Augment

Augment requires a free account

AboutAugment

Generate synthetic data samples to expand small datasets.Category: AI & Machine Learning

Expanding a small labeled dataset before training a machine learning modelCreating realistic demo data for a product walkthrough without exposing real customer recordsPadding a test dataset so a data pipeline can be validated at production-like volume

The Augment tool is compatible with:.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.
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.

Frequently Asked Questions:

Why would I want synthetic data?

To expand a small dataset when you need more rows for testing, demos, or training a model without enough real records.

Does synthetic data replace real data in an analysis?

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.