Generate Realistic Persons#

Data Designer provides comprehensive capabilities for generating realistic person data with optional synthetic personas. This feature creates synthetic individuals with complete demographic profiles, personality traits, and context-specific personas suitable for testing, research, and application development.

Person Objects in Data Designer#

Creating Person Samplers#

Person samplers generate realistic person entities with configurable attributes and optional synthetic persona data. Create them using the with_person_samplers method:

builder.with_person_samplers({
        "customer": {"sex": "Female", "locale": "en_US", "with_synthetic_personas": True},
        "employee": {"sex": "Female", "locale": "en_GB", "with_synthetic_personas": False},
        "random_person": {}  # Default settings
    },
    keep_person_columns=True # False by default
)

Each sampler creates a different person object that you can reference throughout your data design.

Configuration Options#

Person samplers accept these configuration parameters:

Basic Configuration:

sex: Specify “Male” or “Female” (optional)
locale: Language and region code (optional, e.g., “en_US”, “fr_FR”, “de_DE”)
city: City within the specified locale (optional)
age_range: Age range for filtering (default: ages above 18 only)
state: US state code, only valid when locale is set to “en_US” (optional)
keep_person_columns (default: False): When set to False, all person columns will be dropped from the final dataset.

Synthetic Personas Configuration:

with_synthetic_personas (default: False): When set to True, generates detailed personality profiles, cultural backgrounds, skills, interests, and context-specific personas for comprehensive character modeling.

Filtering Notes:

When using US locale (“en_US”), you can filter on age range, sex, city, and state
For non-US locales, filtering is limited to age range, sex, and city only
You can choose either city or state when filtering, not both

Locale Support and Data Quality#

US Locale (en_US): Data Designer uses Gretel’s proprietary Probabilistic Generative Model (PGM) trained on US census demographic data. This provides high-quality, realistic, and demographically accurate person data with preserved attribute relationships, resulting in coherent person profiles.

Other Locales: Data Designer uses the Faker library as fallback. While Faker provides basic attributes like names and addresses, it doesn’t maintain the same demographic accuracy or attribute relationships as the PGM. Data quality is notably lower than for US-based personas.

Synthetic Personas: Available for all locales when with_synthetic_personas=True. Persona generation adapts to cultural context based on the specified locale and demographic information.

Person Data Structure#

Core Demographic Fields (Always Available)#

Field Name	Type	Description
first_name	str	Person’s first name
middle_name	str	None
last_name	str	Person’s last name
sex	Sex	Person’s sex (enum type)
age	int	Person’s age
zipcode	str	Zipcode/Postal Code
street_number	int	str
street_name	str	Name of the street
unit	str	Unit/apartment number (US locale only)
city	str	City name
state	str	None
county	str	None
country	str	Country name
ethnic_background	str	None
marital_status	str	None
education_level	str	None
bachelors_field	str	None
occupation	str	None
uuid	str	None
locale	str	Locale setting
phone_number	str	None
email_address	str	None
birth_date	date	Calculated birth date based on age
ssn	str	None

Personality Traits (Available when `with_synthetic_personas=True`)#

Big Five personality model with t-scores and interpretive labels:

Field Name	Type	Description
openness	dict	Openness to experience (t_score, label, description)
conscientiousness	dict	Conscientiousness (t_score, label, description)
extraversion	dict	Extraversion (t_score, label, description)
agreeableness	dict	Agreeableness (t_score, label, description)
neuroticism	dict	Neuroticism (t_score, label, description)

Each personality trait contains:

t_score: Standardized score (typically 0-100)
label: Interpretive label (“low”, “average”, “high”, “very high”)
description: Detailed behavioral description

Synthetic Persona Fields (Available when `with_synthetic_personas=True`)#

Background and Development#

Field Name	Type	Description
cultural_background	str	Detailed narrative about cultural influences and upbringing
skills_and_expertise	str	Comprehensive description of professional and personal capabilities
skills_and_expertise_list	str	List format of key skills and competencies
hobbies_and_interests	str	Detailed description of personal interests and activities
hobbies_and_interests_list	str	List format of hobbies and interests
career_goals_and_ambitions	str	Professional aspirations and long-term objectives

Persona Profiles#

Field Name	Type	Description
persona	str	Brief summary personality profile
detailed_persona	str	Comprehensive personality and behavioral description
professional_persona	str	Work environment personality and career approach
finance_persona	str	Financial decision-making style and money management approach
healthcare_persona	str	Health and wellness attitudes and behaviors
sports_persona	str	Sports interests and physical activity preferences
arts_persona	str	Artistic tastes, cultural interests, and creative preferences
travel_persona	str	Travel style, preferences, and exploration approach
culinary_persona	str	Food interests, cooking style, and dining preferences

Usage Examples#

Basic Person Generation#

# Generate person with demographic and personality data only
builder.with_person_samplers({
    "basic_customer": {"locale": "en_US", "sex": "Female"}
})

Enhanced Person Generation with Synthetic Personas#

# Generate comprehensive person with full persona profiles
builder.with_person_samplers({
    "detailed_customer": {
        "locale": "en_US", 
        "sex": "Female",
        "with_synthetic_personas": True
    }
})

Using Person Data in Columns#

Extracting Basic Attributes#

builder.add_column(
    name="customer_name",
    type="expression",
    expr="{{customer.first_name}} {{customer.last_name}}"
)

builder.add_column(
    name="customer_contact",
    type="expression", 
    expr="{{customer.email_address}}"
)

Using Personality Traits#

builder.add_column(
    name="personality_summary",
    type="expression",
    expr="Extraversion: {{customer.extraversion.label}}, Openness: {{customer.openness.label}}"
)

Leveraging Synthetic Personas in Prompts#

builder.add_column(
    name="personalized_marketing_message",
    prompt="""
    Create a personalized marketing message for this customer:
    
    Name: {{customer.first_name}} {{customer.last_name}}
    Professional Background: {{customer.professional_persona}}
    Interests: {{customer.hobbies_and_interests_list}}
    Financial Style: {{customer.finance_persona}}
    
    Tailor the message to their personality and interests while promoting our financial planning services.
    """
)

builder.add_column(
    name="product_recommendations",
    prompt="""
    Based on this customer profile, recommend 3 relevant products:
    
    Customer: {{customer.first_name}} {{customer.last_name}}
    Age: {{customer.age}}
    Occupation: {{customer.occupation}}
    Personality: {{customer.detailed_persona}}
    Interests: {{customer.hobbies_and_interests}}
    
    Focus on products that align with their personality traits and lifestyle.
    """
)

Best Practices#

Choosing Configuration Options#

Use en_US locale for maximum demographic accuracy and realism
Enable with_synthetic_personas=True when you need rich character development, personalized content generation, or comprehensive behavioral modeling
Disable synthetic personas for basic demographic testing or when computational efficiency is prioritized

Effective Persona Usage#

Match persona depth to use case: Use basic personas for simple applications, detailed personas for comprehensive character modeling
Leverage context-specific personas: Use professional_persona for workplace scenarios, culinary_persona for food-related applications
Combine multiple persona fields in prompts for richer, more nuanced content generation

Performance Considerations#

Synthetic personas add processing time: Only enable when the additional data provides value
Cache person objects when using the same personas across multiple columns
Consider batch generation for large datasets requiring consistent persona quality

Quality Assurance#

Validate persona consistency: Ensure generated content aligns with personality traits and demographic information
Test across different locales to understand quality variations
Review persona coherence when using multiple context-specific personas for the same individual