Augment
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Supported formats: .csv,.xlsx,.xls,.xlsm,.xlsb,.tsv
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About Augment
Generate synthetic data samples to expand small datasets.
This tool learns the statistical distribution, mean, spread, and value ranges, of each column in your dataset and generates entirely new synthetic rows that follow those same patterns.
Unlike the Scenario Builder, which adjusts variables on your real rows, Augmentation manufactures brand-new records from scratch, useful when you simply don't have enough real data to work with.
The generated rows are statistically plausible but not real observations, so they're meant for padding out sample sizes, not for replacing genuine data in an analysis that requires ground truth.
Category: AI & Machine LearningCommon Use Cases
- Expanding a small labeled dataset before training a machine learning model
- Creating realistic demo data for a product walkthrough without exposing real customer records
- Padding a test dataset so a data pipeline can be validated at production-like volume
Key Features
- Distribution-Aware Row Generation
- Preserves Column Statistics (Mean, Range, Spread)
- Configurable Number of New Rows
- Keeps Original Data Separate from Generated Rows
Check Stats first to confirm your source distribution is representative, generate new rows here, then use Scenario Builder if you also want to test hypothetical adjustments on top of the expanded dataset.
Frequently Asked Questions
To expand a small dataset when you need more rows for testing, demos, or training a model without enough real records.
No, treat it as supplementary generated data useful for testing pipelines or padding out small samples, not as a substitute for real observations in serious analysis.
Aimed at data scientists and QA engineers who need more rows for training or testing without waiting on more real-world data to arrive.
Read the related guide →