical rather than as a numeric column makes a big difference in the synthetic data generation process.

Most countries use numeric postal code systems. Only a few in the world are alphanumeric. MOSTLY AI automatically detects these as categories. With numeric systems, it’s likely to assign the Numeric encoding type.

We highly recommend verifying the encoding type for your postal code column. If the synthesization process uses the Numeric encoding type, unique postal codes may appear that aren’t present in the original dataset.

Setting the threshold lower than 20 may introduce privacy risks.
Aside from privacy protection, setting this value reduces computational resources for high-cardinality columns.


Datetime refers to values that contain a date part and a time part. This encoding type enables MOSTLY AI to synthesize them and generate valid and statistically representative dates and times.

The following formats are supported:

Format Example




Datetime with hours

yyyy-MM-dd HH

2020-02-08 09

Datetime with minutes

yyyy-MM-dd HH:mm

2020-02-08 09:30

Datetime with seconds

yyyy-MM-dd HH:mm:ss

2020-02-08 09:30:26

Datetime with milliseconds

yyyy-MM-dd HH:mm:ss.SSS

2020-02-08 09:30:26.123

You will receive an error message during the encoding stage if your format doesn’t meet the criteria specified above. MOSTLY AI does not support the following formats:

  • Any format with a week number.
    Example: 2020-W06-5 (Week 6, Day 5 of 2020)

  • Any format with ordinal dates.
    Example: 2020-039 (Day 39 of 2020)

  • Formats with a time zone offset that don’t contain a Z
    Example: 2020-02-08 09+07:00

  • Short formats that do not contain any special characters, such as -, T, Z, etc.
    Example: 20200208T0930

  • Formats that separate seconds and milliseconds with a comma.
    Example: 2020-02-08T09:30:26,123

  • Formats that separate seconds and milliseconds with a colon.
    Example: 2020-02-08 09:30:26:123

  • Date only formats that have a time zone component.
    Example: 2020-02-08Z


Use the Text encoding type to synthesize unstructured natural language texts up to 1000 characters long and for a maximum of 1 column per table.

You can use this encoding type to generate realistic, representative, and anonymous financial transaction texts, short user feedback, medical assessments, PII fields, etc. As the resulting synthetic texts are representative of the terms, tokens, and their co-occurrence in the original data, they can be confidently used in analytics and machine learning use cases, such as sentiment analysis and named-entity recognition. Even though they might look noisy and not very human-readable, they will work perfectly for these use cases.

Our privacy and accuracy tests cannot detect potential leakages of protected rare categories or measure how representative the resulting synthetic texts are.

Our text synthetization model is language-agnostic and doesn’t contain the biases of some pre-trained models—any content is solely learned from the original training data. This means that it can process any language, vernacular, and slang present in the original data.

The amount of data required to produce usable results depends on the diversity of the original texts' vocabulary, categories, names, etc. As a rule of thumb, the more structure there is, the fewer samples are needed.

The synthetic texts are generated in a context-aware manner—the messages from a teenager are different from those of an 85-year old grandmother, for instance. By considering the other attributes of a synthetic subject’s profile, MOSTLY AI is capable of synthesizing appropriate natural language texts for each of them.

Below, you can find two examples. The first example demonstrates MOSTLY AI’s ability to synthesize entirely new names from a multilingual dataset. And the second example shows the result of synthesizing Tripadvisor reviews. Here you can see that the resulting texts accurately retain the context of the establishment they discuss (Restaurant or Hotel) and the synthesized rating.

Multilingual names dataset

Original Synthetic
    Nationality     Name
 1: Czech           Svoboda
 2: Greek           Chrysanthopoulos
 3: Spanish         Ventura
 4: Russian         Gagarin
 5: Japanese        Yokoyama
 6: English         Parsons
 7: Spanish         Ruiz
 8: Russian         Chekhov
 9: English         Blake
10: English         Wigley
    Nationality     Name
 1: English         Olsewood
 2: German          Kort
 3: Japanese        Misaghi
 4: English         Roger
 5: Russian         Lusov
 6: Russian         Zhuszenko
 7: Japanese        Noraghi
 8: English         Dalman
 9: Russian         Michov
10: Polish          Poskan
11: Arabic          Shaif

Tripadvisor reviews

   Establishment    Rating  Review
1: Restaurant       6       Not bad, great interior but let down by
                            unimaginative food. Perfectly good for a
                            quick lunch or drink though, good ales!
2: Hotel            2       Awful!!! stunk of smoke! guttering outside
                            window ledge filled with cigarette ends and
                            bottles. NOISY air con unit in room, husband
                            had no sleep.
3: Restaurant       8       Helpful staff, pleasant enough with quick
                            service. Sat at bar by revolving food server.
                            Everything seemed nice and fresh. Good value.
4: Hotel            8       We stayed in a standard room at the hotel.
                            The room was adequate, though a bit short on
                            cupboard/ drawer space.
5: Hotel            4       Expected much more from here and they just
                            didn't deliver, for the price of the room it
                            was no different than any of the other cheaper
   Establishment    Rating  Review
1: Restaurant       8       My only complaint are the portion sizes. Lovely
                            restaurant with good food, though.
2: Restaurant       8       I'm a year-round regular. Service is really
                            friendly. The starters are OK and the seafood
                            buffet is amazing and tasty. Overall a nice menu
                            throughout and our children love it.
3: Hotel            8       This is a fantastic hotel. Great food but few
                            options, a brilliant room and spent an excellent
                            time. Very clean environment and a high level of
4: Hotel            2       I booked an offer for a spa day. The food was
                            below-average, the room was dated, smelled of
                            fried fish, and the staff has an attitude.
5: Hotel            2       We've been here before. But for £70, the rooms
                            are still poor and glamourless. We spent a few
                            days with 5 people and 4 were not impressed.


ITT, or Inter-Transaction Time, is an encoding type that models the time interval between two subsequent events in the synthetic dataset. This encoding type causes the time between events to become very accurate, but the dates become less accurate.

You can select this encoding type for only one column with date and time information in your linked tables.
You will receive an error message during the encoding stage if you select `ITT`for a subject table column, multiple columns, or a column that doesn’t contain date or time information in the supported datetime formats..

Latitude, Longitude

Use the Latitude, Longitude encoding type to synthesize geolocation coordinates.

MOSTLY AI requires a geolocation coordinate to be encoded in a single field with the latitude and longitude as comma-separated values. The values must be in decimal degrees format and range from -90 to 90 for latitude and -180 to 180 for longitude. Their precision cannot be larger than five digits after the decimal dot. Any additional digits will be ignored.

The table below shows a use case with three geolocation columns.

Start location End location Some other location

"70.31311, 150.1"

"-90.0, 180.0"

"37.311, 173.8998"

"-39.0, 120.33114"

"78.31112, -100.031"

"-10.10, -80.901"

When formatting the geolocation coordinates, please keep in mind that they must be enclosed in double quotes. If you want to learn more about content rules, please visit the Preparing your data section.

Generating mock data instead of synthetic data

Instead of synthetic data, you can also choose to generate mock data — random data that is generated within the constraints of a configured data type and format. To do so, click on the gear icon of an AI-powered generation column, and change the generation method to Mock data.

MOSTLY AI can produce random numbers, names, addresses, and other personal details. If you want more precise control over the output, then you can select the Custom string option.

By creating a string pattern and providing a character range, you can generate random, but true-to-life phone numbers, transaction IDs, license plates, or any other type of information that is structured as a series of digits and letters.

Below, you’ll find an overview of available mock data types and formats.

Data type Format Description


Full name

Generates random full names from the English language.


'Norma Fisher'
'Jorge Sullivan'
'Elizabeth Woods'
'Susan Wagner'
'Peter Montgomery'

First name

Generates random first names from the English language.



Last name

Generates random last names from the English language.




No available format

Generates random, life-like IBAN bank account numbers. They conform to the official format, are of the expected length, and have a valid checksum.

Generated IBANs that turn out to be valid in real life are purely coincidental.




No available format

Generates random emails.

Each email address contains the domain name of a free email service provider.



Full name

Generates random random address details.

Each full address is formatted as a string containing a street name and house number, place of residence, and postal code.


'48764 Howard Forge Apt. 421\nVanessaside, PA 19763'
'578 Michael Island\nNew Thomas, NC 34644'
'60975 Jessica Squares\nEast Sallybury, FL 71671'
'8714 Mann Plaza\nLisaside, PA 72227'
'96593 White View Apt. 094\nJonesberg, FL 05565'


Generates random cities.


'West Tammyfort'
'West Donald'


Generates random countries.




No available format

Generates random numbers.

Set the range by specifying the minimum and maximum values. To specify the precision, enter the number of digits after the decimal dot in the precision field.

Custom string

String pattern

Generates random strings based on a string pattern.

Format your string using the # and ? symbols as placeholders for random values:

  • Number signs (#) are replaced with a random digit (0 to 9).

  • Question marks (?) are randomly drawn from the characters you entered in the Character range field.

Usage Examples:

'Telephone: +1 (###) ###-####'
'Company ID: Company ????????'
'Transaction ID: ???-####-?######'


No available format

Writes a constant value.
Enter the value to be written in the input field.

Row number

No available format

Writes the row numner.

Setting the format of a Primary key ID

Ai-powered generation

Primary keys are unique identifiers for each entry in a table and can come in different formats. MOSTLY AI can generate primary keys in sequential, UUID version 1—either with or without hyphens, or a proprietary hash format. Please select the format that’s present in the original data.


UUID: 7b20cc44-da3b-11eb-810e-acde48001122
UUID-no-hyphen: 7b20cc44da3b11eb810eacde48001122