Seeding your Dataset

Seeding Data in Data Designer

Creating a Foundation for High-Quality Synthetic Data

Seeding is a critical concept in Data Designer that provides the foundation for generating diverse, realistic data. Seeds serve as the starting point from which additional data is generated, helping ensure your synthetic data has the right distribution, relationships, and characteristics.

Why Seeding Matters

Enhancing Data Diversity and Realism

Proper seeding is essential for several reasons:

• Diversity: Seeds introduce initial variation that gets amplified during generation

• Realism: Using real-world data patterns as seeds leads to more realistic outputs

• Consistency: Seeds provide a stable foundation for repeatable generation

• Domain Knowledge: Seeds encode domain expertise into your data generation process

Without good seeds, generated data might lack diversity, contain unrealistic patterns, or miss important edge cases. By thoughtfully seeding your Data Designer, you can dramatically improve the quality and usefulness of your synthetic data.

Methods of Seeding in Data Designer

There are two primary approaches to seeding data in Data Designer:

1. Using Your Own Dataset: Upload existing data to serve as a seed as shown here.

2. Creating Columns for Seeding: You can use any of the column types defined here to define columns that you want to use to seed your dataset.

Best Practices for Seeding

1. Use Domain-Appropriate Seeds: Select seed values that accurately reflect your domain and use case.

2. Balance Specificity and Diversity: Include enough seed values to capture important variations, but allow room for generation.

3. Create Meaningful Relationships: Use subcategories and expressions to establish realistic relationships between attributes.

4. Combine Approaches: Use both categorical seeds and seed datasets when appropriate for maximum control.

5. Test Your Seeds: Preview your results and iterate on your seed strategy to ensure you're getting the diversity and realism you need.